LigASite--a Database of Biologically Relevant Binding Sites in Proteins with Known Apo-structures
Overview
Affiliations
Better characterization of binding sites in proteins and the ability to accurately predict their location and energetic properties are major challenges which, if addressed, would have many valuable practical applications. Unfortunately, reliable benchmark datasets of binding sites in proteins are still sorely lacking. Here, we present LigASite ('LIGand Attachment SITE'), a gold-standard dataset of binding sites in 550 proteins of known structures. LigASite consists exclusively of biologically relevant binding sites in proteins for which at least one apo- and one holo-structure are available. In defining the binding sites for each protein, information from all holo-structures is combined, considering in each case the quaternary structure defined by the PQS server. LigASite is built using simple criteria and is automatically updated as new structures become available in the PDB, thereby guaranteeing optimal data coverage over time. Both a redundant and a culled non-redundant version of the dataset is available at http://www.scmbb.ulb.ac.be/Users/benoit/LigASite. The website interface allows users to search the dataset by PDB identifiers, ligand identifiers, protein names or sequence, and to look for structural matches as defined by the CATH homologous superfamilies. The datasets can be downloaded from the website as Schema-validated XML files or comma-separated flat files.
BioLiP2: an updated structure database for biologically relevant ligand-protein interactions.
Zhang C, Zhang X, Freddolino P, Freddolino L, Zhang Y Nucleic Acids Res. 2023; 52(D1):D404-D412.
PMID: 37522378 PMC: 10767969. DOI: 10.1093/nar/gkad630.
Molecular docking in organic, inorganic, and hybrid systems: a tutorial review.
Mohanty M, Mohanty P Monatsh Chem. 2023; :1-25.
PMID: 37361694 PMC: 10243279. DOI: 10.1007/s00706-023-03076-1.
PLBD: protein-ligand binding database of thermodynamic and kinetic intrinsic parameters.
Linge D, Gedgaudas M, Merkys A, Petrauskas V, Vaitkus A, Grybauskas A Database (Oxford). 2023; 2023.
PMID: 37290059 PMC: 10250011. DOI: 10.1093/database/baad040.
Feidakis C, Krivak R, Hoksza D, Novotny M Bioinformatics. 2022; 38(24):5452-5453.
PMID: 36282546 PMC: 9750100. DOI: 10.1093/bioinformatics/btac701.
CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome.
Wang S, Lin H, Huang Z, He Y, Deng X, Xu Y Biomolecules. 2022; 12(7).
PMID: 35883523 PMC: 9312471. DOI: 10.3390/biom12070967.