{"doi":"10.1093/nar/gkaf831","title":"Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning","abstract":"Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with machine learning have made it possible to accurately define such molecular recognition in a biophysically interpretable way for hundreds of TFs across many structural families, providing new avenues for predicting how the sequence preference of a TF is impacted by disease-associated mutations in its DNA binding domain. We developed a method based on a reference-free tetrahedral representation of variation in base preference within a given structural family that can be used to accurately predict the effect of mutations in the protein sequence of the TF. Using the basic helix-loop-helix (bHLH) and homeodomain (HD) families as test cases, our results demonstrate the feasibility of accurately predicting the shifts (ΔΔΔG/RT) in binding free energy associated with TF mutants by leveraging high-quality DNA binding models for sets of homologous wild-type TFs.","journal":"Nucleic Acids Research","year":2025,"id":526693,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9475,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1403342,"name":"Pilar Gomez-Alcala","orcid":null,"position":1,"is_corresponding":false},{"id":279491,"name":"Christ Leemans","orcid":"0000-0002-3271-840X","position":2,"is_corresponding":false},{"id":882246,"name":"William J. Glassford","orcid":"0000-0003-4007-6731","position":3,"is_corresponding":false},{"id":820833,"name":"Lucas A. N. Melo","orcid":"0000-0002-7563-7554","position":4,"is_corresponding":false},{"id":329195,"name":"Xiang‐Jun Lu","orcid":"0000-0002-8901-7444","position":5,"is_corresponding":false},{"id":285977,"name":"Richard S. Mann","orcid":"0000-0002-4749-2765","position":6,"is_corresponding":false},{"id":356490,"name":"Harmen J. Bussemaker","orcid":"0000-0002-7274-5277","position":7,"is_corresponding":false},{"id":1402815,"name":"Shaoxun Liu","orcid":"0000-0003-0997-3485","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":null,"created_at":"2026-07-19T02:50:34.851930Z","pmid":"40874594","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}