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A new scoring framework for selecting structural models

用于选择结构模型的新评分框架

基本信息

批准号：
7943077
负责人：
XIAOQIN ZOU
金额：
$ 22.73万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2012-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7943077
关键词：
Agreement Algorithms Animal Model Antineoplastic Agents Apoptosis Binding Binding Proteins Biochemical Bioinformatics Breast Cancer Cell Cations Cell Culture Techniques Chemicals Collaborations Communities Complex Computer Simulation Computer-Aided Design Computing Methodologies DNA Data Databases Derivation procedure Development Docking Drug Design Exposure to Human In Vitro International Lead Letters Licensing Ligands Malignant Neoplasms Mechanics Methods Molecular Target Performance Potential Energy Prevention Protein Structure Databases Proteins RNA-Protein Interaction Research Research Personnel Sampling Scheme Screening procedure Series Software Tools Source Code Statistical Mechanics Structural Models Structure Testing Therapeutic Training Validation Work anticancer research antitumor agent base design drug discovery enzyme activity human disease in vivo inhibitor/antagonist killings knowledge base malignant breast neoplasm novel open source protein structure protein structure prediction success virtual

项目摘要

DESCRIPTION (provided by applicant): 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.