The protein-protein docking problem is one of the focal points of activity in computational structural biology. The 3D structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The project will improve our understanding of fundamental properties of protein interaction and will facilitate development of better tools for prediction of protein complexes.
The Specific Aims of the project are: (1) Protein recognition data resources, (2) Advanced docking methodology, and (3) Integrated web-based environment. The long-term goals are: (a) development of an automated tool for reliable modeling of protein interactions, which will account for dynamic changes in the molecular structures and kinetics of protein association, and (b) utilization of this tool to understand the principles of protein interaction. The ultimate goal is structural modeling of a cell, as a new emerging frontier and a grand challenge of computational structural biology, with a promise of providing fundamental understanding of life at the molecular level, leading to important applications to biology and medicine. The focus of the proposal is an integrated system of resources for studying protein-protein 3D interactions. The core dataset consists of regularly updated and annotated co-crystallized protein-protein structures. The database of experimentally determined and simulated unbound complexes will be further expanded upon the core dataset. It will serve as a comprehensive benchmark set for the development of docking techniques. The database of complexes of protein models will provide a unique expansion of the core dataset for the development of docking capabilities in protein modeling, including genome-wide studies. The docking decoys will provide the community-wide testing ground for new scoring functions. The docking procedure will be developed further to make it more adequate to the challenges of structural modeling of protein-protein complexes. The development will make use of the rapidly growing body of experimentally determined structures of protein-protein complexes. The docking methods will utilize available knowledge on the protein targets, combined with the improved use of structural and physicochemical recognition factors. Coarse-graining of the docking in terms of structural representation, energy function, and sampling will be systematically explored, making use of the interface rotamer libraries and probabilities of conformational transitions. The docking procedure and the related databases will continue to be provided as a web-based public resource.

Public Health Relevance

The proposed research will facilitate development of modeling techniques capable of delivering starting points for structure-based drug design by providing structural information on docking modes of existing and potential drug targets.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM074255-16
Application #
10000106
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Flicker, Paula F
Project Start
2005-03-01
Project End
2021-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
16
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Kansas Lawrence
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
076248616
City
Lawrence
State
KS
Country
United States
Zip Code
66045
Kundrotas, Petras J; Anishchenko, Ivan; Dauzhenka, Taras et al. (2018) Dockground: A comprehensive data resource for modeling of protein complexes. Protein Sci 27:172-181
Dauzhenka, Taras; Kundrotas, Petras J; Vakser, Ilya A (2018) Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking. J Comput Chem 39:2012-2021
Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A (2018) Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. Biophys J 115:809-821
Kundrotas, Petras J; Anishchenko, Ivan; Badal, Varsha D et al. (2018) Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function. Proteins 86 Suppl 1:302-310
Badal, Varsha D; Kundrotas, Petras J; Vakser, Ilya A (2018) Natural language processing in text mining for structural modeling of protein complexes. BMC Bioinformatics 19:84
Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A (2017) Structural quality of unrefined models in protein docking. Proteins 85:39-45
Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A (2017) Modeling complexes of modeled proteins. Proteins 85:470-478
Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A et al. (2016) Template-Based Modeling of Protein-RNA Interactions. PLoS Comput Biol 12:e1005120
Im, Wonpil; Liang, Jie; Olson, Arthur et al. (2016) Challenges in structural approaches to cell modeling. J Mol Biol 428:2943-64
Lensink, Marc F; Velankar, Sameer; Kryshtafovych, Andriy et al. (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 84 Suppl 1:323-48

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