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

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它们的准确性。然而，尽管在过去十年中已经取得了重大进展，但能够可靠地产生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发展仍然是一个悬而未决的问题。主要的困难包括巨大而复杂的蛋白质构象空间，以及更重要的是，缺乏令人满意的准确度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？人们仍然可以使用现有的评分功能高精度地模拟蛋白质结构吗？现有的评分功能可能不敏感和不准确。与描述构象能量的全局优化评分函数的常见方法不同，研究人员探索了一种新的方向来建模蛋白质结构，方法是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分区域。这种新的方法解决了得分函数不敏感的问题，基于这样的假设，即天然或类天然构象应该通过产生较低的得分值来满足大多数现有的良好的得分函数。对多个计分函数进行采样允许容忍个别计分函数中的不敏感性和缺陷，并识别最能满足大多数计分函数的构象，这最终将导致显著的分辨率提高。研究人员通过将其应用于从头算蛋白质环结构预测的概念验证问题来验证这种采样策略。这项工作涉及将多种评分函数，包括三重扭角评分、物理能量、基于距离的势、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的可靠预测环主干结构。用于环结构预测的计算工具正在作为一个软件包提供给蛋白质建模研究社区。

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.