Sitetack: a deep learning model that improves PTM prediction by using known PTMs
Name
btae602.pdf
Description
Published version
Size
6.75 MB
Format
Adobe PDF
Checksum (MD5)
56f66f586e61e7df78b2290e07e253df
Author(s)
Gutierrez, Clair S
Kassim, Alia A
Gutierrez, Benjamin D
Raines, Ronald T
Date Issued
November 1, 2024
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Clair S Gutierrez, Alia A Kassim, Benjamin D Gutierrez, Ronald T Raines, Sitetack: a deep learning model that improves PTM prediction by using known PTMs, Bioinformatics, Volume 40, Issue 11, November 2024.
Version
Final published version
Abstract
Motivation
Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success.
Results
We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.
Availability and implementation
Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Broad Institute of MIT and Harvard
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btae602
