Many important cellular processes, such as gene expression regulation, transport, and cell division, are carried out by protein complexes. To compensate for the difficulty of solving protein complex structures, computational approaches for protein docking prediction have been developed over recent decades. Although steady progress has been observed in the field, current methods and developments are largely limited to pairwise protein docking. The proposed project aims to extend the capability of protein docking methods to two important classes of protein complexes, multimeric complexes and disordered interactions, while making further improvements in pairwise protein docking. A substantial fraction of protein complexes involved in critical cellular processes are multimeric complexes or involve interactions with intrinsically disordered regions. As these two types of complex structures are particularly difficult to determine by experimental means, it is an urgent and important task for protein structural bioinformatics to develop efficient and accurate computational methods to build models for these types of complexes. Structure models provide hypotheses for designing biochemical experiments to elucidate interactions in a complex, which can lead to solving full or partial complex structures. The structural information provided by multimeric protein docking is not merely an incremental scale-up of pairwise docking; multimeric docking can also predict interacting and non-interacting subunits and assembly order within a complex. The developed docking methods will be applied to build models of protein complexes from four biological systems in collaboration with biologists. The proposed methods for protein complex structure prediction are also useful for designing drugs for protein-protein interaction targets as well as artificial design of protein complexes and bio- nanomaterials. Collaboration with biologists will further enhance integrated computational and experimental approaches in biology.

Public Health Relevance

Many important cellular processes are carried out by various types of protein complexes, which include pairwise protein complexes, multimeric complexes, and disordered interactions. We propose to develop computational methods that can effectively model multifarious protein complexes and thereby facilitate understanding of molecular mechanisms of protein interactions and diseases that involve protein interactions as well as the development of drug molecules for protein-protein interaction targets.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM123055-04
Application #
9879748
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Mcguirl, Michele
Project Start
2017-06-01
Project End
2021-02-28
Budget Start
2020-03-01
Budget End
2021-02-28
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Purdue University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
072051394
City
West Lafayette
State
IN
Country
United States
Zip Code
47907
Terashi, Genki; Kihara, Daisuke (2018) De novo main-chain modeling with MAINMAST in 2015/2016 EM Model Challenge. J Struct Biol 204:351-359
Peterson, Lenna X; Shin, Woong-Hee; Kim, Hyungrae et al. (2018) Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions. Proteins 86 Suppl 1:311-320
Ding, Ziyun; Wei, Qing; Kihara, Daisuke (2018) Computing and Visualizing Gene Function Similarity and Coherence with NaviGO. Methods Mol Biol 1807:113-130
Ding, Ziyun; Kihara, Daisuke (2018) Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Curr Protoc Protein Sci 93:e62
Terashi, Genki; Kihara, Daisuke (2018) Protein structure model refinement in CASP12 using short and long molecular dynamics simulations in implicit solvent. Proteins 86 Suppl 1:189-201
Terashi, Genki; Kihara, Daisuke (2018) De novo main-chain modeling for EM maps using MAINMAST. Nat Commun 9:1618
Shin, Woong-Hee; Kihara, Daisuke (2018) Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method. Methods Mol Biol 1762:105-121
Peterson, Lenna X; Roy, Amitava; Christoffer, Charles et al. (2017) Modeling disordered protein interactions from biophysical principles. PLoS Comput Biol 13:e1005485
Han, Xusi; Wei, Qing; Kihara, Daisuke (2017) Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER. Curr Protoc Bioinformatics 60:3.14.1-3.14.15
Shin, Woong-Hee; Christoffer, Charles W; Kihara, Daisuke (2017) In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 131:22-32