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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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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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xProtCAS: A Toolkit for Extracting Conserved Accessible Surfaces from Protein Structures.

Hazem M Kotb¹, Norman E Davey¹

¹Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.

|June 28, 2023

Summary

This study introduces a new tool to find conserved protein surfaces using deep learning structural models. It identifies numerous uncharacterized conserved surfaces in the human proteome, some linked to disease mutations.

Keywords:

conserved surface discovery evolution graph theory interactions protein structure

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Area of Science:

Biochemistry
Structural Biology
Bioinformatics

Background:

Protein interaction interfaces are crucial for understanding protein function, regulation, and disease.
Identifying conserved surface residues is key to predicting these interfaces.
Deep learning advancements provide high-quality protein structure models for analysis.

Purpose of the Study:

To develop and present computational tools for identifying conserved protein surfaces on AlphaFold2 structural models.
To enable the discovery of novel functional protein regions and disease-associated sites.

Main Methods:

Utilized autonomous structural modules derived from protein models.
Encoded structural modules into graphs representing residue topology, accessibility, and conservation.
Employed an eigenvector centrality-based approach to extract conserved surfaces.

Main Results:

Successfully applied the tool to the human proteome, identifying hundreds of conserved surfaces.
Discovered numerous uncharacterized conserved surfaces, many harboring clinically significant mutations.
Developed xProtCAS as open-source software and an interactive web server.

Conclusions:

The developed tools effectively identify conserved protein surfaces from deep learning structural models.
This approach facilitates the discovery of novel functional sites and disease-related mutations across the proteome.
xProtCAS provides a valuable resource for researchers in structural biology and disease genetics.