Work

LMSuccSite: Improving Protein Succinylation Sites Prediction Using Embeddings from Protein Language Model

Year: 2022

Type: preprint

Abstract: Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.

Source: Research Square

DOI: 10.21203/rs.3.rs-1953874/v1

Authors: Suresh Pokharel, Pawel Pratyush, Michael Heinzinger, Robert Newman, Dukka KC

Institutions: Michigan Technological University, Technical University of Munich, North Carolina Agricultural and Technical State University

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Collaborative Research: ABI Development: Integrated platforms for protein structure and function predictions, III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction