Area of Interest

The major focus of our group is the design and development of novel tools for the modeling and analysis of biological networks using Computational Systems Biology. Briefly, Computational Systems Biology can be considered as a complex platform that integrates many algorithms from different research areas such as Structural Bioinformatics, Functional Genomics, Cheminformatics and Pharmacogenomics. We are interested in applying various Computational Systems Biology tools to study the evolution and organization of pathways into biological networks with the primary application in modern drug discovery and design.

Selected Publications

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. 173:558-69. [abstract]

Brylinski, M, Skolnick J. 2008. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci USA. 105:129-34. [full text]

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. 79:735-51. [abstract]

Brylinski, M, Skolnick J. 2009. FINDSITE: a threading-based approach to ligand homology modeling. PLoS Comput Biol. 5:e1000405. [full text]

Brylinski, M, Skolnick J. 2010. Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling. J Comput Chem. 31:1093-105. [abstract]

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. 50:1839-54. [abstract]

Brylinski, M, Skolnick J. 2010. Cross-reactivity virtual profiling of the human kinome by X-React(KIN): a Chemical Systems Biology approach. Mol Pharm. 7:2324-33. [abstract]