ArticleNature biotechnology2022
Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- Identification of methylation-sensitive human transcription factors using meSMiLE-seq.Nature communications · 2026Article
- REAL-TIME VISUALIZATION OF SPLICEOSOME ASSEMBLY REVEALS BASIC PRINCIPLES OF 3' SPLICE SITE SELECTION.Research square · 2026Article
- Integrated structure-based and systems-level identification of PDE9A inhibitors for heart failure with preserved ejection fraction.BMC biotechnology · 2026Article
- Context-dependent peptide recognition shapes tyrosine kinase substrate specificity beyond consensus motifs.bioRxiv : the preprint server for biology · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- U2AF regulates the translation and localization of nuclear-encoded mitochondrial mRNAs.Molecular cell · 2026Article
- BayesPI-FLY: a Bayesian neural network approach for inferring feature weighted TF-DNA interaction.Briefings in bioinformatics · 2026Article
- Multimodal bHLH-PAS DNA binding controls specificity and drives obesity.Nucleic acids research · 2026Article
- Profiling tyrosine kinase substrate recognition using bacterial peptide display and deep sequencing.bioRxiv : the preprint server for biology · 2025Article
- Enhancing monoclonal antibody diversity by integrating bulk sorting and machine learning.Biochemistry and biophysics reports · 2025Article
- KATMAP infers splicing factor activity and regulatory targets from knockdown data.Nature biotechnology · 2025Article
- Accurate affinity models for SH2 domains from peptide binding assays and free-energy regression.Protein science : a publication of the Protein Society · 2025Article
- Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids.Briefings in bioinformatics · 2025Article
- An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.Bioinformatics (Oxford, England) · 2025Article
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13 authors.
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Abstract
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called K
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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.