biostudies-literatureChaves EJFConselho Nacional de Desenvolvimento Cient?fico e Tecnol?gicoInstituto Nacional de Ci?ncia e Tecnologia de Fluidos Complexos2602-2609https://www.ebi.ac.uk/biostudies/studies/S-EPMC11898044biostudies-literatureUnknown65(5)Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to <i>ex vivo</i> biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them. We devised a machine learning ensemble model that uses Rosetta-based quantities to predict binding free energies of protein-protein complexes with accuracy rivaling both computationally demanding methodsJournal of chemical information and modelingEstimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.PMC11898044303833/2022-0Cruz CHBMhrous ENLins RDNacimento-Filho MFFerraz MVFSartori JSantos WMChaves EJFfalseEstimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to <i>ex vivo</i> biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them. We devised a machine learning ensemble model that uses Rosetta-based quantities to predict binding free energies of protein-protein complexes with accuracy rivaling both computationally demanding methods2025-01-01T00:00:00Z2025 Mar2026-06-02T21:05:05.156Z2026-05-28T03:06:13.996ZS-EPMC118980443997329210.1021/acs.jcim.4c01641