ArticleNature communications2016
De-novo protein function prediction using DNA binding and RNA binding proteins as a test case.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- PAI-1 is a potential transcriptional silencer that supports bladder cancer cell activity.Scientific reports · 2022Article
- In silico approach to probe the binding affinity between OMVs harboring the ZJournal of molecular modeling · 2022Article
- Identifying protein function and functional links based on large-scale co-occurrence patterns.PloS one · 2022Article
- DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.Computational and structural biotechnology journal · 2022Article
- dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.Nucleic acids research · 2021Article
- Article
- Refinement of bamboo genome annotations through integrative analyses of transcriptomic and epigenomic data.Computational and structural biotechnology journal · 2021Article
- Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- Long noncoding RNA FGF14-AS2 inhibits breast cancer metastasis by regulating the miR-370-3p/FGF14 axis.Cell death discovery · 2020Article
- FGF14 Functions as a Tumor Suppressor through Inhibiting PI3K/AKT/mTOR Pathway in Colorectal Cancer.Journal of Cancer · 2020Article
- Refinement of evolutionary medicine predictions based on clinical evidence for the manifestations of Mendelian diseases.Scientific reports · 2019Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Of the currently identified protein sequences, 99.6% have never been observed in the laboratory as proteins and their molecular function has not been established experimentally. Predicting the function of such proteins relies mostly on annotated homologs. However, this has resulted in some erroneous annotations, and many proteins have no annotated homologs. Here we propose a de-novo function prediction approach based on identifying biophysical features that underlie function. Using our approach, we discover DNA and RNA binding proteins that cannot be identified based on homology and validate these predictions experimentally. For example, FGF14, which belongs to a family of secreted growth factors was predicted to bind DNA. We verify this experimentally and also show that FGF14 is localized to the nucleus. Mutating the predicted binding site on FGF14 abrogated DNA binding. These results demonstrate the feasibility of automated de-novo function prediction based on identifying function-related biophysical features.
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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.