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

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arxiv 2205.07673 v2 pith:KT4CDVPG submitted 2022-05-16 q-bio.QM q-bio.BMq-bio.MN

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

classification q-bio.QM q-bio.BMq-bio.MN
keywords proteinsurfacepropertyrepresentationinteractionstructuresbindingdatabase
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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/.

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