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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: Jul 4, 2025

Localization of SUMO-modified Proteins Using Fluorescent Sumo-trapping Proteins

Localization of SUMO-modified Proteins Using Fluorescent Sumo-trapping Proteins

Published on: April 27, 2019

SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.

Andrew Vargas Palacios¹, Pujan Acharya¹, Anthony Stephen Peidl²

¹Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA.

NAR Genomics and Bioinformatics

|February 8, 2024

Summary

This study introduces SumoPred-PLM, a novel AI tool for predicting SUMOylation sites in human proteins. It leverages deep learning to enhance the identification of these crucial post-translational modifications, accelerating biological discovery.

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SUMO-Binding Entities SUBEs as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

SUMO-Binding Entities SUBEs as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

Published on: November 1, 2019

Area of Science:

Biochemistry
Computational Biology
Genomics

Background:

SUMOylation is a critical post-translational modification regulating cellular functions.
SUMO1, SUMO2, and SUMO3 are key paralogues involved in this process.
Current methods struggle to fully map the SUMOylated proteome.

Purpose of the Study:

To develop a novel deep learning approach for predicting human SUMOylation sites.
To identify SUMO2/3-specific conjugation sites using advanced AI.
To accelerate the discovery of SUMOylated proteins and their functions.

Main Methods:

Developed SumoPred-PLM, a deep learning-based prediction tool.
Utilized contextualized embeddings from the ProtT5-XL-UniRef50 protein language model.
Validated performance on the CPLM 4.0 independent test dataset.

Main Results:

SumoPred-PLM achieved high performance metrics: 74.64% sensitivity, 73.36% specificity, 0.48% MCC, and 74.00% accuracy.
The platform successfully identified SUMO2/3-specific conjugation sites.
Demonstrated the tool's power in predicting SUMOylation sites.

Conclusions:

SumoPred-PLM is a powerful computational tool for predicting protein SUMOylation.
This AI-driven approach significantly advances the study of SUMOylation.
Facilitates accelerated discovery in molecular and cellular biology.