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: 1017231
• 负责人: Christopher Bailey-Kellogg
• 金额: $ 33.18万
• 依托单位: Dartmouth College
• 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=1017231&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.

在开发具有改进特性和活性的天然蛋白质变体时，蛋白质工程师面临着大型，复杂的设计空间。 产生变体的自由度反映了镜像，但可以特异性地针对实验，选择母蛋白，替代某些氨基酸（位于定位的突变）以及在父母之间越过的位置（现场定向重组）。 可以通过多个不同的标准来评估一组构成设计的选择，包括与进化信息的一致性，相对于三维结构的能量优惠性以及区分功能亚类的特定特征的结合。不幸的是，不同的评估指标可能是互补的，甚至是矛盾的，并且它们所基于的先前信息是不完整的，因此指标在预测设计的现实生活质量时或多或少是准确的。该项目的总体目标是为了开发有效的复杂蛋白质设计空间并优化高质量的实验性评估方法，以开发有效的方法。 将采用一种组合蛋白工程方法，通过实验来构建相关变体的库，并分析它们的感兴趣属性。 潜在的分数将评估有关从序列，结构和功能子类的先验信息的可能库（不明确列举其成员）。 为了说明不同的评估指标，设计算法将集中在帕累托最佳设计的识别上，而这些设计在所有所需的标准方面都没有其他设计是更好或更好的。 为了说明不完整的先前信息，设计算法将在对先前信息的开发和对设计空间的更广泛探索之间进行权衡，以寻求确定各种各样的设计，每种设计都有各种各样的变体。 Markov Chain Monte Carlo采样算法将使用分数和多样性指标来适当地探索该空间，从而通过为自由程度生成自由度的选择来表征整个设计空间。 精确的算法将更加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的最佳设计。设计空间方法为蛋白质工程应用提供了强大的新机制，以解决蛋白质工程应用程序，使工程师能够在重要的机构中显式地评估和优化重要的折算和优化。 交互式工具将帮助工程师浏览感兴趣的区域，可视化设计并执行“ What-if”分析，并比较和对比帕累托最佳设计。设计空间存储库将能够共享分析和基础数据。 这些工具和存储库将支持针对国家利益的一系列活动的蛋白质工程，包括生物传感器，新型生物学疗法的生产以及用于绿色化学合成，能量提取和生物修复的新型酶。 作为该项目的一部分，该机制将用于用于可溶性和稳健的细胞色素P450的工程，这些P450s使用廉价和无毒的过氧化氧化氧化氧化氢和多环类固醇和多环类化合物，这些化合物在环境中模仿雌激素（女性化）类固醇中无需使用活细胞或蛋白质Cofactors或蛋白质cofactors的多环类固醇。 这样的酶将作为化学合成，废物处理和生物修复的工具很有价值。该项目通过说明如何在回答重要的生物学问题时如何将计算技术与实验结合在一起，为学生提供跨学科培训的理想场所。 该项目的各个方面都将在本科和研究生课程中使用，从入门生物学课程到先进的生物信息学课程。 该项目本身将为毕业生和本科生，包括来自代表性不足的群体的跨学科研究培训提供机会。