Development of data-driven sampling and its application to protein design and variant prediction

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In previous work, we have successfully developed machine-learning based methods to solve various prediction problems in structural biology, including predicting changes to stability and binding affinity upon mutations. We have also developed a new protein engineering platform tightly coupling computational design with high-throughput screening. However, in these approaches, we still made use of conventional, physics inspired modeling methods. Here, we plan to develop more data-driven methods for protein modeling.