ArticleNature biotechnology2015
Affinity regression predicts the recognition code of nucleic acid-binding proteins.
Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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Who cites it
27 citing papers in PubMed, 65 citations in OpenAlex.
- NPBIP: predicting binding preferences of uncharacterized nucleic-acid-binding proteins.Bioinformatics (Oxford, England) · 2026Article
- A resource of RNA-binding protein motifs across eukaryotes reveals evolutionary dynamics and gene-regulatory function.Nature biotechnology · 2026Article
- Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning.Nucleic acids research · 2025Article
- DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.Nature communications · 2024Article
- Mapping drug biology to disease genetics to discover drug impacts on the human phenome.Bioinformatics advances · 2024Article
- COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells.Journal of medical virology · 2023Article
- Linking Expression of Cell-Surface Receptors with Transcription Factors by Computational Analysis of Paired Single-Cell Proteomes and Transcriptomes.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.Nucleic acids research · 2022Article
- RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites.PLoS computational biology · 2022Article
- Inferring RNA-binding protein target preferences using adversarial domain adaptation.PLoS computational biology · 2022Article
- SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators.Nucleic acids research · 2021Article
- Representation learning applications in biological sequence analysis.Computational and structural biotechnology journal · 2021Review
- ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins.Bioinformatics (Oxford, England) · 2020Article
- Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination.Nucleic acids research · 2020Article
- Similarity regression predicts evolution of transcription factor sequence specificity.Nature genetics · 2019Article
- Specificity landscapes unmask submaximal binding site preferences of transcription factors.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks.Wiley interdisciplinary reviews. Systems biology and medicine · 2018Review
- Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution.BMC bioinformatics · 2018Article
- Finding RNA structure in the unstructured RBPome.BMC genomics · 2018Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a challenging problem. We present a statistical approach for learning the recognition code of a family of transcription factors or RNA-binding proteins (RBPs) from high-throughput binding data. Our method, called affinity regression, trains on protein binding microarray (PBM) or RNAcompete data to learn an interaction model between proteins and nucleic acids using only protein domain and probe sequences as inputs. When trained on mouse homeodomain PBM profiles, our model correctly identifies residues that confer DNA-binding specificity and accurately predicts binding motifs for an independent set of divergent homeodomains. Similarly, when trained on RNAcompete profiles for diverse RBPs, our model correctly predicts the binding affinities of held-out proteins and identifies key RNA-binding residues, despite the high level of sequence divergence across RBPs. We expect that the method will be broadly applicable to modeling and predicting paired macromolecular interactions in settings where high-throughput affinity data are available.
Identifiers
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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.