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

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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). !