Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
1
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Brylinski M, Lingam D. (2012) eThread: A highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS ONE7 (11): e50200.
3
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
4
Brylinski M. (2015) Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling? Bio Algorithms Med Syst11 (1): 1-7.
For more information see:
1
Maheshwari S, Brylinski M. (2015) Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures. BMC Struct Biol15 (1): 23.
2
Maheshwari S, Brylinski M. (2015) Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit28 (1): 35-48.
3
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
4
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
5
Maheshwari S, Brylinski M. (2017) Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics18 (1): 257.
For more information see:
1
Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L. (2016) PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics32 (4): 579-86.
2
Naderi M, Govindaraj RG, Brylinski M. (2018) eModel-BDB: A database of comparative structure models of drug-target interactions from the Binding Database. GigaScience7: 1-9.
3
Wang C, Brylinski M, Kurgan L. (2019) PDID: database of experimental and putative drug targets in human proteome. In silico drug design: Repurposing techniques and methodologies 827-847.
For more information see:
1
Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. (2016) A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform8: 14.
2
Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. (2017) Break down in order to build up: Decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model57 (4): 627-631.
For more information see:
1
Feinstein WP, Brylinski M. (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform7 (1): 18.
For more information see:
1
Jambunathan N, Charles AS, Subramanian R, Saied AA, Naderi M, Rider P, Brylinski M, Chouljenko VN, Kousoulas KG. (2016) Deletion of a predicted β-sheet domain within the amino terminus of herpes simplex virus glycoprotein K conserved among alphaherpesviruses prevents virus entry into neuronal axons. J Virol90 (5): 2230-9.
2
Chouljenko DV, Jambunathan N, Chouljenko VN, Naderi M, Brylinski M, Caskey JR, Kousoulas KG. (2016) Herpes simplex virus type 1 UL37 protein tyrosine residues conserved among all alphaherpesviruses are required for interactions with glycoprotein K (gK), cytoplasmic virion envelopment, and infectious virus production. J Virol90 (22): 10351-61.
3
Rider P, Naderi M, Bergeron S, Chouljenko VN, Brylinski M, Kousoulas KG. (2017) Cysteines and N-glycosylation sites conserved among all alphaherpesviruses regulate membrane fusion in herpes simplex virus 1 infection. J Virol91 (21): e00873-17.
For more information see:
1
Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. (2015) GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem36 (27): 2013-26.
2
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
For more information see:
1
Ding Y, Fang Y, Moreno J, Ramanujam J, Jarrell M, Brylinski M. (2016) Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem64: 403-13.
For more information see:
1
Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl4: 13.
2
Brylinski M, Naderi M, Govindaraj RG, Lemoine J. (2018) eRepo-ORP: Exploring the opportunity space to combat orphan diseases with existing drugs. J Mol Biol430 (15): 2266-2273.
For more information see:
1
Brylinski M. (2018) Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des91 (2): 380-90.
For more information see:
1
Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. (2019) eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol20 (1): 2.
For more information see:
1
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
2
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
For more information see:
1
Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol6 (1): 001-010.
2
Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int2014: 348725.
3
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
4
Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience14 (4): 429-39.
5
Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE11 (7): e0158898.
6
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
For more information see:
1
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2
Brylinski M, Feinstein WP. (2013) eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des27 (6): 551-67.
3
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
For more information see:
1
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
For more information see:
1
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
For more information see:
1
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
For more information see:
1
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
2
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
3
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
For more information see:
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Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Limbach PA, Bricker TM. (2014) Use of protein cross-linking and radiolytic footprinting to elucidate PsbP and PsbQ interactions within higher plant Photosystem II. Proc Natl Acad Sci USA111 (45): 16178-83.
2
Mummadisetti MP, Frankel LK, Bellamy HD, Sallans L, Goettert JS, Brylinski M, Bricker TM. (2016) Use of protein cross-linking and radiolytic labeling to elucidate the structure of PsbO within higher-plant photosystem II. Biochemistry55 (23): 3204-13.
