Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors

Abstract

Significance We propose and demonstrate the use of two-dimensional UV (2DUV) spectroscopic features as observable-based descriptors for a machine learning protocol aimed at discriminating protein secondary structure motifs. The 2DUV spectra viewed as grayscale images were fed into convolutional neural networks (CNNs), resulting in accurate spectrum-structure correlation model. This enables an automated secondary structure recognition with accuracies of 97% (91%) for (non-)homologous protein segments. The success of protein 2DUV descriptors for machine learning is ascribed to their unique cross-peak information that reflects couplings between electronic transitions located at different chromophores. Incorporating coupling information is crucial for connecting the descriptors to the protein structure.