Riccardo Baron

The GROMOS software for biomolecular simulation: GROMOS05

We present the latest version of the Groningen Molecular Simulation program package, GROMOS05. It has been developed for the dynamical modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization. An overview of GROMOS05 is given, highlighting features not present in the last major release, GROMOS96. The organization of the program package is outlined and the included analysis package GROMOS++ is described. Finally, some applications illustrating the various available functionalities are presented.

An improved relaxed complex scheme for receptor flexibility in computer-aided drug design

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.

Water in Cavity−Ligand Recognition

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.

How Can Hydrophobic Association Be Enthalpy Driven

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.

Multiple pathways guide oxygen diffusion into flavoenzyme active sites

Dioxygen (O2) and other gas molecules have a fundamental role in a variety of enzymatic reactions. However, it is only poorly understood which O2 uptake mechanism enzymes employ to promote efficient catalysis and how general this is. We investigated O2 diffusion pathways into monooxygenase and oxidase flavoenzymes, using an integrated computational and experimental approach. Enhanced-statistics molecular dynamics simulations reveal spontaneous protein-guided O2 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 O2 from the solvent and direct it to the reacting C4a atom of the flavin cofactor. The difference in O2 reactivity among dehydrogenases, monooxygenases, and oxidases ultimately resides in the fine modulation of the local environment embedding the reactive locus of the flavin.

Molecular Recognition and Ligand Association

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.

Conformational properties of glucose-based disaccharides investigated using molecular dynamics simulations with local elevation umbrella sampling

Explicit-solvent molecular dynamics (MD) simulations of the 11 glucose-based disaccharides in water at 300K and 1bar are reported. The simulations were carried out with the GROMOS 45A4 force-field and the sampling along the glycosidic dihedral angles phi and psi was artificially enhanced using the local elevation umbrella sampling (LEUS) method. The trajectories are analyzed in terms of free-energy maps, stable and metastable conformational states (relative free energies and estimated transition timescales), intramolecular H-bonds, single molecule configurational entropies, and agreement with experimental data. All disaccharides considered are found to be characterized either by a single stable (overwhelmingly populated) state ((1-->n)-linked disaccharides with n=1, 2, 3, or 4) or by two stable (comparably populated and differing in the third glycosidic dihedral angle omega ; gg or gt) states with a low interconversion barrier ((1-->6)-linked disaccharides). Metastable (anti-phi or anti-psi) states are also identified with relative free energies in the range of 8-22 kJ mol(-1). The 11 compounds can be classified into four families: (i) the alpha(1-->1)alpha-linked disaccharide trehalose (axial-axial linkage) presents no metastable state, the lowest configurational entropy, and no intramolecular H-bonds; (ii) the four alpha(1-->n)-linked disaccharides (n=1, 2, 3, or 4; axial-equatorial linkage) present one metastable (anti-psi) state, an intermediate configurational entropy, and two alternative intramolecular H-bonds; (iii) the four beta(1-->n)-linked disaccharides (n=1, 2, 3, or 4; equatorial-equatorial linkage) present two metastable (anti-phi and anti-psi) states, an intermediate configurational entropy, and one intramolecular H-bond; (iv) the two (1-->6)-linked disaccharides (additional glycosidic dihedral angle) present no (isomaltose) or a pair of (gentiobiose) metastable (anti-phi) states, the highest configurational entropy, and no intramolecular H-bonds. The observed conformational preferences appear to be dictated by four main driving forces (ring conformational preferences, exo-anomeric effect, steric constraints, and possible presence of a third glycosidic dihedral angle), leaving a secondary role to intramolecular H-bonding and specific solvation effects. In spite of the weak conformational driving force attributed to solvent-exposed H-bonds in water (highly polar protic solvent), intramolecular H-bonds may still have a significant influence on the physico-chemical properties of the disaccharide by decreasing its hydrophilicity. Along with previous work, the results also complete the suggestion of a spectrum of approximate transition timescales for carbohydrates up to the disaccharide level, namely: approximately 30 ps (hydroxyl groups), approximately 1 ns (free lactol group, free hydroxymethyl groups, glycosidic dihedral angleomega in (1-->6)-linked disaccharides), approximately 10 ns to 2 micros (ring conformation, glycosidic dihedral angles phi and psi). The calculated average values of the glycosidic torsional angles agree well with the available experimental data, providing validation for the force-field and simulation methodology employed.

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti‐influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti‐influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS‐Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40‐ns MD simulations, CS‐Map was utilized to assess the ability of small, solvent‐sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150‐ and 430‐loop regions. Our hot spot analysis provides further support for the feasibility of developing high‐affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain.

Configurational entropy elucidates the role of salt-bridge networks in protein thermostability

Detailed knowledge of how networks of surface salt bridges contribute to protein thermal stability is essential not only to understand protein structure and function but also to design thermostable proteins for industrial applications. Experimental studies investigating thermodynamic stability through measurements of free energy associated with mutational alterations in proteins provide only macroscopic evidence regarding the structure of salt‐bridge networks and assessment of their contribution to protein stability. Using explicit‐solvent molecular dynamics simulations to provide insight on the atomic scale, we investigate here the structural stability, defined in terms of root‐mean‐square fluctuations, of a short polypeptide designed to fold into a stable trimeric coiled coil with a well‐packed hydrophobic core and an optimal number of intra‐ and interhelical surface salt bridges. We find that the increase of configurational entropy of the backbone and side‐chain atoms and decreased pair correlations of these with increased temperature are consistent with nearly constant atom‐positional root‐mean‐square fluctuations, increased salt‐bridge occupancies, and stronger electrostatic interactions in the coiled coil. Thus, our study of the coiled coil suggests a mechanism in which well‐designed salt‐bridge networks could accommodate stochastically the disorder of increased thermal motion to produce thermostability.

Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening

Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range.