2024
Leveraging 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 frameworkA survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Tang X, Dai H, Knight E, Wu F, Li Y, Li T, Gerstein M. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Briefings In Bioinformatics 2024, 25: bbae338. PMID: 39007594, PMCID: PMC11247410, DOI: 10.1093/bib/bbae338.Peer-Reviewed Original Research
2005
Calculation of Standard Atomic Volumes for RNA and Comparison with Proteins: RNA is Packed More Tightly
Voss NR, Gerstein M. Calculation of Standard Atomic Volumes for RNA and Comparison with Proteins: RNA is Packed More Tightly. Journal Of Molecular Biology 2005, 346: 477-492. PMID: 15670598, DOI: 10.1016/j.jmb.2004.11.072.Peer-Reviewed Original Research