Prediction of Protein Secondary Structures Based on Substructural Descriptors of Molecular Fragments

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence–structure–property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed.