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：
7708263
负责人：
XIAOQIN ZOU
金额：
$ 18.94万
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 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7708263
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.

描述（由申请人提供）：可靠且高效的能量评分功能对于准确的蛋白质结构预测、蛋白质设计和计算机辅助药物发现至关重要。不幸的是，这种能量评分功能仍然存在。最成功的评分函数类型可能是基于统计潜力（也称为基于知识）的评分函数。尽管取得了显着的成功，但这些评分函数存在以下问题：1）其成对势能函数的推导过于简单化；2）仅考虑（低能量）天然结构，而忽略（高能量）非天然结构。因此，这些评分函数很难从一大群诱饵（即非本机）结构中辨别出本机结构。例如，通常发现基于统计势的评分函数在预测蛋白质-配体结合模式方面的成功率相对较低，并且在虚拟数据库筛选中失败。在这个项目中，我们建议推导出一种新型的能量评分函数，用于预测蛋白质结构以及蛋白质与 RNA、DNA 或配体的相互作用。我们基于统计力学的方法的新颖性有两个方面：} 1）包括非自然状态/结构以实现更好的构象采样，2）使用新颖的迭代方法严格推导有效的成对势函数。我们将使用已知的不同集合来测试和完善我们的新评分函数。该项目中开发的所有源代码和可执行文件将免费向公众开放。为了直接测试我们的方法，我们与实验人员建立了密切的合作，研究新型抗癌剂 PRIMA-1 的机制。这项生物信息学驱动的研究可能会带来治疗和/或预防人类乳腺癌的潜在治疗应用。我们的初步结果表明我们的新能源评分功能具有良好的性能。我们的初步研究还发现了一种新的有效药物，可以显着杀死人类乳腺癌细胞。我的生物信息学专业知识与合作者的生化和癌症研究专业知识的协同组合，为我们寻找 PRIMA-1 的分子靶点铺平了道路，希望能够确定用于治疗和/或预防人类乳腺癌的新型抗肿瘤药物。