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bioRxiv · 10.1101/2022.01.28.478262

Deep learning of Protein Sequence Design of Protein-protein Interactions

Syrlybaeva, R.·Strauch, E.-M.
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Abstract

MotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes. ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions. AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign Supplementary informationSupplementary data are available at Bioinformatics online.

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Syrlybaeva, R., Strauch, E.-M.. 2022-01-29. Deep learning of Protein Sequence Design of Protein-protein Interactions. https://doi.org/10.1101/2022.01.28.478262

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