Fast, sensitive detection of protein homologs using deep dense retrieval
Hong L, Hu Z, Sun S, Tang X, Wang J, Tan Q, Zheng L, Wang S, Xu S, King I, Gerstein M, Li Y. Fast, sensitive detection of protein homologs using deep dense retrieval. Nature Biotechnology 2024, 1-13. PMID: 39123049, DOI: 10.1038/s41587-024-02353-6.Peer-Reviewed Original ResearchProtein language modelsRemote homologsProtein homologsProtein sequence comparisonsAlignment-based approachesWell-characterized proteinsPSI-BLASTSuperfamily levelProtein evolutionSequence comparisonProtein sequencesHomologyProteinSensitivity compared to previous methodsSensitive detectionHMMERSuperfamilyStructural informationSequenceLeveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach
Frank M, Ni P, Jensen M, Gerstein M. Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320510121. PMID: 39110734, PMCID: PMC11331094, DOI: 10.1073/pnas.2320510121.Peer-Reviewed Original ResearchConceptsProtein phase transitionsAssociated with reduced gene expressionProtein structure predictionAlzheimer's disease-related proteinsDisease-related proteinsAlzheimer's diseaseProtein sequencesSequence variantsStructure predictionAmyloid aggregatesProtein designGene expressionAge-related diseasesNatural defense mechanismsSoluble stateProteinDefense mechanismsBiophysical featuresAlzheimerSequenceAmyloidVariantsExpressionLanguage modelComputational framework