The DOI matches its Crossref record.
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210
The I-TASSER Suite: protein structure and function prediction
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596
Self-citationGeneralized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978
Zhang, C., Freddolino, L., & Zhang, Y. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx366 · Cited by 606
Self-citationAnnotation of biologically relevant ligands in UniProtKB using ChEBI
Coudert, E., Gehant, S., de Castro, E., Pozzato, M., Baratin, D., Neto, T., Sigrist, C. J. A., Redaschi, N., Bridge, A., The UniProt Consortium, et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac793 · Cited by 350
Impact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., García Melani, A., Novomisky Nechcoff, J., Salas, M. N., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac202 · Cited by 196
AlphaFold2-aware protein–DNA binding site prediction using graph transformer
Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network
Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133
Pony, P., Rapisarda, C., Terradot, L., Marza, E., & Fronzes, R. (2020) · Nature Communications · doi:10.1038/s41467-020-15214-y · Cited by 82
Zhang, C., Zheng, W., Freddolino, L., & Zhang, Y. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.03.004 · Cited by 80
Self-citationBhagavat, R., Sankar, S., Srinivasan, N., & Chandra, N. (2018) · Structure · doi:10.1016/j.str.2018.02.001 · Cited by 41
RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks
Szymborski, J., & Emad, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac429 · Cited by 34
Language models for the prediction of SARS-CoV-2 inhibitors
Blanchard, A. E., Gounley, J., Bhowmik, D., Chandra Shekar, M., Lyngaas, I., Gao, S., Yin, J., Tsaris, A., Wang, F., & Glaser, J. (2022) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420221121804 · Cited by 30
Zhang, C., Zheng, W., Cheng, M., Omenn, G. S., Freddolino, P. L., & Zhang, Y. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00359 · Cited by 27
Self-citationKiefl, E., Esen, O. C., Miller, S. E., Kroll, K. L., Willis, A. D., Rappé, M. S., Pan, T., & Eren, A. M. (2023) · Science Advances · doi:10.1126/sciadv.abq4632 · Cited by 26
Zhang, H., Fan, H., Wang, J., Hou, T., Saravanan, K. M., Xia, W., Kan, H. W., Li, J., Zhang, J. Z. H., Liang, X., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae281 · Cited by 25
Gamouh, H., Novotný, M., & Hoksza, D. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf431 · Cited by 23
Zhang, C., Lane, L., Omenn, G. S., & Zhang, Y. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00537 · Cited by 22
Self-citationLambdaPP : Fast and accessible protein‐specific phenotype predictions
(2022) · doi:10.1002/pro.4524 · Cited by 19
Impact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., et al. (2021) · doi:10.1101/2021.10.27.466189 · Cited by 196
MacGowan, S. A., Madeira, F., Britto-Borges, T., & Barton, G. J. (2024) · Communications Biology · doi:10.1038/s42003-024-06117-5 · Cited by 8
A scalable equivariant graph network framework for precise protein function prediction
Ran, Z., Guo, X., Pan, T., Bi, Y., Hao, Y., Sun, H., Song, J., & Li, F. (2025) · Genome Biology · doi:10.1186/s13059-025-03886-y · Cited by 8
Prediction of Protein Ion–Ligand Binding Sites with ELECTRA
Essien, C., Jiang, L., Wang, D., & Xu, D. (2023) · Molecules · doi:10.3390/molecules28196793 · Cited by 5
Deep learning molecular interaction motifs from receptor structures alone
Kim, S., Oh, S., Woo, H., Sim, J., Seok, C., & Park, H. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01055-8 · Cited by 3
Zhang, S., Xie, L., Tiourine, D., & Xie, L. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01227-0 · Cited by 2
Metadata
- Authors
- Yang, J., Roy, A., & Zhang, Y.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-10-18
- Volume
- 41
- Issue
- D1
- Pages
- D1096-D1103
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2018661561
- Cited by
- 759
- References
- 0
- FWCI
- 21.04
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.