Reliable and efficient energy scoring functions are vitally important for accurate protein structure prediction, protein design and computer-aided drug discovery. Unfortunately, such energy scoring functions still remain at large. Probably the most successful type of scoring functions is the statistical potential-based (also referred to as knowledge-based) scoring functions. Despite achieving significant success, these scoring functions suffer from 1) oversimplified derivation of their pairwise potential energy functions and 2) sole consideration of (low-energy) native structures while ignoring (high-energy) non-native structures. Consequently, these scoring functions have difficulty in discerning native structures from a large ensemble of decoy (i.e., non-native) structures. For instance, statistical potential-based scoring functions were usually found to have relatively low success rates in predicting protein-ligand binding modes and failed in virtual database screening. In this project we propose to derive a new type of energy scoring functions for predicting protein structures and protein interactions with RNA, DNA, or ligands. The novelty of our statistical mechanics-based approach is two-fold:} 1) including the non-native states/structures for better conformational sampling, and 2) using a novel iterative method to rigorously derive the effective pairwise potential functions. We will test and refine our new scoring functions using known diverse sets. All the source codes and executables developed in this project will be freely available to the public. To directly test our methods, we have established closed collaborations with experimentalists on studying the mechanism of a novel anti-cancer agent PRIMA-1. This bioinformatics-driven study may lead to potential therapeutic application for treatment and/or prevention of human breast cancer. Our preliminary results show promising performance of our new energy scoring functions. Our preliminary studies have also identified a new potent agent that dramatically kills human breast cancer cells. The synergetic combination of my bioinformatics expertise with my collaborators'biochemical and cancer research expertise paves our way to find molecular target(s) of PRIMA-1 with the hope of identifying novel anti-tumor agents for treatment and/or prevention of human breast cancer.

Public Health Relevance

The ability of accurate protein structure selection is vitally important for protein design and for rational drug design. This proposal outlines a new strategy for structure selection through the development of a novel scoring scheme. Through experimental collaborations with cancer researchers, the results derived from the proposed research would potentially impact our ability to intervene human diseases such as cancer.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21GM088517-02
Application #
7943077
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Remington, Karin A
Project Start
2009-09-30
Project End
2012-11-30
Budget Start
2010-09-01
Budget End
2012-11-30
Support Year
2
Fiscal Year
2010
Total Cost
$227,250
Indirect Cost
Name
University of Missouri-Columbia
Department
Type
Organized Research Units
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
Liang, Yayun; Besch-Williford, Cynthia; Aebi, Johannes D et al. (2014) Cholesterol biosynthesis inhibitors as potent novel anti-cancer agents: suppression of hormone-dependent breast cancer by the oxidosqualene cyclase inhibitor RO 48-8071. Breast Cancer Res Treat 146:51-62
Huang, Sheng-You; Zou, Xiaoqin (2014) ITScorePro: an efficient scoring program for evaluating the energy scores of protein structures for structure prediction. Methods Mol Biol 1137:71-81
Huang, Sheng-You; Zou, Xiaoqin (2014) A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method. Nucleic Acids Res 42:e55
Xu, Juan; Xie, Jie; Yan, Chengfei et al. (2014) A chemical genetic approach demonstrates that MPK3/MPK6 activation and NADPH oxidase-mediated oxidative burst are two independent signaling events in plant immunity. Plant J 77:222-34
Grinter, Sam Z; Zou, Xiaoqin (2014) A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions. J Comput Chem 35:932-43
Huang, Sheng-You; Yan, Chengfei; Grinter, Sam Z et al. (2013) Inclusion of the orientational entropic effect and low-resolution experimental information for protein-protein docking in Critical Assessment of PRedicted Interactions (CAPRI). Proteins 81:2183-91
Grinter, Sam Z; Yan, Chengfei; Huang, Sheng-You et al. (2013) Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark. J Chem Inf Model 53:1905-14
Huang, Sheng-You; Zou, Xiaoqin (2013) A nonredundant structure dataset for benchmarking protein-RNA computational docking. J Comput Chem 34:311-8
Yu, Tao; Wang, Xiao-Qing; Sang, Jian-Ping et al. (2012) Influences of mutations on the electrostatic binding free energies of chloride ions in Escherichia coli ClC. J Phys Chem B 116:6431-8
Huang, Sheng-You; Zou, Xiaoqin (2011) Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction. J Chem Inf Model 51:2107-14

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