michal

Michal Brylinski

Position
Associate professor
Degree
PhD in Chemistry, PharmD
CV
  

 

Contact

Department of Biological Sciences
407 Choppin Hall
Louisiana State University
Baton Rouge, LA 70803
Email 1: mbrylinski@lsu.edu
Email 2: michal@brylinski.org
Tel: (225) 578-2791
Fax: (225) 578-2597

 

Research interests

I am interested in incorporating multi-dimensional biological data into computer-aided drug discovery and design. I study drug-oriented interactomes by combining high-level “omics” data, such as genomics, transcriptomics, proteomics, metabolomics, glycomics, and genome-wide association studies with molecular and atomic-level modeling of medically relevant biomolecules and interactions among them. I also apply deep learning technologies to integrate, analyze, and classify large-scale experimental and computer-generated biological data. My goals are to support the development of novel and safe pharmaceuticals with enhanced selectivity profiles, to expand the current opportunity space for polypharmacology and rational drug repositioning, and to contribute to the field of precision medicine.

 

Publications
96 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 Cancer 22: 1211.     
95 Shi W, Singha M, Pu L, Ramanujam J, Brylinski M. (2022) GraphSite: Ligand-binding site classification with deep graph learning. Biomolecules 12: 1053.     
94 Pu L, Singha M, Ramanujam J, Brylinski M. (2022) CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling. Oncotarget 13: 695-706.     
93 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 Appl 8: 14.     
92 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 Pharmacol 13: 837715.     
91 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 Data 23 (1): 13.     
90 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 Today 27 (4): 1099-1107.     
89 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 Cheminform 13 (1): 58.     
88 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 Biol 2266: 299-312.   
87 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. Bioinformatics 36 (10): 3077-3083.     
86 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. Biomolecules 9 (10): 603.     
85 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 Rep 9 (1): 14625.     
84 Kana OZ, Brylinski M. (2019) Elucidating the druggability of the human proteome with eFindSite. J Comput Aided Mol Des 33 (5): 509-519.     
83 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.     
82 Pu L, Govindaraj RG, Wu HC, Brylinski M. (2019) DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol 15 (2): e1006718.     
81 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.   
80 Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. (2019) Binding site matching in rational drug design: Algorithms and applications. Brief Bioinform 20 (6): 2167-2184.     
79 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 Model 87: 227-239.     
78 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.     
77 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.     
76 Govindaraj RG, Brylinski M. (2018) Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 19 (1): 91.     
75 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.     
74 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.     
73 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.     
72 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.     
71 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.     
70 Brylinski M. (2017) Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning. Methods Mol Biol 1611: 109-22.     
69 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.     
68 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.     
67 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.     
66 Maheshwari S, Brylinski M. (2016) Template-based identification of protein-protein interfaces using eFindSite(PPI). Methods 93: 64-71.     
65 Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (14): 1595-609.     
64 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.     
63 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.     
62 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.     
61 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.     
60 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.     
59 Maheshwari S, Brylinski M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 16 (6): 1025-34.     
58 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.     
57 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.     
56 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.    
55 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.     
54 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.     
53 Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 55-72.    
52 Brylinski M. (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol 10 (9): e1003829.     
51 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.     
50 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.     
49 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.     
48 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.     
47 Brylinski M. (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model 53 (11): 3097-112.     
46 Brylinski M. (2013) The utility of artificially evolved sequences in protein threading and fold recognition. J Theor Biol 328: 77-88.     
45 Brylinski M. (2013) eVolver: an optimization engine for evolving protein sequences to stabilize the respective structures. BMC Res Notes 6 (1): 303.     
44 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.     
43 Brylinski M. (2013) Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci 11 (1): 47.     
42 Brylinski M. (2013) Unleashing the power of meta-threading for evolution/structure-based function inference of proteins. Front Genet 4: 118.     
41 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 B 116 (23): 6654-64.     
40 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.     
39 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.    
38 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 Phys 13 (38): 17044-55.     
37 Brylinski M, Skolnick J. (2011) FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level. Proteins 79 (3): 735-51.     
36 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 Biol 173 (3): 558-69.     
35 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 Model 50 (10): 1839-54.     
34 Pandit SB, Brylinski M, Zhou H, Gao M, Arakaki AK, Skolnick J. (2010) PSiFR: an integrated resource for prediction of protein structure and function. Bioinformatics 26 (5): 687-8.     
33 Brylinski M, Skolnick J. (2010) Comparison of structure-based and threading-based approaches to protein functional annotation. Proteins 78 (1): 118-34.     
32 Brylinski M, Skolnick J. (2010) Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach. Mol Pharm 7 (6): 2324-33.     
31 Brylinski M, Skolnick J. (2010) Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling. J Comput Chem 31 (5): 1093-105.     
30 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 USA 106 (37): 15690-5.     
29 Brylinski M, Skolnick J. (2009) FINDSITE: a threading-based approach to ligand homology modeling. PLoS Comput Biol 5 (6): e1000405.     
28 Skolnick J, Brylinski M. (2009) FINDSITE: a combined evolution/structure-based approach to protein function prediction. Brief Bioinform 10 (4): 378-91.     
27 Brylinski M, Skolnick J. (2009) Novel computational approaches to drug discovery. Proc Int Conf Quant Bio Inform III: 327-36.   
26 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 USA 106 (51): E138.    
25 Roterman I, Brylinski M, Konieczny L. (2009) Active site recognition in silico. Structure-function relation in proteins 2: 105-27.   
24 Roterman I, Konieczny L, Brylinski M. (2009) Folding process in the presence of specific ligand. Structure-function relation in proteins 2: 129-48.   
23 Roterman I, Konieczny L, Brylinski M. (2009) Late-stage folding intermediate in silico model. Structure-function relation in proteins 2: 79-103.   
22 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 Biol 251 (2): 275-85.     
21 Brylinski M, Skolnick J. (2008) Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints. J Comput Chem 29 (10): 1574-88.     
20 Brylinski M, Skolnick J. (2008) A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci USA 105 (1): 129-34.     
19 Brylinski M, Skolnick J. (2008) What is the relationship between the global structures of apo and holo proteins? Proteins 70 (2): 363-77.     
18 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 Biol 3 (5): e94.     
17 Brylinski M, Kochanczyk M, Broniatowska E, Roterman I. (2007) Localization of ligand binding site in proteins identified in silico. J Mol Model 13 (6-7): 665-75.     
16 Brylinski M, Konieczny L, Roterman I. (2007) Early-stage protein folding - In silico model. Rec Adv Struct Bioinform 1: 69-104.   
15 Brylinski M, Konieczny L, Roterman I. (2007) Is the protein folding an aim-oriented process? Human haemoglobin as example. Int J Bioinform Res Appl 3 (2): 234-60.     
14 Brylinski M, Konieczny L, Roterman I. (2006) Hydrophobic collapse in late-stage folding (in silico) of bovine pancreatic trypsin inhibitor. Biochimie 88 (9): 1229-39.     
13 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 Monit 12 (6): BR208-14.    
12 Dabrowska J, Brylinski M. (2006) Stereoselectivity of 8-OH-DPAT toward the serotonin 5-HT1A receptor: biochemical and molecular modeling study. Biochem Pharmacol 72 (4): 498-511.     
11 Brylinski M, Konieczny L, Roterman I. (2006) Hydrophobic collapse in (in silico) protein folding. Comput Biol Chem 30 (4): 255-67.     
10 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 Dyn 23 (5): 519-28.     
9 Konieczny L, Brylinski M, Roterman I. (2006) Gauss-function-based model of hydrophobicity density in proteins. In Silico Biol 6 (1-2): 15-22.    
8 Brylinski M, Konieczny L, Roterman I. (2006) Ligation site in proteins recognized in silico. Bioinformation 1 (4): 127-9.    
7 Brylinski M, Kochanczyk M, Konieczny L, Roterman I. (2006) Sequence-structure-function relation characterized in silico. In Silico Biol 6 (6): 589-600.    
6 Brylinski M, Konieczny L, Czerwonko P, Jurkowski W, Roterman I. (2005) Early-stage folding in proteins (in silico) sequence-to-structure relation. J Biomed Biotechnol 2005 (2): 65-79.     
5 Brylinski M, Konieczny L, Roterman I. (2005) SPI - structure predictability index for protein sequences. In Silico Bio 5 (3): 227-37.    
4 Jurkowski W, Brylinski M, Konieczny L, Roterman I. (2004) Lysozyme folded in silico according to the limited conformational sub-space. J Biomol Struct Dyn 22 (2): 149-58.     
3 Brylinski M, Jurkowski W, Konieczny L, Roterman I. (2004) Limited conformational space for early-stage protein folding simulation. Bioinformatics 20 (2): 199-205.     
2 Jurkowski W, Brylinski M, Konieczny L, Wisniowski Z, Roterman I. (2004) Conformational subspace in simulation of early-stage protein folding. Proteins 55 (1): 115-127.     
1 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 Quarterly 8 (3): 413-22.