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

约翰霍普金斯大学获得一项奖励，通过结合进化遗传学、网络模型和蛋白质序列分析，开发一种新的方法来模拟高阶突变相互作用。研究单个突变的效应如何结合联合收割机来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联合收割机非相加地结合。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），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呈指数增加。如果一个模型？的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察的数量。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。具体而言，目前缺乏蛋白质突变如何结合联合收割机作为蛋白质向新功能进化路径中的步骤的详细建模。在这个项目中，将进行蛋白质序列的系统进化分析，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连接在一起。该建模框架大大减少了建模参数的数量，并以易于处理的方式结合了高阶交互。由于网络中的节点对应于突变的蛋白质位置，因此任何复杂的突变组合都可以表示为通过网络的路径。使用图论，将开发度量来评估网络内的路径重要性与蛋白质对其环境的成功适应之间的关系。这项工作的重点是一个简单的生物模型系统-进化的抗生素耐药性的酶TEM β-内酰胺酶在大肠杆菌细菌。在这个系统中，新功能的进化（抗生素抗性）与整个生物体水平的生存（细菌生长）之间存在直接相关性。计算预测将通过将感兴趣的突变引入细菌培养物并测量暴露于给定抗生素下的存活率来系统地测试。这项工作促进了计算科学和生物学社区之间的密切互动：它结合了蛋白质突变的计算/统计建模和微生物系统中应用进化遗传学的专业知识。这项工作的更广泛用途将是预测临床相关蛋白质中耐药性的出现。它也将有很大的效用，蛋白质工程师谁寻求设计蛋白质与新的或改进的功能。此外，它将有助于设计由细菌或病毒驱动的疾病的治疗方案，其中耐药性的演变是常见的。该项目的教育目标包括为项目调查人员任教的大学的本科生和研究生开设新的课程，并向科学和工程专业人数不足的少数民族学生开展外联活动。有关该项目的更多信息，请访问：http://karchinlab.org/。

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/.