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：
10626884
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/10626884
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 Energy harvesting Engineering Environment Enzymes Escherichia coli Evolution Foundations Gene Duplication Gene Mutation Genome Geometry Goals Health Homologous Gene 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 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 aminoacid sequence protein folding protein structure rational design 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.

项目概要：进化构建了具有显着特征组合的蛋白质。它们可以自发折叠进行困难的化学反应，但对扰动也具有鲁棒性，并且能够适应适应条件波动。近年来，基于序列的统计模型为所有这些如何进行提供了具体的模型。特性被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，通过此处概述的发展，可以解决蛋白质中的几个基本问题机制和演化。我们将统一和优化 EBD 方法，然后应用它 (1) 来量化蛋白质家族的功能序列空间，（2）解析蛋白质旁系同源物和直系同源物的约束家族，以及（3）了解酶中的底物特异性如何通过逐步的过程进行适应变化和选择。这项工作得到了初步数据的广泛支持，并得到了新的支持体外和体内统计推断、基因合成和高通量功能测定技术体内。结果将是基于序列的统计推断的统一计算框架，以及对新兴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。