Mixed-resolution drug docking

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     

Prediction of toxicity and synthesizability

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     

Analysis of aromatic interactions

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     

Drug repositioning for orphan diseases

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     

Similarity measures for ligand-binding conformations

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     

Large-scale modeling of protein-drug interactions

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 Biol 6 (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 ONE 7 (11): e50200.     
3 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 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 Syst 11 (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 Biol 15 (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 Recognit 28 (1): 35-48.     
3 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
4 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 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 Bioinformatics 18 (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. Bioinformatics 32 (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. GigaScience 7: 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 Cheminform 8: 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 Model 57 (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 Cheminform 7 (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 Virol 90 (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 Virol 90 (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 Virol 91 (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 Chem 36 (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 ONE 11 (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 Chem 64: 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 Appl 4: 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 Biol 430 (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 Des 91 (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 Toxicol 20 (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 Notes 6 (1): 303.     
2 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 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 Biol 6 (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 Int 2014: 348725.     
3 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 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 Nanobioscience 14 (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 ONE 11 (7): e0158898.     
6 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (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 Genet 4: 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 Des 27 (6): 551-67.     
3 Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf 33 (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 Sci 11 (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 Model 53 (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. Molecules 19 (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 Biol 10 (9): e1003829.     
2 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
3 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (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 USA 111 (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. Biochemistry 55 (23): 3204-13.     
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