Binding free energy prediction in strongly hydrophobic biomolecular systems
文献信息
Landry Charlier, Claude Nespoulous, Sébastien Fiorucci, Serge Antonczak, Jérome Golebiowski
We present a comparison of various computational approaches aiming at predicting the binding free energy in ligand–protein systems where the ligand is located within a highly hydrophobic cavity. The relative binding free energy between similar ligands is obtained by means of the thermodynamic integration (TI) method and compared to experimental data obtained through isothermal titration calorimetry measurements. The absolute free energy of binding prediction was obtained on a similar system (a pyrazine derivative bound to a lipocalin) by TI, potential of mean force (PMF) and also by means of the MMPBSA protocols. Although the TI protocol performs poorly either with an explicit or an implicit solvation scheme, the PMF calculation using an implicit solvation scheme leads to encouraging results, with a prediction of the binding affinity being 2 kcal mol−1 lower than the experimental value. The use of an implicit solvation scheme appears to be well suited for the study of such hydrophobic systems, due to the lack of water molecules within the binding site.
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Physical Chemistry Chemical Physics

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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