BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions

By Jianyi Yang, Ambrish Roy, Yang Zhang. 2012

733 citations recorded by the source corpus.

BioLiP (http://zhanglab.ccmb.med.umich.edu/BioLiP/) is a semi-manually curated database for biologically relevant ligand–protein interactions. Establishing interactions between protein and biologically relevant ligands is an important step toward understanding the protein functions. Most ligand-binding sites prediction methods use the protein structures from the Protein Data Bank (PDB) as templates. However, not all ligands present in the PDB are biologically relevant, as small molecules are often used as additives for solving the protein structures. To facilitate template-based ligand–protein docking, virtual ligand screening and protein function annotations, we develop a hierarchical procedure for assessing the biological relevance of ligands present in the PDB structures, which involves a four-step biological feature filtering followed by careful manual verifications. This procedure is used for BioLiP construction. Each entry in BioLiP contains annotations on: ligand-binding residues, ligand-binding affinity, catalytic sites, Enzyme Commission numbers, Gene Ontology terms and cross-links to the other databases. In addition, to facilitate the use of BioLiP for function annotation of uncharacterized proteins, a new consensus-based algorithm COACH is developed to predict ligand-binding sites from protein sequence or using 3D structure. The BioLiP database is updated weekly and the current release contains 204 223 entries.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Yang Zhang
2. Jianyi Yang
3. Robin Pearce
4. Chengxin Zhang
5. David Hoksza
6. Radoslav Krivák
7. Zhenling Peng
8. Qi Wu

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jianyi Yang
4. Liam J. McGuffin
5. Chengxin Zhang
6. Yang Li
7. Wei Zheng
8. Eric W. Bell

In Pharmacology

1. Yang Zhang
2. Jianyi Yang
3. Chengxin Zhang
4. Anima Anandkumar
5. Weili Nie
6. Arash Vahdat
7. Thomas F. Miller
8. Zhuoran Qiao

Citations in the corpus

• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, positive): “The holo structures are obtained from the intersection between the filtered set and the annotated protein–ligand complex database BioLip 71 41 and distinct bound ligands can be found from the PDB.”
• I-TASSER server: new development for protein structure and function predictions (2015, methodology): “ThecurrentreleaseofBioLiP(Jan16,2015) contains 304 050 entries constructed from 65 506 PDB proteins, in which 37 685 entries are for DNA / RNA–protein interactions, 13 967 for peptide–protein interactions, 85 652 for metal ion– protein interactions and 166 746 for regular small molecule– protein int”
• P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure (2018, background): “BioLiP [8], PDBbind [7]) that use different criteria for accepting particular ligand as biologically relevant (with MOAD being the strictest of them, not accepting any small ions for example).”
• Impact of protein conformational diversity on AlphaFold predictions (2021, methodology): “In parallel, the database of biological ligands BioLip [55] in its most recent version (October 01, 2021) was used to crosslink all the chains of the PDB (October 2021, total chains 661494) and CoDNaS v3 (March 2021, total chains 430151).”
• COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “Enzymatic homologs are identified by aligning the target structure, using TMalign (13), to a library of 8392 enzyme structures from the BioLiP library (12), with the active site residues mapped from the Catalytic Site Atlas database (21).”
• CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, methodology): “The template structure database used in CB-Dock2 is taken from BioLip (version of 2021.09.15) (34) which is currently the most comprehensive protein–ligand interaction database.”
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, methodology): “samples collected Sampling weight L3D LCC LMLM BioLip [55] ligands (deposited date<2019.”
• PDBFlex: exploring flexibility in protein structures (2015, methodology): “We used the BioLiP database to identify all biologically relevant ligands associated with PDB chains (15).”
• FireDB: a compendium of biological and pharmacologically relevant ligands (2013, methodology): “The recently introduced BioLip (7) has a more sophisticated composite automated and manual procedure in order to avoid the loss of information.”
• Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “Even for the proteins with experimentally solved 3D structure, there are nearly 40% of proteins (i.e. 35 633 out of 90 424) in the PDB which lack biologically relevant ligand-binding information , as shown in BioLiP (Yang et al., 2013).”
• UniProt: the Universal Protein Knowledgebase in 2023 (2022, background): “…ligand data taking advantage of the ChEBI chemical ontology hierarchy and chemical structure data, improves interoperability with other resources of cognate ligands (11–14), and provides better support for the development of computational approaches to predict protein-ligand interactions (15–17).”