Consensus and Covariance Proteins: Stability, Cooperativity, Function, & Design

共识和协方差蛋白质：稳定性、协作性、功能、

• 批准号: 10798386
• 负责人: DOUGLAS E. BARRICK
• 金额: $ 8.16万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10798386
• 关键词: Affect; Affinity; Amino Acid Sequence; Binding; Catalysis; Consensus; Coupling; Disease; Elements; Funding; Health; Learning; Mainstreaming; Methods; Molecular Biology; Mutagenesis; Pattern; Pharmaceutical Preparations; Positioning Attribute; Property; Protein Engineering; Proteins; Research; Research Project Grants; Role; Sequence Alignment; Site; Source; Specificity; Taxonomy; Testing; Thermodynamics; Time; clinical diagnostics; deep learning; design; enzyme activity; falls; insight; learning strategy; protein function; protein structure prediction; statistics; success; tool; Affect; Affinity; Amino Acid Sequence; Binding; Catalysis; Consensus; Coupling; Disease; Elements; Funding; Health; Learning; Mainstreaming; Methods; Molecular Biology; Mutagenesis; Pattern; Pharmaceutical Preparations; Positioning Attribute; Property; Protein Engineering; Proteins; Research; Research Project Grants; Role; Sequence Alignment; Site; Source; Specificity; Taxonomy; Testing; Thermodynamics; Time; clinical diagnostics; deep learning; design; enzyme activity; falls; insight; learning strategy; protein function; protein structure prediction; statistics; success; tool

PROJECT SUMMARY/ABSTRACT With the exponential increase in protein sequences, the statistical power of multiple sequence alignments (MSAs) has been recognized as an important source of information for analysis and design of proteins. For example, consensus design, where the most frequent residue is selected from each position of an MSA, has been recognized as generating folded, functional, stabilized proteins. At the same time, covariance among pairs of residues at different positions has been recognized as having powerful value in predicting protein structures, and is a major component of the recent successes of deep-learning methods such as AlphaFold. Despite the power of pairwise residue covariance, these statistics have seen limited use in design of proteins. Moreover, it is not presently known which properties of proteins—for example, folding, stability, binding, and catalysis--are affected by the forces that contribute to covariance. The proposed research will combine consensus design with covariance. Using well-behaved consensus proteins we designed in the previous funding cycle, we will use two complementary methods to design proteins with varying amounts of covariance and consensus information. The first uses a statistical thermodynamic "Potts" formalism to determine coupling biases between residue pairs and separate them from single-site biases. This separation allows us to adjust the amount of covariance information in our designs. The second method uses singular value decomposition (SVD) to transform an MSA to a set of coordinates that separate consensus from covariance. Within this space, sequences fall into well-defined clusters that have shared conservation and covariance patterns. We will use the coordinate values of these clusters to design sequences with specific patterns of covariance. Designed proteins will be produced in the lab, and their stabilities, binding affinities, and enzyme activities will be determined. By projecting Potts designs into SVD space, we will refine the Potts designs and gain insights into the specific pair correlations that position each SVD cluster. We will also project extant sequences with known specificities into SVD space to predict functional features of clusters, which will be tested experimentally. To identify specific consensus and covariance sequence elements that contribute to stability and activity patterns, we will make single-and multisite point substitutions that are found in our consensus, Potts, and SVD designs. These will focus the non-additivity of consensus stabilization, which has been suggested from the previous funding cycle, which is likely to be related to covariance. These mutagenesis studies will also better define the striking stability and activity differences we have seen in preliminary Potts designs. Overall, the proposed research will better define the roles of covariance in the various properties of proteins, and will lead to new tools for more precise protein design. Furthermore, we expect better connect the SVD method to taxonomy, and help establish it as a mainstream tool for molecular biology research.

项目摘要/摘要 随着蛋白质序列的指数增加，多个序列比对的统计能力 （MSA）被认为是用于分析和设计蛋白质的重要​​信息来源。为了 例如，共识设计，其中最常存在的是从MSA的每个位置选择的， 已被公认为产生折叠，功能性，稳定的蛋白质。同时，协方差 在不同位置的一对救援中，人们被认为在预测方面具有强大的价值 蛋白质结构，是深度学习方法最近成功的主要组成部分 Alphafold。尽管成对居住协方差的力量，但这些统计数据的用途有限 蛋白质的设计。此外，目前尚不知道蛋白质的哪些特性，例如，折叠， 稳定性，结合和催化剂 - 受促进协方差的力的影响。 拟议的研究将将共识设计与协方差相结合。使用行为良好的共识 我们在上一个资金周期中设计的蛋白质，我们将使用两种完整的方法设计 具有不同数量的协方差和共识信息的蛋白质。第一个使用统计 热力学“ potts”格式，以确定居住对之间的耦合偏差并分开 来自单位点偏见。这种分离使我们能够调整我们的协方差信息量 设计。第二种方法使用单数值分解（SVD）将MSA转换为一组 协调将共识与协方差分开的协调。在这个空间内，序列属于明确的 具有共享保护和协方差模式的集群。我们将使用这些坐标值 簇以设计具有协方差的特定模式的序列。设计的蛋白质将在 将确定实验室及其稳定性，结合亲和力和酶活性。通过投影Potts 设计到SVD空间，我们将完善Potts的设计并获得特定对相关性的见解 每个SVD群集的位置。我们还将在SVD中具有已知规范的项目范围序列 预测簇的功能特征的空间，将通过实验进行测试。 确定有助于稳定性和活动的特定共识和协方差序列元素 模式，我们将在我们的共识，Potts和Potts和 SVD设计。这些将集中于共识稳定的非促进性，已提出 从以前的资金周期开始，这可能与协方差有关。这些诱变研究将 还可以更好地定义我们在初步Potts设计中看到的惊人稳定性和活动差异。 总体而言，拟议的研究将更好地定义协方差在蛋白质的各种特性中的作用， 并将为更精确的蛋白质设计提供新的工具。此外，我们希望更好地连接SVD 分类法的方法，并帮助将其确立为分子生物学研究的主流工具。