Learning the shape of protein micro-environments with a holographic convolutional neural network

• URL: http://arxiv.org/abs/2211.02936v1
• Date: Sat, 5 Nov 2022 16:29:15 GMT
• Title: Learning the shape of protein micro-environments with a holographic convolutional neural network
• Authors: Michael N. Pun, Andrew Ivanov, Quinn Bellamy, Zachary Montague, Colin LaMont, Philip Bradley, Jakub Otwinowski, Armita Nourmohammad
• Abstract summary: We introduce Holographic Convolutional Neural Network (H-CNN) for proteins. H-CNN is a physically motivated machine learning approach to model amino acid preferences in protein structures. It accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

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