Next-Generation Protein Engineering: Machine Learning for Enzyme Engineering

酶是催化反应的蛋白质。它们被用于各种应用，如清洁污渍的洗涤剂、检测血糖水平的传感器、生产高果糖玉米糖浆等工业过程。利用一种名为定向进化(DE)的方法，可以对酶进行改造以提高其性能。这是一个重复的基因序列突变和酶筛选的过程，以确定突变序列引起的性能变化。DE实验的一个副产品是大量未使用的数据。该项目将把机器学习(ML)融入到远程教育工作流程中。这些数据和其他数据将训练ML算法识别并最终预测有助于产生所需酶活性的蛋白质结构。其目标是能够以更低的成本更快地制造出新的、有用的酶。生命的多样性在很大程度上源于蛋白质的进化和适应能力。20种蛋白质产生的氨基酸的排列使得可能的蛋白质序列的数量超过了天文数字，其中绝大多数没有折叠或编码有用的功能。我们建议机器学习(ML)模型可以使用从定向进化(DE)实验中获得的信息来提高搜索这个空间寻找功能蛋白质的效率。虽然像DE这样的技术隐含地依赖于蛋白质序列空间功能景观的底层结构，但明确地建模这种结构将允许更有效的搜索算法。我们最近展示了一种数据驱动的ML方法来指导DE实验，该方法解释了蛋白质突变的上位性质，并使多个有益突变能够合并到单一一代突变和筛选中。我们的目标是进一步开发这一工作流程，以同时处理多个任务，特别是预测跨多个底物的酶活性。为了实现这一点，我们建议将关于多个底物的信息整合到ML引导的定向进化中，并使用针对不同预测任务开发的蛋白质和底物的有效编码。然而，这些编码并未针对ML进行优化以协同工作。因此，我们建议开发新的编码，以内聚和协同的方式描述酶系统的组成部分。最后，虽然定向进化已经成功地使酶适应了人类的应用，但这一过程目前需要专业知识和每一项工程任务的密集试错。对于专家知识的需求在应对为远程教育寻找开始活动这一艰巨挑战时最为明显。利用ML，我们将建立一个模型，基于已知的P450酶的天然底物(S)，预测这些酶的非天然卡宾/硝烯转移活性。该奖项由化学、生物工程、环境和运输系统部门的细胞和生化工程项目以及分子和细胞生物科学部门的系统和合成生物学项目共同设立。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Enzymes are proteins that catalyze reactions. They are used in various applications such as detergents to clean stains, sensors to detect blood sugar levels, industrial processes such as production of high fructose corn syrup. Enzymes can be engineered to improve their performance using a method called directed evolution (DE). This is a process of repeated gene sequence mutation and enzyme screening to determine the change in performance caused by the mutated sequences. A side-product of DE experiments is an abundance of unused data. This project will incorporate machine learning (ML) into the DE workflow. This and other data will train the ML algorithm to recognize and ultimately predict protein structures that are useful in creating the enzyme activity desired. The objective is to be able to make novel and useful enzymes more rapidly and at lower cost.The diversity of life arises in great measure from the ability of proteins to evolve and adapt. The permutations of the 20 proteinogenic amino acids allow for supra-astronomical numbers of possible protein sequences, the vast majority of which do not fold or encode a useful function. We propose that machine learning (ML) models can use information gained from directed evolution (DE) experiments to improve the efficiency of searching this space for functional proteins. While techniques such as DE implicitly rely on an underlying structure of the functional landscape of protein sequence space, explicitly modeling this structure would allow for far more efficient search algorithms. We recently demonstrated a data-driven, ML approach to guiding DE experiments which accounts for the epistatic nature of protein mutations and enables multiple beneficial mutations to be incorporated in a single generation of mutation and screening. We aim to further develop this workflow to address multiple tasks simultaneously, specifically to predict enzyme activity across multiple substrates. To accomplish this, we propose to incorporate information about multiple substrates into ML-guided directed evolution and use validated encodings for proteins and substrates developed for separate predictive tasks. However, these encodings are not optimized to work together for ML. We therefore propose to develop new encodings that describe the components of an enzymatic system in a cohesive and synergistic manner. Finally, while directed evolution has successfully adapted enzymes for human applications, this process currently requires expert knowledge and intensive trial-and-error for each engineering task. The requirement for expert knowledge is clearest when approaching the formidable challenge of finding starting activity for DE. Using ML, we will build a model that can predict the non-natural carbene/nitrene transfer activities of P450s against target substrates, based on what is known of the natural substrate(s) of these enzymes.This award is cofounded by the Cellular and Biochemical Engineering Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems and the Systems and Synthetic Biology Program in the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.