The interactions among associating (macro)molecules are dynamic, which adds to the complexity of molecular recognition. While ligand flexibility is well accounted for in computational drug design, the effective inclusion of receptor flexibility remains an important challenge. The relaxed complex scheme (RCS) is a promising computational methodology that combines the advantages of docking algorithms with dynamic structural information provided by molecular dynamics (MD) simulations, therefore explicitly accounting for the flexibility of both the receptor and the docked ligands. Here, we briefly review the RCS and discuss new extensions and improvements of this methodology in the context of ligand binding to two example targets: kinetoplastid RNA editing ligase 1 and the W191G cavity mutant of cytochrome c peroxidase. The RCS improvements include its extension to virtual screening, more rigorous characterization of local and global binding effects, and methods to improve its computational efficiency by reducing the receptor ensemble to a representative set of configurations. The choice of receptor ensemble, its influence on the predictive power of RCS, and the current limitations for an accurate treatment of the solvent contributions are also briefly discussed. Finally, we outline potential methodological improvements that we anticipate will assist future development.

We use explicit solvent molecular dynamics simulations to estimate free energy, enthalpy, and entropy changes along the cavity−ligand association coordinate for a set of seven model systems with varying physicochemical properties. Owing to the simplicity of the considered systems we can directly investigate the role of water thermodynamics in molecular recognition. A broad range of thermodynamic signatures is found in which water (rather than cavity or ligand) enthalpic or entropic contributions appear to drive cavity−ligand binding or rejection. The unprecedented, nanoscale picture of hydration thermodynamics can help the interpretation and design of protein−ligand binding experiments. Our study opens appealing perspectives to tackle the challenge of solvent entropy estimation in complex systems and for improving molecular simulation models.

Hydrophobic association is often recognized as being driven by favorable entropic contributions. Here, using explicit solvent molecular dynamics simulations we investigate binding in a model hydrophobic receptor−ligand system which appears, instead, to be driven by enthalpy and opposed by entropy. We use the temperature dependence of the potential of mean force to analyze the thermodynamic contributions along the association coordinate. Relating such contributions to the ongoing changes in system hydration allows us to demonstrate that the overall binding thermodynamics is determined by the expulsion of disorganized water from the receptor cavity. Our model study sheds light on the solvent-induced driving forces for receptor−ligand association of general, transferable relevance for biological systems with poorly hydrated binding sites.

We review recent developments in our understanding of molecular recognition and ligand association, focusing on two major viewpoints: (a) studies that highlight new physical insight into the molecular recognition process and the driving forces determining thermodynamic signatures of binding and (b) recent methodological advances in applications to protein-ligand binding. In particular, we highlight the challenges posed by compensating enthalpic and entropic terms, competing solute and solvent contributions, and the relevance of complex configurational ensembles comprising multiple protein, ligand, and solvent intermediate states. As more complete physics is taken into account, computational approaches increase their ability to complement experimental measurements, by providing a microscopic, dynamic view of ensemble-averaged experimental observables. Physics-based approaches are increasingly expanding their power in pharmacology applications.

Dioxygen (O ₂ ) and other gas molecules have a fundamental role in a variety of enzymatic reactions. However, it is only poorly understood which O ₂ uptake mechanism enzymes employ to promote efficient catalysis and how general this is. We investigated O ₂ diffusion pathways into monooxygenase and oxidase flavoenzymes, using an integrated computational and experimental approach. Enhanced-statistics molecular dynamics simulations reveal spontaneous protein-guided O ₂ diffusion from the bulk solvent to preorganized protein cavities. The predicted protein-guided diffusion paths and the importance of key cavity residues for oxygen diffusion were verified by combining site-directed mutagenesis, rapid kinetics experiments, and high-resolution X-ray structures. This study indicates that monooxygenase and oxidase flavoenzymes employ multiple funnel-shaped diffusion pathways to absorb O ₂ from the solvent and direct it to the reacting C4a atom of the flavin cofactor. The difference in O ₂ reactivity among dehydrogenases, monooxygenases, and oxidases ultimately resides in the fine modulation of the local environment embedding the reactive locus of the flavin.

Coarse-grained models are becoming a competitive alternative for modeling processes that occur over time and length scales beyond the reach of fully atomistic molecular simulations. Ideally, coarse-grained models should not only achieve high computational efficiency but also provide accurate predictions and fundamental insight into the role of molecular interactions, the characteristic behavior, and properties of the system they model. In this work we derive a series of monatomic coarse-grained water models mX^REM from the most popular atomistic water models X = TIP3P, SPC/E, TIP4P-Ew, and TIP4P/2005, using the relative entropy minimization (REM) method. Each coarse-grained water molecule is represented by a single particle that interacts through short-ranged anisotropic interactions that encourage the formation of “hydrogen-bonded” structures. We systematically investigate the features of the coarse-grained models in reproducing over 20 structural, dynamic, and thermodynamic properties of the reference atomistic water models—including the existence and locus of the characteristic density anomaly. The mX^REM coarse-grained models reproduce quite faithfully the radial and angular distribution function of water, produce a temperature of maximum density (TMD), and stabilize the ice I crystal. Moreover, the ratio between the TMD and the melting temperature of the crystal in the mX^REM models and liquid–ice equilibrium properties show reasonable agreement with the results of the corresponding atomistic models. The mX^REM models, however, severely underestimate the cohesive energy of the condensed water phases. We investigate which specific limitations of the coarse-grained models arise from the REM methodology, from the monatomic nature of the models, and from the Stillinger–Weber interaction potential form. Our analysis indicates that a small compromise in the accuracy of structural properties can result in a significant increase of the overall accuracy and representability of the coarse-grained water models. We evaluate the accuracy of the atomistic and the monatomic anisotropic coarse-grained water models, including the mW water model, in reproducing experimental water properties. We find that mW and mTIP4P/2005^REM score closer to experiment than widely used atomistic water models. We conclude that monatomic models of water with short-range, anisotropic “hydrogen-bonding” three-body interactions can be competitive in accuracy with fully atomistic models for the study of a wide range of properties and phenomena at less than 1/100th of the computational cost.