Keywords

protein, prediction, contact, distance, deep learning, alphafold, ProSPr, CASP, dataset, retrainable

Abstract

The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance in the recent Critical Assessment of Techniques for Protein Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on sequence length and multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.

Original Publication Citation

Stern, J., Hedelius, B., Fisher, O., Billings, W. M., & Della Corte, D. (2021). Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets. International Journal of Molecular Sciences, 22(23), 12835. https://doi.org/10.3390/ijms222312835 https://doi.org/10.3390/ijms222312835

Document Type

Peer-Reviewed Article

Publication Date

2021-11-27

Publisher

International Journal of Molecular Sciences

Language

English

College

Computational, Mathematical and Physical Sciences

Department

Physics and Astronomy

University Standing at Time of Publication

Associate Professor

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