Deep learning of Protein Sequence Design of Protein-protein Interactions.

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Abstract

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.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.The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign.Supplementary data is available at Bioinformatics online.© The Author(s) 2022. Published by Oxford University Press.