Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

Abstract

In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. -helices and {beta}-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.