ABI Innovation: Predicting the combined impact of multiple mutations on protein functional adaptation

ABI 创新：预测多种突变对蛋白质功能适应的综合影响

• 批准号: 1262435
• 负责人: Rachel Karchin
• 金额: $ 69.29万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262435&HistoricalAwards=false
• 关键词: ABI; Innovation; Predicting; combined; impact

An award is made to Johns Hopkins University to develop a new method to model higher order mutation interactions, by combining evolutionary genetics, network models, and protein sequence analysis. The study of how the effects of individual mutations combine to impact the overall function of a protein is a key question in evolutionary biology. It is also a very difficult question, as the effects multiple protein mutations can combine non-additively. In this case, higher order interactions (beyond pairwise) between mutations need to be considered, and the number of interactions increases exponentially as a function of the total number of protein mutations involved. Therefore, if a model?s parameters are mutation interactions, these parameters will far exceed the number of possible experimental observations needed to determine them. As a result, existing bioinformatics methods are limited in their exploration of higher-order evolutionary interactions. Specifically, detailed modeling of how protein mutations combine as steps in the evolutionary path of a protein toward new function is currently lacking. In this project, systematic evolutionary analysis of protein sequence will be conducted to ensure that only residue positions most relevant for evolving new functions are considered. Next, mutated positions will be linked together in a network model. This modeling framework dramatically reduces the number of modeling parameters and incorporates higher order interactions in a tractable fashion. Because nodes in the network correspond to mutated protein positions, any complex combination of mutations can be represented as a path through the network. Using graph theory, metrics will be developed to assess the relationship between path importance within the network and the successful adaptation of a protein to its environment. This work focuses on a simple biological model system -- the evolution antibiotic resistance by the enzyme TEM beta-lactamase in Escherichia coli bacteria. In this system, there is a direct correlation between the evolution of a new function (antibiotic resistance) and survival at the level of the whole organism (bacterial growth). Computational predictions will be systematically tested by introducing mutations of interest into bacterial cultures and measuring survival under exposure to a given antibiotic.This work promotes close interaction between the computational sciences and biology communities: It combines expertise in computational/statistical modeling of mutations in proteins and applied evolutionary genetics in microbial systems. The broader use of this work will be to anticipate the emergence of drug resistance in clinically relevant proteins. It will also have great utility for protein engineers who seek to design proteins with new or improved functions. Furthermore, it will contribute to the design of therapeutic regimens for diseases driven by bacteria or viruses, in which the evolution of drug resistance is commonplace. The educational goals of the project include new course components for undergraduates and graduates at the universities where the project investigators teach and outreach to underrepresented minority students in science and engineering. More information about the project can be found at: http://karchinlab.org/.

约翰霍普金斯大学被授予一项新的方法，通过结合进化遗传学、网络模型和蛋白质序列分析来对高阶突变相互作用进行建模。研究个体突变的影响如何组合在一起影响蛋白质的整体功能是进化生物学中的一个关键问题。这也是一个非常困难的问题，因为多个蛋白质突变的影响可以非相加地结合在一起。在这种情况下，需要考虑突变之间的高阶相互作用(超出成对的)，并且相互作用的数量作为所涉及的蛋白质突变总数的函数指数地增加。因此，如果一个模型的S参数是突变相互作用，这些参数将远远超过确定它们所需的可能的实验观测数量。因此，现有的生物信息学方法在探索高阶进化相互作用方面受到限制。具体地说，目前还缺乏关于蛋白质突变如何结合为蛋白质进化路径中新功能的步骤的详细建模。在这个项目中，将对蛋白质序列进行系统的进化分析，以确保只考虑与进化新功能最相关的残基位置。接下来，突变的位置将在网络模型中链接在一起。该建模框架极大地减少了建模参数的数量，并以一种易于处理的方式合并了更高阶的交互。因为网络中的节点对应于突变的蛋白质位置，所以任何复杂的突变组合都可以表示为网络中的一条路径。利用图论，将开发衡量标准，以评估网络中路径重要性与蛋白质成功适应环境之间的关系。本工作重点研究了一个简单的生物模型系统--在大肠杆菌中利用透射电子显微镜β-内酰胺酶进行抗生素耐药性的进化。在这个系统中，一种新功能的进化(抗生素耐药性)与整个有机体水平上的生存(细菌生长)之间存在着直接的关联。通过将感兴趣的突变引入细菌培养并测量在给定抗生素下的存活率，将系统地测试计算预测。这项工作促进了计算科学和生物界之间的密切互动：它结合了蛋白质突变的计算/统计建模方面的专业知识和微生物系统中的应用进化遗传学。这项工作的更广泛用途将是预测临床相关蛋白质中出现的耐药性。对于寻求设计具有新的或改进的功能的蛋白质的蛋白质工程师来说，它也将具有很大的实用价值。此外，它还将有助于设计由细菌或病毒驱动的疾病的治疗方案，在这些疾病中，耐药性的演变是司空见惯的。该项目的教育目标包括在项目调查员任教的大学为本科生和毕业生提供新的课程内容，并向科学和工程专业中代表性不足的少数族裔学生提供服务。有关该项目的更多信息，请访问：http://karchinlab.org/.