Learning the local landscape of protein structures with convolutional neural networks

• Published in: Journal of biological physics Vol. 47; no. 4; pp. 435 - 454
• Main Authors: Kulikova, Anastasiya V., Diaz, Daniel J., Loy, James M., Ellington, Andrew D., Wilke, Claus O.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2021; Springer Nature B.V
• Subjects: Amino Acid Sequence; Amino Acids; Biochemistry; Biological and Medical Physics; Biophysics; Complex Fluids and Microfluidics; Complex Systems; Conserved sequence; Mutation; Neural networks; Neural Networks, Computer; Neurosciences; Nucleotide sequence; Original Paper; Physics; Physics and Astronomy; Predictions; Protein engineering; Protein structure; Proteins; Proteins - genetics; Soft and Granular Matter; The Revolutionary Impact of Landscapes in Biology; Mutation; Microenvironment; Protein structure; Convolutional neural network
• ISSN: 0092-0606; 1573-0689
• DOI: 10.1007/s10867-021-09593-6

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.