I-Corps: Structure Based Machine Learning Aided Protein Engineering

I-Corps：基于结构的机器学习辅助蛋白质工程

• 批准号: 1929560
• 负责人: Andrew Ellington
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929560&HistoricalAwards=false
• 关键词: Corps; Structure; Based; Machine; Learning

The broader impact/commercial potential of this I-Corps project spans chemical manufacturing, pharmaceutical development, and agricultural biotechnology. This technology helps protein engineers take a candidate protein and make it more robust to chemical or environmental perturbations. In one application, this technology could be used to extend the half-life of pharmaceutical candidates during development. In another application, it could enable substituting toxic and wasteful chemistries with suitable enzymatic chemistries. These algorithms may help drive new green chemistries presently inaccessible because although the necessary enzymes can perform a desired catalysis, they cannot stay folded long enough for commercial use. In a third application, the technology can help improve the shelf life of these critical drugs, thereby expanding the population they might benefit.This I-Corps project leverages machine learning to accelerate protein engineering. Convolutional neural networks have recently become the preferred artificial intelligence (AI) solution to a number of computer vision challenges, yet their biological applications remain scarce. Proteins, strings of folded amino acids that drive most biological processes, are exponentially being crystallized to solve their three-dimensional structure. This project combines these two resources by training a 3D convolutional neural network that characterizes chemical environments unique to each of the 20 amino acids. The same neural network can then predict the amino acid best fitting a given environment.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.

这个I-Corps项目的更广泛的影响/商业潜力涵盖化学制造，制药开发和农业生物技术。这项技术可以帮助蛋白质工程师获得候选蛋白质，使其对化学或环境扰动更加稳健。在一个应用中，该技术可用于延长开发期间候选药物的半衰期。在另一个应用中，它可以用合适的酶化学物质取代有毒和浪费的化学物质。这些算法可能有助于推动新的绿色化学目前无法实现，因为虽然必要的酶可以进行所需的催化，但它们不能保持足够长的折叠时间用于商业用途。 在第三个应用中，该技术可以帮助延长这些关键药物的保质期，从而扩大它们可能受益的人群。这个I-Corps项目利用机器学习加速蛋白质工程。卷积神经网络最近已成为许多计算机视觉挑战的首选人工智能（AI）解决方案，但它们的生物应用仍然很少。蛋白质，驱动大多数生物过程的折叠氨基酸串，正在以指数方式结晶以解决其三维结构。该项目通过训练一个3D卷积神经网络来结合这两种资源，该网络表征了20种氨基酸中每一种所特有的化学环境。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。