ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles

TL;DR

ProNet DB is a comprehensive database that provides detailed protein surface property representations and RNA-binding profiles for over 326,000 structures, facilitating research in protein interactions and drug discovery.

Contribution

This work introduces the first extensive database integrating multiple protein surface representations and RNA-binding landscapes across diverse proteomes from AlphaFold and PDB.

Findings

Includes surface properties like hydrophobicity, charge, hydrogen bonds

Provides RNA-binding sites and preferences for proteins

Enables instant access to pre-computed features for research

Abstract

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation. Recently, AlphaFold2 released the comprehensive proteome of various species, and protein surface property representation plays a crucial role in protein-molecule interaction prediction such as protein-protein interaction, protein-nucleic acid interaction, and protein-compound interaction. Here, we proposed the first comprehensive database, namely ProNet DB, which incorporates multiple protein surface representations and RNA-binding landscape for more than 326,175 protein structures covering 16 model organism proteomes from AlphaFold Protein Structure Database (AlphaFold DB) and experimentally validated protein structures deposited in Protein Data Bank (PDB). For each protein, we provided the original protein structure, surface property representation including hydrophobicity, charge distribution, hydrogen bond, interacting face, and RNA-binding landscape such as RNA binding sites and RNA binding preference. To interpret protein surface property representation and RNA binding landscape intuitively, we also integrate Mol* and Online 3D Viewer to visualize the representation on the protein surface. The pre-computed features are available for the users instantaneously and boost computational biology development including molecular mechanism exploration, geometry-based drug discovery and novel therapeutics development. The server is now available on https://proj.cse.cuhk.edu.hk/aihlab/pronet/.