Combining Molecular Dynamics and Machine Learning to Predict Drug Resistance Causing Variants of BRAF in Colorectal Cancer.

The BRAF protein regulates cell growth and division through key signaling pathways. Mutations in BRAF, particularly the V600E variant, are frequently observed in colorectal cancer (CRC) and are associated with poor prognosis and therapeutic challenges. Tumors harboring certain BRAF mutations often exhibit primary resistance to BRAF inhibitor monotherapies. Over time, these tumors can also develop acquired resistance, further complicating treatment. In this study, we employed replica exchange molecular dynamics simulations combined with machine learning techniques to investigate the structural alterations induced by BRAF mutations and their contribution to drug resistance. Our analyses revealed that conformational changes in mutant BRAF proteins associated with dabrafenib residues psi494, phi600, phi644, phi663, psi675, and phi677 were sufficient for classifying drug-resistant vs. drug-sensitive variants. Similarly, for vemurafenib, residues psi450, phi484, phi495, phi518, psi622, and phi622 were the key residues that influence drug binding and resistance mechanisms. These residues are located in the N-lobe of CR3, which is responsible for ATP binding and the regulation of BRAF kinase activity. These findings offer deeper insights into the molecular basis of BRAF-driven resistance and provide predictive models for phenotypic outcomes of various BRAF mutations. The study underscores the importance of targeting specific BRAF variants for more effective, personalized therapeutic strategies in drug-resistant CRC patients.