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

项目摘要/摘要 随着蛋白质序列的指数增长，多个序列比对的统计能力 (MSA)已被认为是蛋白质分析和设计的重要信息来源。为 例如共识设计，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、功能的、稳定的蛋白质。同时，协方差 在不同位置的残基对中被认为具有很强的预测价值 蛋白质结构，是最近深度学习方法成功的主要组成部分，例如 AlphaFold。尽管两两残差协方差的威力很大，但这些统计量在 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--受促成协方差的力量的影响。 建议的研究将把共识设计和协方差结合起来。使用行为良好的共识 我们在前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 具有不同协方差和一致信息量的蛋白质。第一个使用统计量 确定残基对之间耦合偏向并将其分离的热力学“Potts”形式 不受单一地点偏见的影响。这种分离允许我们调整我们的 设计。第二种方法使用奇异值分解(SVD)将MSA转换为一组 协调共识与协方差的分离。在这个空间中，序列属于定义明确的 具有共同守恒和协方差模式的星系团。我们将使用这些元素的坐标值 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 将测定它们的稳定性、结合亲和力和酶活性。通过投射波茨 设计进入奇异值分解空间，我们将改进Potts设计并深入了解特定的配对关联 这定位了每个奇异值分解簇。我们还将现有的具有已知特性的序列投影到奇异值分解中 空间来预测星系团的功能特征，这将进行实验测试。 确定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替换，这在我们的共识、Potts和 SVD设计。这些将侧重于协商一致稳定的非加性，这一点已被提出。 来自上一个供资周期的数据，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POTS初步设计中看到的惊人的稳定性和活性差异。 总体而言，拟议的研究将更好地定义协方差在蛋白质各种性质中的作用， 并将导致更精确的蛋白质设计的新工具。此外，我们希望更好地连接SVD 方法进行分类，并将其确立为分子生物学研究的主流工具。

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.