SBIR Phase I: Highly resource-efficient protein engineering using machine learning

SBIR 第一阶段：利用机器学习实现高度资源效率的蛋白质工程

• 批准号: 2051603
• 负责人: Surojit Biswas
• 金额: $ 25.6万
• 依托单位: NABLA BIO, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051603&HistoricalAwards=false
• 关键词: SBIR; Phase; Highly; resource; efficient

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve, accelerate, and alleviate costs of protein engineering across diverse industries including industrial biocatalysts, biomanufacturing, food technology, and therapeutics. Today, late-stage protein engineering represents a major time, labor, and financial bottleneck. Since real-world translation is the focus of late-stage development, assays are more reflective of their end-use application and therefore necessarily require more time, labor, and capital. This precludes many variants from being screened at this stage. Failure at these late stages of development is costly, and often results from a change in environmental parameters from test conditions in early high throughput screens. Accurate prediction of protein variants based on minimal data but with high likelihood of function under end-use conditions is a critical unmet need.The proposed project will demonstrate the feasibility of leveraging a machine learning model, trained on raw protein sequences, mutagenesis datasets and natural sequence- function pairs, to predict highly functional variants of a protein of interest (POI) without sequence-function datasets specific to the selected POI and application. Such an approach, known as zero-shot learning, has not been applied to protein engineering to date. To achieve this, a large-scale language model will be trained with almost 5 billion curated unlabeled protein sequences from public and private databases and a collection of mutagenesis datasets. This general knowledge model can then be fused with an application-specific top model derived from natural sequences (distinct from the POI) paired with parameters of their natural environments. This training is hypothesized to imbue the model with a notion of which sequence features improve protein function in a general sense, and under particular environmental conditions (e.g., high temperature, high salinity, etc.). To demonstrate the feasibility and utility of this approach, the model will be used in virtual directed evolution experiments to optimize two therapeutically relevant enzymes, optimized for function in non-native environments, and assessed for this function in vitro.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是改善、加速和降低不同行业的蛋白质工程成本，包括工业生物催化剂、生物制造、食品技术和治疗学。如今，后期蛋白质工程是一个主要的时间、劳动力和财务瓶颈。由于现实世界的翻译是后期开发的重点，因此分析更能反映其最终用途应用，因此必然需要更多的时间、劳动力和资本。这使得许多变种无法在此阶段进行筛选。开发后期阶段的失败代价高昂，并且通常是由于早期高通量筛选中测试条件的环境参数变化造成的。基于最少的数据准确预测蛋白质变体，但在最终使用条件下具有很高的功能可能性，这是一个未满足的关键需求。拟议的项目将证明利用机器学习模型的可行性，该模型在原始蛋白质序列、诱变数据集和自然数据上进行训练。序列-功能对，无需特定于所选 POI 和应用的序列-功能数据集即可预测感兴趣的蛋白质 (POI) 的高功能变体。这种被称为零样本学习的方法迄今为止尚未应用于蛋白质工程。为了实现这一目标，将使用来自公共和私人数据库的近 50 亿个精选的未标记蛋白质序列以及一系列诱变数据集来训练大规模语言模型。然后，可以将该通用知识模型与源自自然序列（与 POI 不同）及其自然环境参数配对的特定于应用程序的顶级模型融合。假设这种训练是为了向模型灌输这样的概念：在一般意义上，在特定的环境条件下（例如高温、高盐度等），哪些序列特征可以改善蛋白质功能。为了证明这种方法的可行性和实用性，该模型将用于虚拟定向进化实验，以优化两种治疗相关的酶，优化非天然环境中的功能，并在体外评估该功能。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。