Singha M, Pu L, Stanfield BA, Uche IK, Rider PJF, Kousoulas KG, Ramanujam J, Brylinski M. (2022) Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors. BMC Cancer22: 1211.
Shi W, Singha M, Pu L, Ramanujam J, Brylinski M. (2022) GraphSite: Ligand-binding site classification with deep graph learning. Biomolecules12: 1053.
Pu L, Singha M, Ramanujam J, Brylinski M. (2022) CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling. Oncotarget13: 695-706.
Pu L, Singha M, Wu HC, Busch C, Ramanujam J, Brylinski M. (2022) An integrated network representation of multiple cancer-specific data for graph-based machine learning. NPJ Syst Biol Appl8: 14.
Shi W, Singha M, Srivastava G, Pu L, Ramanujam J, Brylinski M. (2022) Pocket2Drug: An encoder-decoder deep neural network for the target-based drug design. Front Pharmacol13: 837715.
Osman N, Shawky M, Brylinski M. (2022) Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure. BMC Genom Data23 (1): 13.
Bess A, Berglind F, Mukhopadhyay S, Brylinski M, Griggs N, Cho T, Galliano C, Wasan KM. (2022) Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases. Drug Discov Today27 (4): 1099-1107.
2021
Liu G, Singha M, Pu L, Neupane P, Feinstein J, Wu HC, Ramanujam J, Brylinski M. (2021) GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data. J Cheminform13 (1): 58.
Feinstein J, Shi W, Ramanujam J, Brylinski M. (2021) Bionoi: A Voronoi diagram-based representation of ligand-binding sites in proteins for machine learning applications. Methods Mol Biol2266: 299-312.
2020
Shi W, Lemoine JM, Shawky MA, Singha M, Pu L, Yang S, Ramanujam J, Brylinski M. (2020) BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network. Bioinformatics36 (10): 3077-3083.
2019
Thaljeh LF, Rothschild JA, Naderi M, Coghill LM, Brown JM, Brylinski M. (2019) Hinge region in DNA packaging terminase pUL15 of herpes simplex virus: A potential allosteric target for antiviral drugs. Biomolecules9 (10): 603.
Rider P, Coghill L, Naderi M, Brown JM, Brylinski M, Kousoulas KG. (2019) Identification and visualization of functionally important domains and residues in herpes simplex virus glycoprotein K (gK) using a combination of phylogenetics and protein modeling. Sci Rep9 (1): 14625.
Kana OZ, Brylinski M. (2019) Elucidating the druggability of the human proteome with eFindSite. J Comput Aided Mol Des33 (5): 509-519.
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.
Pu L, Govindaraj RG, Wu HC, Brylinski M. (2019) DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol15 (2): e1006718.
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.
Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. (2019) Binding site matching in rational drug design: Algorithms and applications. Brief Bioinform20 (6): 2167-2184.
Gadzala M, Dulak D, Kalinowska B, Baster Z, Brylinski M, Konieczny L, Banach M, Roterman I. (2019) The aqueous environment as an active participant in the protein folding process. J Mol Graph Model87: 227-239.
2018
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.
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.
Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics19 (1): 91.
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.
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.
2017
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.
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.
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.
Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol1611: 109-22.
2016
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.
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.
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.
Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods93: 64-71.
Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets17 (14): 1595-609.
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.
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.
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.
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.
2015
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.
Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform16 (6): 1025-34.
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.
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.
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.
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.
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.
Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls2: 55-72.
2014
Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol10 (9): e1003829.
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.
Feinstein WP, Brylinski M. (2014) eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inf33 (2): 135-50.
Brylinski M, Waldrop GL. (2014) Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries. Molecules19 (4): 4021-45.
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.
2013
Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model53 (11): 3097-112.
Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol328: 77-88.
Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes6 (1): 303.
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.
Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci11 (1): 47.
Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet4: 118.
2012
Skolnick J, Zhou H, Brylinski M. (2012) Further evidence for the likely completeness of the library of solved single domain protein structures. J Phys Chem B116 (23): 6654-64.
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.
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.
2011
Brylinski M, Gao M, Skolnick J. (2011) Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function. Phys Chem Chem Phys13 (38): 17044-55.
Brylinski M, Skolnick J. (2011) FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level. Proteins79 (3): 735-51.
Brylinski M, Lee SY, Zhou H, Skolnick J. (2011) The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement. J Struct Biol173 (3): 558-69.
2010
Brylinski M, Skolnick J. (2010) Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening. J Chem Inf Model50 (10): 1839-54.
Pandit SB, Brylinski M, Zhou H, Gao M, Arakaki AK, Skolnick J. (2010) PSiFR: an integrated resource for prediction of protein structure and function. Bioinformatics26 (5): 687-8.
Brylinski M, Skolnick J. (2010) Comparison of structure-based and threading-based approaches to protein functional annotation. Proteins78 (1): 118-34.
Brylinski M, Skolnick J. (2010) Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach. Mol Pharm7 (6): 2324-33.
Brylinski M, Skolnick J. (2010) Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling. J Comput Chem31 (5): 1093-105.
2009
Skolnick J, Arakaki AK, Lee SY, Brylinski M. (2009) The continuity of protein structure space is an intrinsic property of proteins. Proc Natl Acad Sci USA106 (37): 15690-5.
Brylinski M, Skolnick J. (2009) FINDSITE: a threading-based approach to ligand homology modeling. PLoS Comput Biol5 (6): e1000405.
Skolnick J, Brylinski M. (2009) FINDSITE: a combined evolution/structure-based approach to protein function prediction. Brief Bioinform10 (4): 378-91.
Brylinski M, Skolnick J. (2009) Novel computational approaches to drug discovery. Proc Int Conf Quant Bio InformIII: 327-36.
Skolnick J, Lee SY, Brylinski M. (2009) Reply to Zimmerman et al: The space of single domain protein structures is continuous and highly connected. Proc Natl Acad Sci USA106 (51): E138.
Roterman I, Brylinski M, Konieczny L. (2009) Active site recognition in silico. Structure-function relation in proteins2: 105-27.
Roterman I, Konieczny L, Brylinski M. (2009) Folding process in the presence of specific ligand. Structure-function relation in proteins2: 129-48.
Roterman I, Konieczny L, Brylinski M. (2009) Late-stage folding intermediate in silico model. Structure-function relation in proteins2: 79-103.
2008
Brylinski M, Konieczny L, Kononowicz A, Roterman I. (2008) Conservative secondary structure motifs already present in early-stage folding (in silico) as found in serpines family. J Theor Biol251 (2): 275-85.
Brylinski M, Skolnick J. (2008) Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints. J Comput Chem29 (10): 1574-88.
Brylinski M, Skolnick J. (2008) A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci USA105 (1): 129-34.
Brylinski M, Skolnick J. (2008) What is the relationship between the global structures of apo and holo proteins? Proteins70 (2): 363-77.
2007
Brylinski M, Prymula K, Jurkowski W, Kochanczyk M, Stawowczyk E, Konieczny L, Roterman I. (2007) Prediction of functional sites based on the fuzzy oil drop model. PLoS Comput Biol3 (5): e94.
Brylinski M, Kochanczyk M, Broniatowska E, Roterman I. (2007) Localization of ligand binding site in proteins identified in silico. J Mol Model13 (6-7): 665-75.
Brylinski M, Konieczny L, Roterman I. (2007) Early-stage protein folding - In silico model. Rec Adv Struct Bioinform1: 69-104.
Brylinski M, Konieczny L, Roterman I. (2007) Is the protein folding an aim-oriented process? Human haemoglobin as example. Int J Bioinform Res Appl3 (2): 234-60.
2006
Brylinski M, Konieczny L, Roterman I. (2006) Hydrophobic collapse in late-stage folding (in silico) of bovine pancreatic trypsin inhibitor. Biochimie88 (9): 1229-39.
Meus J, Brylinski M, Piwowar M, Piwowar P, Wisniowski Z, Stefaniak J, Konieczny L, Surowka G, Roterman I. (2006) A tabular approach to the sequence-to-structure relation in proteins (tetrapeptide representation) for de novo protein design. Med Sci Monit12 (6): BR208-14.
Dabrowska J, Brylinski M. (2006) Stereoselectivity of 8-OH-DPAT toward the serotonin 5-HT1A receptor: biochemical and molecular modeling study. Biochem Pharmacol72 (4): 498-511.
Brylinski M, Konieczny L, Roterman I. (2006) Hydrophobic collapse in (in silico) protein folding. Comput Biol Chem30 (4): 255-67.
Brylinski M, Konieczny L, Roterman I. (2006) Fuzzy-oil-drop hydrophobic force field - a model to represent late-stage folding (in silico) of lysozyme. J Biomol Struct Dyn23 (5): 519-28.
Konieczny L, Brylinski M, Roterman I. (2006) Gauss-function-based model of hydrophobicity density in proteins. In Silico Biol6 (1-2): 15-22.
Brylinski M, Konieczny L, Roterman I. (2006) Ligation site in proteins recognized in silico. Bioinformation1 (4): 127-9.
Brylinski M, Kochanczyk M, Konieczny L, Roterman I. (2006) Sequence-structure-function relation characterized in silico. In Silico Biol6 (6): 589-600.
2005
Brylinski M, Konieczny L, Czerwonko P, Jurkowski W, Roterman I. (2005) Early-stage folding in proteins (in silico) sequence-to-structure relation. J Biomed Biotechnol2005 (2): 65-79.
Brylinski M, Konieczny L, Roterman I. (2005) SPI - structure predictability index for protein sequences. In Silico Bio5 (3): 227-37.
2004
Jurkowski W, Brylinski M, Konieczny L, Roterman I. (2004) Lysozyme folded in silico according to the limited conformational sub-space. J Biomol Struct Dyn22 (2): 149-58.
Brylinski M, Jurkowski W, Konieczny L, Roterman I. (2004) Limited conformational space for early-stage protein folding simulation. Bioinformatics20 (2): 199-205.
Jurkowski W, Brylinski M, Konieczny L, Wisniowski Z, Roterman I. (2004) Conformational subspace in simulation of early-stage protein folding. Proteins55 (1): 115-127.
Jurkowski W, Brylinski M, Konieczny L, Roterman I. (2004) Limitation of conformational space for proteins - early stage folding simulation of human α and β hemoglobin chains. TASK Quarterly8 (3): 413-22.
