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主要取决于其准确性。然而，高分辨率的计算方法，可以可靠地产生蛋白质结构模型或接近实验质量的发展仍然是一个未解决的问题，虽然在过去的十年中取得了显着的进展。主要的困难包括巨大而复杂的蛋白质构象空间，更重要的是，没有令人满意的准确性和灵敏度的评分功能。在这个项目中，研究试图回答一个具有挑战性的问题？使用现有的可能不敏感和不准确的评分函数，仍然可以高精度地建模蛋白质结构吗？与通常的全局优化描述构象能的评分函数的方法不同，研究人员探索了一个新的方向，通过在多个精心选择的基于知识，基于物理或基于回归的评分函数中有效地采样常见的低评分区域来建模蛋白质结构。这种新的方法解决了评分函数不敏感性问题的基础上的假设，即本地或类似本地的构象应满足大多数现有的良好的评分功能，产生低评分值。对多个评分函数进行采样允许容忍个体评分函数中的不敏感性和缺陷，并识别可以最好地满足大多数评分函数的构象，这最终将导致显著的分辨率改善。 研究人员通过将其应用于概念验证从头算蛋白质环结构预测问题来验证这种采样策略。这项工作涉及将多个评分功能，包括三重态扭转角评分，物理能量，基于距离的潜力，环闭合评分等，整合到采样方案中，目标是以接近实验分辨率可靠地预测环骨架结构。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。