Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

Autor: López-Cortés A; Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador. aalc84@gmail.com., Cabrera-Andrade A; Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador.; Escuela de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador., Echeverría-Garcés G; Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública 'Leopoldo Izquieta Pérez', Quito, Ecuador.; Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile., Echeverría-Espinoza P; Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador., Pineda-Albán M; Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador., Elsitdie N; Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador., Bueno-Miño J; Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador., Cruz-Segundo CM; RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain.; Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico., Dorado J; RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain.; Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain., Pazos A; RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain.; Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain.; Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain., Gonzáles-Díaz H; Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Biscay, Spain.; IKERBASQUE, Basque Foundation for Science, Biscay, Spain., Pérez-Castillo Y; Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador., Tejera E; Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador., Munteanu CR; RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain.; Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain.; Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 21; Vol. 14 (1), pp. 19359. Date of Electronic Publication: 2024 Aug 21.
DOI: 10.1038/s41598-024-68565-7
Abstrakt: The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .
(© 2024. The Author(s).)
Databáze: MEDLINE
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