Dataset Information

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.

ABSTRACT: Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.

SUBMITTER: Motmaen A

PROVIDER: S-EPMC9992841 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.

Motmaen Amir A Dauparas Justas J Baek Minkyung M Abedi Mohamad H MH Baker David D Bradley Philip P

Proceedings of the National Academy of Sciences of the United States of America 20230221 9

Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned t ...[more]

PMID: 36802421

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Project description:BackgroundObtaining transcripts of homologs of closely related organisms and retrieving the reconstructed exon-intron patterns of the genes is a very important process during the analysis of the evolution of a protein family and the comparative analysis of the exon-intron structure of a certain gene from different species. Due to the ever-increasing speed of genome sequencing, the gap to genome annotation is growing. Thus, tools for the correct prediction and reconstruction of genes in related organisms become more and more important. The tool Scipio, which can also be used via the graphical interface WebScipio, performs significant hit processing of the output of the Blat program to account for sequencing errors, missing sequence, and fragmented genome assemblies. However, Scipio has so far been limited to high sequence similarity and unable to reconstruct short exons.ResultsScipio and WebScipio have fundamentally been extended to better reconstruct very short exons and intron splice sites and to be better suited for cross-species gene structure predictions. The Needleman-Wunsch algorithm has been implemented for the search for short parts of the query sequence that were not recognized by Blat. Those regions might either be short exons, divergent sequence at intron splice sites, or very divergent exons. We have shown the benefit and use of new parameters with several protein examples from completely different protein families in searches against species from several kingdoms of the eukaryotes. The performance of the new Scipio version has been tested in comparison with several similar tools.ConclusionsWith the new version of Scipio very short exons, terminal and internal, of even just one amino acid can correctly be reconstructed. Scipio is also able to correctly predict almost all genes in cross-species searches even if the ancestors of the species separated more than 100 Myr ago and if the protein sequence identity is below 80%. For our test cases Scipio outperforms all other software tested. WebScipio has been restructured and provides easy access to the genome assemblies of about 640 eukaryotic species. Scipio and WebScipio are freely accessible at http://www.webscipio.org.

Dataset Information

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.

Publications

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.

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