Alignments and Improved Refinements for High-Accuracy Protein Structure Modeling

描述（由申请人提供）：该题为“Correct Alignments and Improved Refinements for High-Accuracy Structure Modeling”的提案紧密遵循NIH RFA定义的两个目标，即（1）获得近端同源物（超过30%序列同一性）的晶体结构质量模型和（2）构建远端同源物（低至10%序列同一性）的高精度模型。我们相信，实现这些目标将需要开发（a）整合蛋白质序列家族上所有可用的生物学和结构信息的新方法，以改善靶蛋白序列与已知结构模板的比对，（2）对蛋白质结构可接近的构象空间进行采样的新方法，以及（3）提供近天然蛋白质结构模型的精确改进的新方法。我们将通过以下具体目标实现这些目标。 (1)通过结合广泛的不同信息来源，在靶蛋白序列和结构模板之间生成准确的比对。实现这一目标的关键是将这些数据整合到一个统一的框架中。我们将发展的概念，残基位置注释（RPA）的同源性建模，其中序列中的不同位置有不同的影响，根据其属性，来自多个序列比对的建模过程。我们将使用平均场最小化的框架来设计一个新的对齐包，该对齐包包含不同类型的约束，甚至是非加性的约束。 (2)为目标蛋白建立高精度模型。基于靶序列与结构已知的模板蛋白序列之间的给定序列比对的同源性建模通常涉及两个步骤：环构建，以固定比对中的插入和缺失区域，以及侧链建模。我们将从我们在开发这两个问题的解决方案方面的丰富经验中详细阐述，以提出规避这些困难的新方法。 (3)改进初始模型以使用结构细化生成晶体结构质量模型。我们将遵循三个方向（a）最小化和分子动力学与改进的力场，包括量子力学条款，（B）最小化和分子动力学与改进的隐式溶剂模型和（c）能量最小化与合作的多体能量条款。 (4)将本提案中开发的所有计算机程序组织成一个以用户为中心的软件包，包括可视化计算工具，使其能够被生物界广泛使用。

DESCRIPTION (provided by applicant): This proposal entitled "Correct Alignments and Improved Refinements for High-Accuracy Structure Modeling" follows closely the two goals defined by the NIH RFA, namely, (1) getting crystal structure quality models for close homologs (more than 30% sequence identity) and (2) building high accuracy models for remote homologs (as low as 10% sequence identity). We believe that reaching these goals will require the development of (a) new approaches to integrate all biological and structural information available on a protein sequence family to improve the alignment of the target protein sequence to a known structural template, (2) new methods for sampling the conformational space accessible to a protein structure, and (3) new methods that provide accurate refinements of near native protein structural models. We will achieve these goals through the following specific aims. (1) Generate accurate alignments between the target protein sequence and a structural template by combining a wide range of different sources of information. Essential to this aim is the integration of these data into a unified framework. We will develop the concept of residue position annotation (RPA) for homology modeling, in which different positions in the sequence have different impact in the modeling procedure depending on their properties, derived from multiple sequence alignments. We will use the framework of mean field minimization to design a new alignment package that incorporate different types of constraints, even non-additive. (2) Build high accuracy models for the target protein. Homology modeling based on a given sequence alignment between a target sequence and the sequence of a template protein whose structure is known usually involve two steps: loop building, to fix regions of insertion and deletion in the alignment, and side-chain modeling. We will elaborate from our extensive experience in developing solutions to both problems to propose new approaches that circumvent these difficulties. (3) Improve initial models to generate crystal structure quality models using structure refinement. We will follow three directions (a) minimization and molecular dynamics with improved force fields, including quantum mechanical terms, (b) minimization and molecular dynamics with improved implicit solvent models and (c) energy minimization with cooperative many-body energy terms. (4) Organize all computer programs developed within this proposal into a user-centric package, including visual computing tools, to make it accessible to the biological community at large.