III: Small: Collaborative Research: Analysis of Multi-Dimensional Protein Design Spaces with Pareto Optimization of Experimental Designs

III：小：协作研究：利用实验设计的帕累托优化分析多维蛋白质设计空间

• 批准号: 1018110
• 负责人: Alan Friedman
• 金额: $ 16.59万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018110&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Analysis

In developing variants of natural proteins with improved properties and activities, protein engineers are confronted with large, complex design spaces. The degrees of freedom for producing variants mirror nature but can be specifically targeted experimentally, choosing parent proteins, replacements for some amino acids (site-directed mutation), and locations for crossing over between parents (site-directed recombination). A set of choices, constituting a design, can be evaluated by multiple disparate criteria, including consistency with evolutionary information, energetic favorability with respect to a three-dimensional structure, and incorporation of specific characteristics distinguishing functional subclasses. Unfortunately, the different evaluation metrics may be complementary or even contradictory, and the prior information on which they are based is incomplete, so that the metrics are only more or less accurate in predicting the real-life quality of the designs.The overall goal of this project is to develop efficient methods to characterize complex protein design spaces and optimize high-quality designs for experimental evaluation. A combinatorial protein engineering approach will be pursued, experimentally constructing a library of related variants and assaying them for properties of interest. Potential scores will evaluate a possible library (without explicitly enumerating its members) with respect to prior information from sequence, structure, and functional subclass. To account for disparate evaluation metrics, design algorithms will focus on theidentification of Pareto optimal designs, those for which no other design is as good or better with respect to all desired criteria. To account for incomplete prior information, design algorithms will trade off between exploitation of the prior information and broader exploration of the design space, seeking to identify a diverse set of designs, each with a diverse set of variants. Markov Chain Monte Carlo sampling algorithms will characterize the overall design space by generating choices for the degrees of freedom and evaluating the designs with the potential scores, using the scores and diversity metrics to appropriately explore the space. Exact algorithms will more precisely focus on regions of interest, dividing and conquering the design space and employing combinatorial optimization algorithms to identify Pareto optimal designs.The design space approach provides a powerful new mechanism to address protein engineering applications, enabling the engineer to explicitly evaluate and optimize for trade-offs among important criteria and considerations. Interactive tools will help engineers navigate through the regions of interest, visualize designs and perform "what-if" analyses, and compare and contrast Pareto optimal designs. A design space repository will enable sharing of analyses and underlying data. The tools and repository will support protein engineering for a range of activities in the national interest, including biosensors, production of novel biological therapeutics and novel enzymes for green chemical synthesis, energy extraction, and bioremediation. As part of the project, the mechanism will be put to use in the engineering of soluble and robust cytochrome P450s that employ the inexpensive and non-toxic hydrogen peroxide to hydroxylate steroids and multi-ring compounds that mimic estrogenic (feminizing) steroids in the environment without the need for living cells or protein cofactors. Such enzymes would be valuable as tools for chemical synthesis, waste treatment, and bioremediation.This project provides an ideal venue to impart cross-disciplinary training to students by illustrating how computational techniques can be fruitfully integrated with experimentation in answering important biological questions. Aspects of the project will be used in both undergraduate and graduate courses, from an introductory biology course to an advanced bioinformatics course. The project itself will provide the opportunity for inter-disciplinary research training for graduates and undergraduates, including those from underrepresented groups.

在开发具有改进的性质和活性的天然蛋白质变体时，蛋白质工程师面临着巨大而复杂的设计空间。 产生变体的自由度反映了自然，但可以通过实验进行特定靶向，选择亲本蛋白质，替换某些氨基酸（定点突变）以及亲本之间的交叉位置（定点重组）。 一组选择，构成一个设计，可以通过多个不同的标准进行评估，包括与进化信息的一致性，相对于三维结构的能量可重复性，以及区分功能子类的特定特征的结合。不幸的是，不同的评价指标可能是互补的，甚至是矛盾的，他们所基于的先验信息是不完整的，所以这些指标只是或多或少准确地预测现实生活中的质量的designs.The总体目标是开发有效的方法来表征复杂的蛋白质设计空间和优化高质量的设计实验评估。 一个组合的蛋白质工程方法将被追求，实验性地构建一个相关变体的库，并测定它们的感兴趣的特性。 潜在评分将评估可能的文库（没有明确列举其成员）关于来自序列、结构和功能亚类的先前信息。 考虑到不同的评估指标，设计算法将集中在识别帕累托最优设计，没有其他设计是好的或更好的所有期望的标准。 考虑到不完整的先验信息，设计算法将在利用先验信息和更广泛的设计空间探索之间进行权衡，寻求识别一组不同的设计，每个设计都有一组不同的变体。 马尔可夫链蒙特卡罗抽样算法将通过生成自由度的选择并使用潜在分数评估设计来表征整体设计空间，使用分数和多样性度量来适当地探索空间。 精确算法将更精确地关注感兴趣的区域，划分和征服设计空间，并采用组合优化算法来识别Pareto最优设计。设计空间方法为解决蛋白质工程应用提供了一种强大的新机制，使工程师能够明确地评估和优化重要标准和考虑因素之间的权衡。 交互式工具将帮助工程师浏览感兴趣的区域，可视化设计和执行“假设”分析，并比较和对比帕累托最优设计。设计空间储存库将使分析和基础数据共享成为可能。 该工具和储存库将支持蛋白质工程的一系列活动在国家利益，包括生物传感器，生产新的生物疗法和新的酶的绿色化学合成，能源提取和生物修复。 作为该项目的一部分，该机制将用于可溶性和稳健的细胞色素P450的工程设计，该细胞色素P450采用廉价且无毒的过氧化氢来羟基化类固醇和多环化合物，这些化合物在环境中模拟雌激素（女性化）类固醇，而不需要活细胞或蛋白质辅因子。 这类酶作为化学合成、废物处理和生物修复的工具将是有价值的。本项目通过说明计算技术如何有效地与实验结合来回答重要的生物学问题，为学生提供了一个理想的场所，以传授跨学科的培训。 该项目的各个方面将用于本科生和研究生课程，从生物学入门课程到高级生物信息学课程。 该项目本身将为毕业生和本科生，包括代表性不足的群体提供跨学科研究培训的机会。