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来快速 设计和测试抗体的目的有很多，包括癌症和 传染病免疫疗法。我们将在现有的基础上进行改进 提供时间、成本和人文效益的抗体设计方法 过度免疫的动物方法和大大提高目前的力量 使用随机设计的合成方法。为了实现这一目标，我们将 使用最近的数据显示数百万个经过计算设计的抗体序列 可用的技术，以高通量格式测试显示的抗体，请访问 低成本，并使用得到的测试数据来训练分子动力学和 机器学习方法，以生成新的测试序列。基于我们的 测试数据我们的计算方法将识别出具有理想的 靶向结合特性和治疗效果。我们将实现这些目标 有三个具体目标的目标。我们将开发一种新的方法来整合 分子动力学和机器学习使用控制目标和已知 受体序列，以改进我们的受体泛化和建模方法 根据观测数据进行更新(目标1)。我们将设计一个迭代框架 旨在能够在最低限度内识别高效抗体 许多实验中，我们的方法自动提出了有希望的 后续化验中要分析的抗体序列(目标2)。我们将聘用 几轮自动合成设计、亲和力测试和模型改进，以 产生高度靶标特异性抗体。(目标3)。 好了！

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). !