Most proteins conduct their functions through interactions with other proteins. The atomic-level quaternary structure of protein-protein complexes can provide a clear physical landscape to help our understanding of how the interactions are conducted in living cells and how new therapies can be designed to regulate the interaction networks. Since experimental characterization of complex structures is difficult and expensive, computational modeling of the protein-protein interactions has been a major theme in computational biology. Most efforts have been focused on rigid-body docking, which builds complex conformations by combining known structures of interacting components. But docking is applicable only when the monomer structures are known and the success rate is low when components involve conformational change upon binding. Alternatively, complex structures can be deduced from homologous structures with alignments generated by the multi-chain threading technique. While the latter approach has the advantage of not requiring solved monomer structures, the modeling accuracy for distant-homology targets is unreliable and the threading alignments generally have gaps and errors. In this project, we seek to develop a new generation of computational approaches aiming to significantly improve the coverage and accuracy of protein-protein complex structure modeling by the integration of the cutting-edge rigid-body docking and threading assembly simulations.
The specific aims i nclude: (1) development of new interface-specific threading algorithms for distant-homology detection; (2) new fragment assembly simulation method for full-length complex structure construction and refinement; (3) development of new strategies for ab initio docking; (4) integration of the threading and docking methods for low-resolution docking and template-based docking structure refinement. The algorithms will be systematically trained on large-scale benchmark protein sets and tested in community-wide docking experiments, with focus on modeling the binding-induced conformational changes and predicting high-resolution complex structures for distantly homologous proteins. The methods and potentials developed in this project will be made freely available to the general community through Internet websites. The long-term goals of this project are (a) to develop advanced computer methods for accurate structure modeling of various protein-protein complexes, and (b) to utilize the methods for genome-wide structure modeling and structure-based function annotation of protein-protein networks of various organisms.

Public Health Relevance

Protein-protein interactions are responsible for the development of pathological processes such as Alzheimer's disease and cancer. The atomic structures of protein-protein complexes are needed for designing synthetic compounds and biologics to disrupt or enhance protein-protein interactions. The goal of this project is to develo computational algorithms to build atomic structure of the complexes from amino acid sequences. The success of such method developments can result in important impact on drug discovery and public health.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM116960-03
Application #
9405029
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Wehrle, Janna P
Project Start
2016-01-01
Project End
2019-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Brender, Jeffrey R; Shultis, David; Khattak, Naureen Aslam et al. (2017) An Evolution-Based Approach to De Novo Protein Design. Methods Mol Biol 1529:243-264
Vreven, Thom; Pierce, Brian G; Borrman, Tyler M et al. (2017) Performance of ZDOCK and IRAD in CAPRI rounds 28-34. Proteins 85:408-416
Hu, Xiuzhen; Dong, Qiwen; Yang, Jianyi et al. (2016) Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals. Bioinformatics 32:3260-3269
Li, Chunhua; Lv, Dashuai; Zhang, Lei et al. (2016) Approach to the unfolding and folding dynamics of add A-riboswitch upon adenine dissociation using a coarse-grained elastic network model. J Chem Phys 145:014104
Wang, Yan; Virtanen, Jouko; Xue, Zhidong et al. (2016) Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. Acta Crystallogr D Struct Biol 72:616-28
Hu, X; Dong, Q; Yang, J et al. (2016) Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals. Bioinformatics 32:3694
Dong, Qiwen; Menon, Rajasree; Omenn, Gilbert S et al. (2015) Structural Bioinformatics Inspection of neXtProt PE5 Proteins in the Human Proteome. J Proteome Res 14:3750-61
Brender, Jeffrey R; Zhang, Yang (2015) Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles. PLoS Comput Biol 11:e1004494
Yang, Jianyi; Zhang, Yang (2015) Protein Structure and Function Prediction Using I-TASSER. Curr Protoc Bioinformatics 52:5.8.1-15