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

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.

项目概要/摘要 随着蛋白质序列呈指数增长，多重序列比对的统计功效 （MSAs）已被认为是蛋白质分析和设计的重要信息来源。为了 例如，共识设计，其中最常见的残基是从 MSA 的每个位置中选择的， 已被认为可以产生折叠的、功能性的、稳定的蛋白质。同时，协方差 不同位置的残基对之间的预测已被认为在预测方面具有强大的价值 蛋白质结构，是最近成功的深度学习方法的主要组成部分，例如 阿尔法折叠。尽管成对残差协方差很强大，但这些统计数据在以下领域的用途有限： 蛋白质的设计。此外，目前尚不清楚蛋白质的哪些特性，例如折叠、 稳定性、结合力和催化作用——受到促成协变的力量的影响。 拟议的研究将共识设计与协方差结合起来。使用行为良好的共识 我们在上一个资助周期设计的蛋白质，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”形式确定残基对之间的耦合偏差并将其分离 来自单站点偏差。这种分离使我们能够调整协方差信息的数量 设计。第二种方法使用奇异值分解 (SVD) 将 MSA 转换为一组 将共识与协方差分开的坐标。在这个空间内，序列属于明确定义的 具有共享守恒和协方差模式的簇。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，以及它们的稳定性、结合亲和力和酶活性将被确定。通过投影波茨 将设计引入 SVD 空间，我们将改进 Potts 设计并深入了解特定的配对相关性 定位每个 SVD 簇。我们还将把具有已知特异性的现有序列投影到 SVD 中 空间来预测簇的功能特征，这将通过实验进行测试。 识别有助于稳定性和活性的特定共识和协方差序列元素 模式，我们将进行单站点和多站点点替换，这些替换在我们的共识、Potts 和 SVD 设计。这些将集中共识稳定的非可加性，这已被建议 来自上一个融资周期，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 设计中看到的显着稳定性和活性差异。 总体而言，拟议的研究将更好地定义协方差在蛋白质各种特性中的作用， 并将带来更精确蛋白质设计的新工具。此外，我们期望更好地连接 SVD 方法分类学，并帮助将其确立为分子生物学研究的主流工具。

项目成果

Size and sequence and the volume change of protein folding.

• DOI: 10.1021/ja200228w
• 发表时间: 2011-04-20
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Rouget, Jean-Baptiste;Aksel, Tural;Roche, Julien;Saldana, Jean-Louis;Garcia, Angel E.;Barrick, Doug;Royer, Catherine A.
• 通讯作者: Royer, Catherine A.

Synergistic enhancement of cellulase pairs linked by consensus ankyrin repeats: Determination of the roles of spacing, orientation, and enzyme identity.

• DOI: 10.1002/prot.25047
• 发表时间: 2016-08
• 期刊: Proteins
• 影响因子: 2.9
• 作者: Cunha ES;Hatem CL;Barrick D
• 通讯作者: Barrick D

Predicting coupling limits from an experimentally determined energy landscape.

根据实验确定的能量景观预测耦合极限。

• DOI: 10.1073/pnas.0608756104
• 发表时间: 2007
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Street,TimothyO;Bradley,ChristinaM;Barrick,Doug
• 通讯作者: Barrick,Doug

Predicting repeat protein folding kinetics from an experimentally determined folding energy landscape.

从实验确定的折叠能量景观预测重复蛋白质折叠动力学。

• DOI: 10.1002/pro.9
• 发表时间: 2009
• 期刊: Protein science : a publication of the Protein Society
• 作者: Street,TimothyO;Barrick,Doug
• 通讯作者: Barrick,Doug

Unique features of the folding landscape of a repeat protein revealed by pressure perturbation.

• DOI: 10.1016/j.bpj.2010.02.044
• 发表时间: 2010-06
• 期刊: Biophysical journal
• 影响因子: 3.4
• 作者: Jean-Baptiste Rouget;M. Schroer;C. Jeworrek;Matthias Pühse;J. Saldana;Yannick Bessin;M. Tolan;D. Barrick;R. Winter;C. Royer