SGER: A Novel Multi-Scoring Functions Sampling Approach to Impove Protein Modeling Resolution and It's Applications in Protein Loop Structure Prediction

SGER：一种提高蛋白质建模分辨率的新型多评分函数采样方法及其在蛋白质环结构预测中的应用

• 批准号: 0829382
• 负责人: Yaohang Li
• 金额: --
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0829382&HistoricalAwards=false
• 关键词: SGER; Novel; Multi; Scoring; Functions

The value of computer-generated protein structural models in biological research and practice relies critically on their accuracy. However, development of high-resolution computational approaches that can reliably produce protein structural models with or close to experimental quality remains an unsolved problem, though significant advances have been made in the past ten years. The main difficulties include the tremendously large and complex protein conformation space and, more importantly, the absence of scoring functions with satisfactory accuracy as well as sensitivity. In this project, the research seeks to answer a challenging question ? can one still model protein structures with high accuracy using the existing scoring functions which are potentially insensitive and inaccurate? Different from the common approaches of globally optimizing a scoring function describing the conformational energy, the investigators explore a new direction to model protein structures via efficiently sampling the common low score regions in multiple carefully-selected knowledge-based, physics-based, or regression-based scoring functions. This new approach addresses the scoring function insensitivity problem based on the assumption that the native or native-like conformations should satisfy most of the existing good scoring functions by yielding low score values. Sampling multiple scoring functions allows toleration of insensitivity and deficiency in individual scoring functions and identification of conformations that can best satisfy most scoring functions, which will eventually lead to significant resolution improvement. The investigators verify this sampling strategy by applying it to a proof-of-concept ab initio protein loop structure prediction problem. The work involves integrating multiple scoring functions, including triplet torsion angle score, physical energy, distance-based potential, loop closure score, and others, into the sampling scheme with the goal of reliably predicting loop backbone structures with near experimental resolution. The computational tools for loop structure prediction are being made available as a software package to the protein modeling research community.

计算机生成的蛋白质结构模型在生物学研究和实践中的价值关键取决于其准确性。然而，尽管在过去十年中已经取得了重大进展，但能够可靠地生成具有或接近实验质量的蛋白质结构模型的高分辨率计算方法的开发仍然是一个未解决的问题。主要困难包括巨大且复杂的蛋白质构象空间，更重要的是，缺乏具有令人满意的准确性和灵敏度的评分函数。在这个项目中，研究试图回答一个具有挑战性的问题？人们仍然可以使用可能不敏感且不准确的现有评分函数来高精度地模拟蛋白质结构吗？与全局优化描述构象能量的评分函数的常见方法不同，研究人员通过在多个精心选择的基于知识、基于物理或回归的多个精心选择的常见低分区域中探索了蛋白质结构建模的新方向。基于评分函数。这种新方法基于以下假设解决了评分函数不敏感问题：天然或类天然构象应通过产生低分值来满足大多数现有的良好评分函数。对多个评分函数进行采样可以容忍各个评分函数的不敏感性和缺陷，并识别最能满足大多数评分函数的构象，这最终将导致分辨率的显着提高。 研究人员通过将其应用于从头开始的概念验证蛋白质环结构预测问题来验证这种采样策略。这项工作涉及将多种评分函数（包括三重态扭转角评分、物理能量、基于距离的势能、闭环评分等）集成到采样方案中，目的是以接近实验分辨率的方式可靠地预测环主干结构。用于环结构预测的计算工具正在作为软件包提供给蛋白质建模研究界。