Machine learning approaches for faster discovery and adaptation of enzymes for difficult chemical reactions (MacBioSyn). Part I: providing solutions for regioselective oxygenations by 2OGD oxidases

机器学习方法可更快地发现和适应困难化学反应的酶 (MacBioSyn)。

• 批准号: 497207454
• 负责人: Dr. Mehdi Davari Dolatabadi, Ph.D.
• 金额: --
• 依托单位: Abteilung Natur- und Wirkstoffchemie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497207454?language=en
• 关键词: Machine; learning; approaches; faster; discovery

Biocatalytic synthesis of chemicals is considered a keystone for future green and sustainable chemistry. It is particularly highlighted in combination with digital transformation (Green Deal) by the European Commission. However, its power is far from being realized today in industry, mainly because of the limited activity or diversity of accessible enzymes. 2-oxoglutarate-dependent (2OGD) proteins are an under-researched family of enzymes which catalyze “tricky” oxidative reactions (e.g., oxyfunctionalization of non-activated carbons, demethylations), which are challenging or cannot be performed using traditional chemosynthesis. Thus, 2OGD proteins have the high potential to revolutionize the industry as a regio- and product-specific “alternative to chemistry”. Identifying new representatives of this large family having e.g. increased substrate scope can offer a new range of biocatalytic routes to e.g. natural products. However, a common challenge for enzyme development is the prediction of activity by exploring the enormous biodiversity through genome mining. Machine learning (ML) can capitalize on large and diverse enzyme datasets to predict function and activity, and explore the biodiversity to identify advanced biocatalysts. Additionally, ML methods comprise can optimize multiple protein traits simultaneously and to navigate sequence and chemical space efficiently.In the MacBioSyn project, we aim to develop (a general, high-throughput (HT)) ML-based framework (deep learning, active learning, reinforcement learning) that predicts the activity of enzymes and their substrate / reaction scope. We will implement a new in silico framework for the analysis of enzyme sequences/substrates pairs based on ML models trained on screening results by a synergistic approach, combining the interdisciplinary expertise of computational design / modeling (Davari) with HT enzyme characterization (Dippe/Wessjohann). To establish this platform, we will focus on 2OGD enzymes as proof-of-concept biocatalysts. The critical challenge that appears through machine learning projects is the dataset size available for training to establish consistent and reliable statistical modeling. Therefore, MacBioSyn aims at generating a large dataset by HT screening (> 1500 enzymes) of the superfamily’s biodiversity. Conversion of 30 substrates covering various structures will generate representative data to train our algorithms in an iterative process. In essence, our framework will provide a solution for activity / substrate scope prediction for biocatalyst discovery in general. The synergistic approach will provide methodologies that enable the power of ML methods to accelerate the discovery of improved enzymes, i. e. how biocatalytic reactions (here oxyfunctionalizations) are developed. The new fundamental design principles learned for 2OGD enzymes will broaden their applications in the biocatalytic production of valuable natural products and beyond.

生物催化合成化学品被认为是未来绿色和可持续化学的重点。欧盟委员会将其与数字化转型（绿色交易）相结合。然而，它的力量在今天的工业中还远远没有实现，主要是因为可获得的酶的活性或多样性有限。2-酮戊二酸依赖性（2 OGD）蛋白是一种研究不足的酶家族，其催化“棘手的”氧化反应（例如，非活性炭的氧官能化、去甲基化），这是具有挑战性的或不能使用传统化学合成进行。因此，2 OGD蛋白具有作为区域和产品特异性“化学替代品”彻底改变工业的高潜力。鉴定具有例如增加的底物范围的该大家族的新代表可以提供一系列新的生物催化途径，例如天然产物。然而，酶开发的一个共同挑战是通过基因组挖掘探索巨大的生物多样性来预测活性。机器学习（ML）可以利用大型和多样化的酶数据集来预测功能和活性，并探索生物多样性以识别先进的生物催化剂。此外，ML方法可以同时优化多个蛋白质性状，并有效地导航序列和化学空间。在MacBioSyn项目中，我们的目标是开发（通用，高通量（HT））基于ML的框架（深度学习，主动学习，强化学习），预测酶的活性及其底物/反应范围。我们将实施一个新的计算机模拟框架，用于分析基于ML模型的酶序列/底物对，该模型通过协同方法对筛选结果进行训练，将计算设计/建模（Davari）的跨学科专业知识与HT酶表征（Dippe/Wessjohann）相结合。为了建立这个平台，我们将专注于2 OGD酶作为概念验证生物催化剂。通过机器学习项目出现的关键挑战是可用于训练的数据集大小，以建立一致和可靠的统计建模。因此，MacBioSyn旨在通过HT筛选（> 1500种酶）超家族的生物多样性来生成大型数据集。覆盖各种结构的30个衬底的转换将生成代表性数据，以在迭代过程中训练我们的算法。从本质上讲，我们的框架将提供一个解决方案的活性/底物范围预测的生物催化剂的发现一般。协同方法将提供方法论，使ML方法的力量能够加速发现改进的酶，即。e.生物催化反应（这里是氧化功能化）是如何发展的。从2 OGD酶中学到的新的基本设计原理将拓宽它们在有价值的天然产物的生物催化生产中的应用。