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Data-driven, evolution-based design of proteins

数据驱动、基于进化的蛋白质设计

基本信息

批准号：
10185231
负责人：
RAMA RANGANATHAN
金额：
$ 31.69万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10185231
关键词：
Address Agriculture Algorithms Amino Acid Sequence Amino Acids Area Bacillus subtilis Base Sequence Binding Biochemical Biological Assay Biological Models Biology Catalysis Characteristics Chemistry Chorismate Mutase Data Data Set Development Diagnostic Directed Molecular Evolution Engineering Environment Enzymes Escherichia coli Evolution Foundations Gene Duplication Gene Mutation Genome Geometry Goals Harvest Health Human In Vitro Individual Laboratories Learning Mathematics Metabolic Microfluidics Modeling Mutagenesis Mutation Orthologous Gene Outcome Pathway interactions Pattern Physics Process Property Protein Engineering Protein Family Proteins Research Route SH3 Domains Serine Protease Shapes Software Tools Specificity Statistical Models Structure Substrate Specificity System Testing Therapeutic Time Trypsin Variant Work Yeasts base chemical reaction chymotrypsin combinatorial computer framework design fitness gene synthesis in vivo information model molecular sequence database new technology novel strategies paralogous gene programs protein folding protein structure synthetic protein

项目摘要

Project Summary: Evolution builds proteins with a remarkable combination of characteristics. They can fold spontaneously and carry out difficult chemical reactions, but also are robust to perturbation and able to adapt as conditions of fitness fluctuate. In recent years, sequence-based statistical models have provided specific models for how all these properties are encoded in the amino acid sequence of proteins. Here, we propose a data-driven, evolution-based design (EBD) process that, with the developments outlined here, can address several basic problems in protein mechanism and evolution. We will unify and optimize approaches for EBD and then apply it (1) to quantify the functional sequence space of a protein family, (2) to parse the constraints on paralogs and orthologs of a protein family, and (3) to understand how substrate specificity in an enzyme can adapt through a process of stepwise variation and selection. The work is extensively supported by preliminary data, and is enabled by new technologies for statistical inference, gene synthesis, and high-throughput functional assays, both in vitro and in vivo. The outcomes will be a unified computational framework for sequence-based statistical inference, and an serious test of the power of emerging evolution-based protein design approaches to understand and engineer protein molecules.

项目总结：进化构建的蛋白质具有显著的特征组合。它们可以自发折叠，进行困难的化学反应，但也对干扰很强，能够适应适应的条件波动。近年来，基于序列的统计模型提供了具体的模型来说明所有这些特性编码在蛋白质的氨基酸序列中。在这里，我们提出了一种数据驱动的、基于进化的设计(EBD)过程，通过这里概述的发展，可以解决蛋白质中的几个基本问题机制和进化。我们将统一和优化EBD的方法，然后应用它(1)来量化蛋白质家族的功能序列空间，(2)解析蛋白质的并列同源和同源同源的约束以及(3)了解酶的底物专一性如何通过一个逐步的过程来适应。变异和选择。这项工作得到了初步数据的广泛支持，并得到了新的统计推断、基因合成和高通量功能分析技术，包括体外和体内活着。结果将是一个统一的计算框架，用于基于序列的统计推断，并对新出现的基于进化的蛋白质设计方法理解和工程能力的严峻考验蛋白质分子。