Ilya Vakser

Ilya Vakser
  • Professor
  • Director of the KU Center for Computational Biology

Contact Info

785-864-1057
200A MRB

Personal Links

Biography —

Dr. Ilya Vakser is an expert in modeling of protein interactions and structural description of protein complexes. He is one of the authors of the FFT docking algorithm, which is a standard tool in protein docking field, and the initiator of the community-wide CAPRI protein docking competition.

Dr. Vakser earned his PhD in biophysics at Moscow State University in 1989 and received further training at the Weizmann Institute, Washington University, and the Rockefeller University. He served on the faculty of the Medical University of South Carolina as an Assistant and Associate Professor and SUNY Stony Brook as an Associate Professor. Since 2005 Dr. Vakser is the founding Director and a Full Professor at the Computational Biology (Bioinformatics) Program and Center for Computational Biology at The University of Kansas.

Research —

Molecular modeling in the context of structural genomics and bioinformatics.

The research in our laboratory focuses on molecular modeling in the context of structural genomics and bioinformatics. The major goals are to develop approaches to the modeling of protein interactions and to design procedures for reconstruction of the network of connections between proteins in a genome. The number of protein-protein interactions in a genome is significantly larger than the number of individual proteins. Moreover, most protein structures will be models of limited accuracy. Thus the structure-based methods for building this network have to be (a) fast, and (b) insensitive to significant inaccuracies of modeled structures. The precision of these methods may be correlated with the precision of the protein structures—lower for less accurate models and higher for more exact models.

Our long-term goals are to understand the fundamental principles of protein interaction and to create a structure-based description of genomes. The primary current objectives are: development of methodology for an accurate prediction of the structure of protein complexes, docking in genome-wide databases of modeled protein structures, and development of the integrated environment for docking studies.

Teaching —

  • BINF 701 - Computational Biology Core I
  • BINF 702 - Computational Biology Core II
  • BIOL 952 - Introduction to Molecular Modeling
  • BINF 709 - Topics in Bioinformatics

Selected Publications —

See all papers by Ilya Vakser on PubMed 

  • Collins, K.W., Copeland, M.M., Brysbaert, G., Wodak, S.J., Bonvin, A.M.J.J., Kundrotas, P.J., Vakser, I.A.*, Lensink, M.F., 2024, CAPRI-Q: The CAPRI resource for assessment of modeling protein interactions, J. Mol. Biol., 436:168540.

  • Singh, A., Kundrotas, P.J., Vakser, I.A., 2024, Diffusion of proteins in crowded solutions studied by docking-based modeling, J. Chem. Phys., 161: 095101.

  • Singh, A., Copeland, M.M., Kundrotas, P.J., Vakser, I.A., 2024, GRAMM webserver for protein docking, Methods Mol. Biol., 2714:101-112.

  • Vakser, I.A., Grudinin, S., Jenkins, N.W., Kundrotas, P.J., Deeds, E.J., 2022, Docking-based long timescale simulation of cell-size protein systems at atomic resolution. Proc. Natl. Acad. Sci. USA, 119: e2210249119. PMID: 36191203

  • Collins, K.W., Copeland, M.M., Kotthoff, I., Singh, A., Kundrotas, P.J., Vakser, I.A., 2022, DOCKGROUND resource for protein recognition studies, Protein Sci., 31: e4481. PMID: 36281025

  • Jenkins, N.W., Kundrotas, P.J., Vakser, I.A., 2022, Size of the protein-protein energy funnel in crowded environment, Front. Mol. Biosci., 9: 1031225. PMID: 36425657

  • Malladi, S., Powell, H.R., David, A., Islam, S.A., Copeland, M.M., Kundrotas, P.J., Sternberg, M.J.E., Vakser, I.A., 2022, GWYRE: A resource for mapping variants onto experimental and modeled structures of human protein complexes, J. Mol. Biol., 434: 167608. PMID: 35662458

  • Kotthoff, I., Kundrotas, P.J., Vakser, I.A., 2022, DOCKGROUND scoring benchmarks for protein docking, Proteins, 90: 1259-1266. PMID: 35072956

  • Hadarovich, A., Chakravarty, D., Tuzikov, A.V., Ben-Tal, N., Kundrotas, P.J., Vakser, I.A., 2021, Structural motifs in protein cores and at protein-protein interfaces are different, Protein Sci., 30:381-390.

  • Singh, A., Dauzhenka, T., Kundrotas, P.J., Sternberg, M.J.E., Vakser, I.A., 2020, Application of docking methodologies to modeled proteins, Proteins, 88:1180-1188.

  • Kundrotas, P.J., Anishchenko, I., Dauzhenka, T., Kotthoff, I., Mnevets, D., Copeland, M.M., Vakser, I.A., 2018, DOCKGROUND: A comprehensive data resource for modeling of protein complexes, Protein Sci., 27:172-181.

  • Kundrotas, P.J., Zhu, Z., Janin, J., Vakser, I.A., 2012, Templates are available to model nearly all complexes of structurally characterized proteins, Proc. Natl. Acad. Sci. USA, 109:9438–9441.

  • Ruvinsky, A.M., Kirys, T., Tuzikov, A.V., Vakser, I.A., 2011, Side-chain conformational changes upon protein-protein association, J. Mol. Biol., 408: 356–365.

  • Kundrotas, P.J., Vakser, I.A., 2010, Accuracy of protein-protein binding sites in high-throughput template-based modeling, PLoS Comp. Biol., 6: e1000727.

  • Ruvinsky, A.M., Vakser, I.A., 2010, Sequence composition and environment effects on residue fluctuations in protein structures, J. Chem. Phys., 133:155101.

  • Ruvinsky, A.M., Vakser, I.A., 2008, Chasing funnels on protein-protein energy landscapes at different resolutions, Biophys. J., 95:2150–2159.

  • Tovchigrechko, A., Wells, C.A., Vakser, I.A., 2002, Docking of protein models, Protein Sci., 11:1888–1896.

  • Tovchigrechko, A., Vakser, I.A., 2001, How common is the funnel-like energy landscape in protein-protein interactions? Protein Sci., 10:1572–1583.

  • Vakser, I.A., Matar, O.G., Lam, C.F., 1999, A systematic study of low-resolution recognition in protein-protein complexes, Proc. Natl. Acad. Sci. USA, 96:8477–8482.

  • Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A.A., Aflalo, C., Vakser, I.A., 1992, Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques, Proc. Natl. Acad. Sci. USA, 89:2195-2199.