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bioRxiv · 10.1101/2023.02.28.530456

PS4: a Next-Generation Dataset for Protein SingleSequence Secondary Structure Prediction

Peracha, O. A.
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Abstract

Protein secondary structure prediction is a subproblem of protein folding. A lightweight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could provide a useful input for tertiary structure prediction, alleviating the reliance on MSA typically seen in todays best-performing models. Unfortunately, existing datasets for secondary structure prediction are small, creating a bottleneck. We present PS4, a dataset of 18,731 non-redundant protein chains and their respective secondary structure labels. Each chain is identified, and the dataset is also non-redundant against other secondary structure datasets commonly seen in the literature. We perform ablation studies by training secondary structure prediction algorithms on the PS4 training set, and obtain state-of-the-art accuracy on the CB513 test set in zero shots.

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Peracha, O. A.. 2023-03-01. PS4: a Next-Generation Dataset for Protein SingleSequence Secondary Structure Prediction. https://doi.org/10.1101/2023.02.28.530456

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