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: 10534973
• 负责人: DOUGLAS E. BARRICK
• 金额: $ 36.56万
• 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/10534973
• 关键词: Adopted; Affect; Affinity; Amino Acid Sequence; Binding; Biological; Catalysis; Communication; Consensus; Consensus Sequence; Coupling; DNA Binding; Data; Disease; Elements; Enzymes; Family; Frequencies; Funding; Health; Learning; Linear Algebra; Mainstreaming; Measures; Methods; Modeling; Molecular Biology; Mutagenesis; Mutation; Pathway interactions; Pattern; Pharmaceutical Preparations; Phylogenetic Analysis; Positioning Attribute; Property; Protein Binding Domain; Protein Engineering; Protein Family; Proteins; Research; Research Project Grants; Role; Sampling; Sequence Alignment; Sequence Determination; Site; Site-Directed Mutagenesis; Source; Specificity; Structure; Taxonomy; Testing; Thermodynamics; Time; Variant; base; clinical diagnostics; deep learning; design; enzyme activity; falls; globular protein; insight; learning strategy; novel; novel strategies; predictive tools; protein folding; protein function; protein structure; protein structure function; rapid growth; statistics; success; tool; vector

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.

项目总结/摘要 随着蛋白质序列的指数增长，多序列比对的统计能力 蛋白质结构分析（MSAs）已被认为是蛋白质分析和设计的重要信息来源。为 例如，共有设计，其中从MSA的每个位置选择最频繁的残基， 已经被认为是产生折叠的、功能性的、稳定的蛋白质。与此同时，协方差 在不同位置上的残基对之间的差异已经被认为在预测中具有强大的价值 蛋白质结构，是最近成功的深度学习方法的主要组成部分， 阿尔法折叠尽管成对残差协方差的能力，这些统计量在 蛋白质的设计此外，目前还不知道蛋白质的哪些特性，例如折叠， 稳定性、结合和催化作用--都受到促成协变性的力量的影响。 拟进行的研究将采用联合收割机一致性设计和协方差设计相结合。使用行为良好的共识 我们在上一个资助周期中设计的蛋白质，我们将使用两种互补的方法来设计 具有不同量的协方差和一致性信息的蛋白质。第一种方法是使用统计 热力学“Potts”形式主义，以确定残基对之间的耦合偏差，并将其分离 单点偏差的影响。这种分离允许我们调整协方差信息的量， 的设计.第二种方法使用奇异值分解（SVD）将MSA变换为一组 将一致性和协方差分开的坐标。在这个空间内，序列落入定义明确的 具有共享守恒和协方差模式的聚类。我们将使用这些的坐标值 聚类来设计具有特定协方差模式的序列。设计的蛋白质将在 实验室，并确定它们的稳定性、结合亲和力和酶活性。通过投射波茨 设计到SVD空间中，我们将改进Potts设计并深入了解特定的对相关性 来定位每个SVD聚类。我们还将已知特异性的现存序列投影到SVD中 空间来预测集群的功能特性，这将通过实验进行测试。 确定有助于稳定性和活性的特定一致性和协方差序列元素 模式，我们将进行单位点和多位点替换，这些替换在我们的共识中找到，Potts， SVD设计这些将集中在共识稳定的非加性，这已经被建议 这可能与协方差有关。这些诱变研究将 也更好地定义了我们在Potts初步设计中看到的惊人的稳定性和活性差异。 总的来说，拟议的研究将更好地定义协方差在蛋白质各种性质中的作用， 并将为更精确的蛋白质设计带来新的工具。此外，我们期望更好地连接SVD 方法分类学，并帮助建立它作为一个主流的工具，分子生物学研究。