Deep learning based antibody design using high-throughput affinity testing of synthetic sequences

项目摘要 我们将开发和应用一种新的高通量方法， 设计和测试抗体用于各种目的，包括癌症和 传染病免疫治疗我们将改进现有的 通过提供时间、成本和人性化的好处来设计抗体的方法 过度免疫的动物方法，大大提高了目前的能力， 使用随机设计的合成方法。为了实现这一目标，我们将 展示了数百万个计算设计的抗体序列， 可用的技术，以高通量的形式测试展示的抗体， 低成本，并使用得到的测试数据来训练分子动力学， 机器学习方法来生成用于测试的新序列。基于我们 我们计算方法将识别具有理想 用于靶结合和治疗功效的性质。我们将实现这些目标 有三个具体目标。我们将开发一种新的方法， 分子动力学和机器学习使用控制目标和已知的 受体序列，以完善我们的方法，受体的泛化和模型 根据观测数据进行更新（目标1）。我们将设计一个迭代框架 旨在能够在最小范围内鉴定高效抗体， 实验的数量，其中我们的方法自动提出有前途的 抗体序列，以在随后的测定中进行分析（目的2）。我们会委聘 一轮轮的自动合成设计、亲和力测试和模型改进， 产生高度针对性的抗体。(Aim 3）。 !

Project Summary We will develop and apply a new high-throughput methodology for rapidly designing and testing antibodies for a myriad of purposes, including cancer and infectious disease immunotherapeutics. We will improve upon current approaches for antibody design by providing time, cost, and humane benefits over immunized animal methods and greatly improving the power of present synthetic methods that use randomized designs. To accomplish this, we will display millions of computationally designed antibody sequences using recently available technology, test the displayed antibodies in a high-throughput format at low cost, and use the resulting test data to train molecular dynamics and machine learning methods to generate new sequences for testing. Based on our test data our computational method will identify sequences that have ideal properties for target binding and therapeutic efficacy. We will accomplish these goals with three specific aims. We will develop a new approach to integrated molecular dynamics and machine learning using control targets and known receptor sequences to refine our methods for receptor generalization and model updating from observed data (Aim 1). We will design an iterative framework intended to enable identification of highly effective antibodies within a minimal number of experiments, in which our methods automatically propose promising antibody sequences to profile in subsequent assays (Aim 2). We will employ rounds of automated synthetic design, affinity test, and model improvement to produce highly target-specific antibodies. (Aim 3). !