Data-driven, evolution-based design of proteins

项目摘要： 进化造就了具有多种特征的蛋白质。它们可以自发折叠， 进行困难的化学反应，但也对扰动具有鲁棒性，并能够适应适应条件 波动近年来，基于序列的统计模型已经为所有这些 蛋白质的氨基酸序列中编码了这些特性。在这里，我们提出了一个数据驱动的，基于进化的 设计（EBD）过程，与这里概述的发展，可以解决蛋白质中的几个基本问题， 机制与演化我们将统一和优化EBD的方法，然后应用它（1）量化 蛋白质家族的功能序列空间，（2）解析蛋白质的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降解过程进行适应。 变异和选择这项工作得到了初步数据的广泛支持， 用于统计推断、基因合成和高通量功能测定的技术，包括体外和体内 vivo.结果将是一个统一的计算框架，用于基于序列的统计推断， 对新兴的基于进化的蛋白质设计方法的理解和工程能力的严峻考验 蛋白质分子

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.