MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization

Abstract MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.

• Publication year

  2020

• Venue

  Nucleic Acids Res.

• Publication date

  2020-04-23

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1093/nar/gkaa275PMID 32324217PMCID 7319475
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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