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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是改善，加速和减轻各种行业的蛋白质工程成本，包括工业生物催化剂，生物制造，食品技术和治疗剂。如今，后期蛋白质工程代表了主要时间，劳动力和金融瓶颈。由于现实世界的翻译是晚期开发的重点，因此测定更大程度地反映了其最终用途的应用，因此必然需要更多的时间，劳动力和资本。这排除了许多变体在此阶段被筛选。在这些后期的这些晚期阶段的失败是昂贵的，通常是由于早期高吞吐量筛查的测试条件的环境参数变化而导致的。基于最小数据的蛋白质变异的准确预测，但在最终用途条件下具有很高功能的可能性是一个至关重要的未满足需求。拟议的项目将证明利用机器学习模型的可行性，对原始蛋白质序列，诱变数据和自然序列的培训培训，以预测potie of Protine of Protine of Protiention（POI）的高度功能（POI）。迄今为止，这种称为零拍学习的方法尚未应用于蛋白质工程。为了实现这一目标，大规模的语言模型将接受来自公共和私人数据库的近50亿个未标记的蛋白质序列和诱变数据集的培训。然后可以将这种通用知识模型与特定于应用的顶部模型融合，该模型与自然环境的自然序列（不同于POI不同）融合在一起。假设该训练以将模型与哪个序列特征在一般意义上以及在特定环境条件下（例如高温，高盐度等）提高蛋白质功能的概念。为了证明这种方法的可行性和实用性，该模型将在虚拟定向的进化实验中使用，以优化两种治疗性相关的酶，在非本地环境中为功能进行了优化，并在体外评估了该功能。该奖项反映了NSF的法定任务，并通过评估该基金会的智力效果，并通过评估了基金会的范围和广泛的影响。