SSpro/ACCpro 6: Almost Perfect Prediction of Protein Secondary Structure and Relative Solvent Accessibility Using Profiles, Deep Learning, and Structural Similarity.

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Abstract

Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure, and an early-stage component of typical protein 3D structure prediction pipelines.We present a new improved version of the SSpro/ACCpro suite of predictors for the prediction of protein secondary structure (in 3 and 8 classes) and relative solvent accessibility. The changes include improved, TensorFlow-trained, deep learning predictors, a richer set of profile features (232 features per residue position) and sequence-only features (71 features per position), a more recent PDB snapshot for training, better hyperparameter tuning, and improvements made to the HOMOLpro module, which leverages structural information from protein segment homologs in the Protein Data Bank (PDB). The new SSpro 6 outperforms the previous version (SSpro 5) by 3-4% in Q3 accuracy and, when used with HOMOLPRO, reaches accuracy in the 95-100% range.The predictors software, data, and web servers are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu. To maximize comptatibility and ease of use, the deep learning predictors are re-implemented as pure Python/numpy code without TensorFlow dependency.© The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].