Integrating molecular dynamics and machine learning to identify potential apo-state conformational and solvent-exposure signatures associated with resistant KRAS mutants
Aug 2026· Frontiers in Chemical Biology· Vol 5· 0 citations· 66 references
TL;DR
A computational framework integrating molecular dynamics (MD)-derived structural, energetic, thermodynamic, and contact-based descriptors with machine learning may inform the design of inhibitors targeting secondary KRAS resistance mutations, pending validation in additional structurally independent mutant systems.
Abstract
Mutation-induced drug resistance is a major contributor to the failure of targeted cancer therapies, particularly in tumors driven by mutations in the KRAS oncogene. Although covalent inhibitors effectively target KRAS G12C, secondary mutations such as G12C/Y96C, G12C/Y96S, and G12C/Y96D confer resistance despite leaving the covalent attachment site intact.
To investigate the conformational basis of this resistance, we developed a computational framework integrating molecular dynamics (MD)-derived structural, energetic, thermodynamic, and contact-based descriptors with machine learning. All simulations were performed in the apo (unbound) state; the results therefore reflect conformational and solvent-exposure correlates associated with resistant mutants rather than inhibitor-specific resistance mechanisms. Molecular descriptors extracted from MD simulations of treatment-sensitive and treatment-resistant KRAS systems were used to train logistic regression, random forest, support vector machine, and Bayesian network classifiers. To address the correlated nature of MD-derived conformers, model performance was evaluated using a system-independent mixed held-out validation scheme, and univariate analysis employed clustering-aware statistical approaches.
Cross-referencing machine learning feature importance rankings with within-resistant-group variability testing revealed an important distinction between features reflecting mutant-identity-specific variation and those consistent with a shared resistance phenotype. Residue-level descriptors: solvent-accessible surface area variability at E62 and H95, Lennard-Jones 1,4 interaction energy, and root mean square fluctuation at M72 and H95 were both consistently discriminative across validation schemes and statistically consistent across all three resistant mutants, suggesting that they represent potential apo-state conformational and solvent-exposure signatures associated with resistant KRAS mutants.
Our proof-of-concept workflow may inform the design of inhibitors targeting secondary KRAS resistance mutations, pending validation in additional structurally independent mutant systems.
An integrated computational workflow combining explainable machine learning, virtual screening, molecular dynamics simulations, and binding free-energy calculations to identify novel inhibitors of this drug-resistant EGFR variant may support the development of new therapeutic strategies for overcoming resistance in EGFR-driven cancers.
Jurica Novak· International Journal of Mol...· 0 citations
Drug resistance in epidermal growth factor receptor (EGFR)-mutant cancers commonly arises from kinase-domain substitutions that remodel the adenosine triphosphate binding pocket and reduce complementarity to orthosteric inhibitors, with the T790M gatekeeper mutation posing a major challenge. This study evaluated whether pre-occupying a proximal allosteric pocket with selected phytochemicals could bias mutant EGFR toward drug-compatible conformations and improve inhibitor binding. A two-phase computational workflow was employed: (i) molecular docking of gefitinib and erlotinib to wild-type and mutant EGFR; and (ii) allosteric pre-docking of phytochemicals followed by redocking of the ATP-site inhibitor. Top-ranked complexes were advanced to 200-nanosecond all-atom molecular dynamics simulations in explicit solvent and end-state binding free-energy estimation using Molecular Mechanics Generalized Born Surface Area (MM/GBSA). Docking predicted stronger binding to wild-type EGFR and reduced affinity for the T790M mutant, whereas co-binding produced compound-dependent improvements. Simulations suggested partial stabilization of the protein-ligand complexes, characterized by reduced root mean square deviation, damped hinge and αC-helix motions, reduced solvent exposure, and radii of gyration approaching wild-type behavior. Binding free energies improved from -12.6 to -17.6 kcal mol-1 (Genistein) and -19.58 kcal mol-1 (Tupichinols C) for gefitinib, and from -13.4 to -18.1 and -20.1 kcal mol-1, respectively, for erlotinib. Absorption, distribution, metabolism, excretion, and toxicity screening supported the developability of the leading candidates. This integrated framework provides structural, dynamic, and energetic criteria for prioritize cooperative allosteric-orthosteric co-binding chemotypes for experimental validation.
A. Sindi· Journal of Biomolecular Stru...· 0 citations
Protein kinases regulate cellular proliferation and survival through tightly controlled signalling mechanisms, and their dysregulation is a major driver of cancer. Human epidermal growth factor receptor 2 (HER2) is a clinically validated kinase target, yet structural and drug-discovery efforts have largely focused on type I inhibitors binding the active DFG-in conformation. The absence of experimentally resolved structures for HER2 in the inactive DFG-out state has limited structure-based development of inactive-state type II inhibitors. Here, we employ molecular dynamics (MD) simulations coupled with machine-learning-guided well-tempered metadynamics (MetaD) to characterize the conformational landscape underlying the DFG-in to DFG-out transition in HER2. Deep learning-based collective variable was constructed by the Deep Targeted Discriminant Analysis (DeepTDA) method employing unbiased MD data from both metastable states. DeepTDA enabled efficient sampling of the DFG-flip pathway, capturing multiple recrossing events and allowing reliable estimation of the free energy difference between the two states within accessible simulation time. The inactive DFG-out conformation is found to be energetically favoured by approximately 25 kJ/mol relative to the DFG-in state, in close agreement with experimental and computational data from other apo kinases. In addition to the canonical active conformation observed crystallographically, we identify an alternative DFG-in conformer that may contribute to the low intrinsic kinase activity reported for HER2. A DFG-up intermediate conformer is also observed, resembling crystal structures reported for Aurora-A kinase. A combined covalent docking and molecular dynamics approach followed to assess the MetaD-predicted DFG-out structure for ligand binding reliability, yielding comparable predicted/experimental binding affinity. Together, these results provide a structurally grounded inactive-state HER2 model and a mechanistic framework for structure-based design of HER2 inhibitors targeting inactive kinase conformations, highlighting the applicability of deep learning CVs to systems of complex free energy landscape.
Muhammad I. Ismail, Mai Adel, Eman M. E. Dokla et al.· Computational biology and ch...· 0 citations
It is elucidated that HY-18,623 achieves high-affinity binding through persistent hydrogen bonds with hinge residues Val96 (CDK4) and Val101 (CDK6) and Val101 (CDK6) as a promising lead candidate for further therapeutic development in oncology.
Yu-Xin Wang, Lin-Xia Fang, Quan-Fang Liu et al.· Molecular diversity· 0 citations
An integrated machine learning–guided computational pipeline was developed to identify potential PIM2 inhibitors by combining quantitative structure–activity relationship (QSAR) modeling, virtual screening, molecular docking, molecular dynamics simulations, and pharmacokinetic prediction, highlighting the potential of the identified molecules as promising molecules.
A. Fahira, M. Shahab, Zaheer Ud Din et al.· Journal of Genetic Engineeri...· 0 citations
A scalable, machine-learning-integrated virtual screening framework designed to explore ultra-large chemical space spanning an input search space of approximately 884 million compounds from ZINC20 and 199,854 purchasable compounds from the SPECS database is reported.
R. Muthuraj, Manasa Pacharla, Nehal Arvind Kumar et al.· Journal of Computer-Aided Mo...· 0 citations
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