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

在开发具有改进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。产生变异的自由度反映了自然性质，但可以通过实验进行特异性靶向，选择亲本蛋白，替换某些氨基酸（位点定向突变），以及在亲本之间杂交的位置（位点定向重组）。构成设计的一组选择可以通过多种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有利性，以及区分功能子类的特定特征的结合。不幸的是，不同的评估指标可能是互补的，甚至是矛盾的，并且它们所基于的先验信息是不完整的，因此这些指标在预测设计的实际质量方面或多或少是准确的。该项目的总体目标是开发有效的方法来表征复杂蛋白质设计空间，并优化实验评估的高质量设计。一种组合蛋白工程方法将被采用，通过实验构建一个相关变异库，并分析它们感兴趣的特性。潜在分数将根据序列、结构和功能子类的先验信息评估一个可能的库（不显式枚举其成员）。考虑到不同的评估指标，设计算法将专注于帕累托最优设计的识别，这些设计在所有期望的标准方面都没有其他设计那么好或更好。为了考虑不完整的先验信息，设计算法将在利用先验信息和更广泛地探索设计空间之间进行权衡，寻求识别不同的设计集，每个设计都有不同的变体集。马尔可夫链蒙特卡罗采样算法将通过生成自由度的选择和评估设计的潜在分数来表征整个设计空间，使用分数和多样性指标来适当地探索空间。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帕累托最优设计。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，可视化设计并执行“假设”分析，并比较和对比帕累托最优设计。设计空间存储库将支持分析和底层数据的共享。这些工具和存储库将支持一系列符合国家利益的蛋白质工程活动，包括生物传感器、用于绿色化学合成的新型生物疗法和新型酶的生产、能源提取和生物修复。作为该项目的一部分，该机制将被用于可溶性和健壮的细胞色素p450的工程，这些细胞色素p450使用廉价无毒的过氧化氢来羟基化类固醇和多环化合物，这些化合物可以模拟环境中的雌激素（雌性化）类固醇，而不需要活细胞或蛋白质辅助因子。这种酶作为化学合成、废物处理和生物修复的工具是有价值的。这个项目提供了一个理想的场所，通过说明如何将计算技术与回答重要生物学问题的实验有效地结合起来，向学生传授跨学科训练。该项目的各个方面将用于本科和研究生课程，从入门生物学课程到高级生物信息学课程。该项目本身将为研究生和本科生提供跨学科研究培训的机会，包括来自代表性不足群体的学生。

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.