Deep learning of protein sequence design of protein–protein interactions

MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence–structure relationships become more feasible. Within this space, the amino acid sequence design of protein–protein interactions is still a rather challenging subproblem with very low success rates—yet, it is central to most biological processes. RESULTS: We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein–protein interactions including antibody–antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein–protein interactions. AVAILABILITY AND IMPLEMENTATION: The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.