ArticleBMC bioinformatics2019
FunFam protein families improve residue level molecular function prediction.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Structural plasticity drives convergent evolution and functional diversification of GNAT polyamine acetyltransferases.Structure (London, England : 1993) · 2026Article
- Glycogen deficiency impairs diurnal energy metabolism and cell division in Synechocystis.Plant physiology · 2026Article
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- Unraveling the microbial diversity of bovine liver abscesses: isolation, identification, and genomic characterization of theMicrobiology spectrum · 2025Article
- Haplotype-resolved reference genomes of the sea turtle clade unveil ultra-syntenic genomes with hotspots of divergence.GigaScience · 2025Article
- Analysis of metagenomic data.Nature reviews. Methods primers · 2025Article
- Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Current successes and remaining challenges in protein function prediction.Frontiers in bioinformatics · 2023Article
- A roadmap for the functional annotation of protein families: a community perspective.Database : the journal of biological databases and curation · 2022Article
- Article
- CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation.PLoS computational biology · 2022Article
- EnsembleFam: towards more accurate protein family prediction in the twilight zone.BMC bioinformatics · 2022Article
- SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites.PLoS computational biology · 2022Article
- Assigning protein function from domain-function associations using DomFun.BMC bioinformatics · 2022Article
- Convergent evolution of venom gland transcriptomes across Metazoa.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- PANNZER-A practical tool for protein function prediction.Protein science : a publication of the Protein Society · 2022Article
- Clustering FunFams using sequence embeddings improves EC purity.Bioinformatics (Oxford, England) · 2021Article
- Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification.BMC bioinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these FunFams and incorporated FunFams into the prediction of protein binding residues.
resultsFunFam members agreed, on average, in 36.9 ± 0.6% of their binding residue annotations. This constituted a 6.7-fold increase over randomly grouped proteins and a 1.2-fold increase (1.1-fold on the same dataset) over proteins with the same enzymatic function (identical Enzyme Commission, EC, number). Mapping de novo binding residue prediction methods (BindPredict-CCS, BindPredict-CC) onto FunFam resulted in consensus predictions for those residues that were aligned and predicted alike (binding/non-binding) within a FunFam. This simple consensus increased the F1-score (for binding) 1.5-fold over the original prediction method. Variation of the threshold for how many proteins in the consensus prediction had to agree provided a convenient control of accuracy/precision and coverage/recall, e.g. reaching a precision as high as 60.8 ± 0.4% for a stringent threshold.
conclusionsThe FunFams outperformed even the carefully curated EC numbers in terms of agreement of binding site residues. Additionally, we assume that our proof-of-principle through the prediction of protein binding residues will be relevant for many other solutions profiting from FunFams to infer functional information at the residue level.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.