# Integrated Resource for Protein Recognition Studies

> **NIH NIH R01** · UNIVERSITY OF KANSAS LAWRENCE · 2020 · $318,096

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 10000106
- **Project number:** 5R01GM074255-16
- **Recipient organization:** UNIVERSITY OF KANSAS LAWRENCE
- **Principal Investigator:** ILYA VAKSER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $318,096
- **Award type:** 5
- **Project period:** 2005-03-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000106

## Citation

> US National Institutes of Health, RePORTER application 10000106, Integrated Resource for Protein Recognition Studies (5R01GM074255-16). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10000106. Licensed CC0.

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