Sitetack: a deep learning model that improves PTM prediction by using known PTMs.

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 ca

SUBMITTER: Gutierrez CS 

PROVIDER: S-EPMC11552626 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature