Electronic polarization effects on membrane translocation of anti-cancer drugs
Literature Information
Atiyeh (Najla) Hosseini, Mohammad Reza Ejtehadi
Free-energy calculations are crucial for investigating biomolecular interactions. However, in theoretical studies, the neglect of electronic polarization can reduce predictive capabilities, specifically for free-energy calculations. To effectively mimick polarization, we explore a Charge Switching (CS) model, aiming to narrow the gap between computational and experimental results. The model requires quantum-level partial charge calculations of the molecule in different environments, combined with atomistic MD simulations. Studying three different anti-cancer drug molecules with three different phospholipid membranes, we show that the method significantly improves agreement with available experimental data. In contrast, using conventional fixed charge atomistic methods, qualitative discrepancies with experiments are observed, and we show that neglecting polarization may lead to an unphysical free energy sign inversion. While the CS method is here applied to anti-cancer drug-membrane translocation, it could be used more generally to study processes considering solvent effects.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












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