Jožica Dolenc

Molecular simulation as an aid to experimentalists

Computer-based molecular simulation techniques are increasingly used to interpret experimental data on biomolecular systems at an atomic level. Direct comparison between experiment and simulation is, however, seldom straightforward. The available experimental data are limited in scope and generally correspond to averages over both time and space. A critical analysis of the various factors that may influence the apparent degree of agreement between the results of simulations and experimentally measured quantities is presented and illustrated using examples from recent literature.

GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories

GROMOS++ is a set of C++ programs for pre- and postprocessing of molecular dynamics simulation trajectories and as such is part of the GROningen MOlecular Simulation software for (bio)molecular simulation. It contains more than 70 programs that can be used to prepare data for the production of molecular simulation trajectories and to analyze these. These programs are reviewed and the various structural, dynamic, and thermodynamic quantities that can be analyzed using time series, correlation functions, and distributions are described together with technical aspects of their implementation in GROMOS. A few examples of the use of GROMOS++ for the analysis of MD trajectories are given. A full list of all GROMOS++ programs, together with an indication of their capabilities, is given in the Appendix .

Molecular dynamics simulations and free energy calculations of netropsin and distamycin binding to an AAAAA DNA binding site

Molecular dynamics simulations have been performed on netropsin in two different charge states and on distamycin binding to the minor groove of the DNA duplex d(CGCGAAAAACGCG)·d(CGCGTTTTTCGCG). The relative free energy of binding of the two non-covalently interacting ligands was calculated using the thermodynamic integration method and reflects the experimental result. From 2 ns simulations of the ligands free in solution and when bound to DNA, the mobility and the hydrogen-bonding patterns of the ligands were studied, as well as their hydration. It is shown that even though distamycin is less hydrated than netropsin, the loss of ligand–solvent interactions is very similar for both ligands. The relative mobilities of the ligands in their bound and free forms indicate a larger entropic penalty for distamycin when binding to the minor groove compared with netropsin, partially explaining the lower binding affinity of the distamycin molecule. The detailed structural and energetic insights obtained from the molecular dynamics simulations allow for a better understanding of the factors determining ligand–DNA binding.

Biomolecular simulation: historical picture and future perspectives

Over the last 30 years, computation based on molecular models is playing an increasingly important role in biology, biological chemistry and biophysics. Since only a very limited number of properties of biomolecular systems are actually accessible to measurement by experimental means, computer simulation complements experiments by providing not only averages, but also distributions and time series of any definable, observable or non-observable, quantity. Biomolecular simulation may be used (i) to interpret experimental data, (ii) to provoke new experiments, (iii) to replace experiments and (iv) to protect intellectual property. Progress over the last 30 years is sketched and perspectives are outlined for the future.

Biomolecular structure refinement using the GROMOS simulation software

For the understanding of cellular processes the molecular structure of biomolecules has to be accurately determined. Initial models can be significantly improved by structure refinement techniques. Here, we present the refinement methods and analysis techniques implemented in the GROMOS software for biomolecular simulation. The methodology and some implementation details of the computation of NMR NOE data, 3J-couplings and residual dipolar couplings, X-ray scattering intensities from crystals and solutions and neutron scattering intensities used in GROMOS is described and refinement strategies and concepts are discussed using example applications. The GROMOS software allows structure refinement combining different types of experimental data with different types of restraining functions, while using a variety of methods to enhance conformational searching and sampling and the thermodynamically calibrated GROMOS force field for biomolecular simulation.

Methods of NMR structure refinement: molecular dynamics simulations improve the agreement with measured NMR data of a C-terminal peptide of GCN4-p1

The C-terminal trigger sequence is essential in the coiled-coil formation of GCN4-p1; its conformational properties are thus of importance for understanding this process at the atomic level. A solution NMR model structure of a peptide, GCN4p16–31, encompassing the GCN4-p1 trigger sequence was proposed a few years ago. Derived using a standard single-structure refinement protocol based on 172 nuclear Overhauser effect (NOE) distance restraints, 14 hydrogen-bond and 11 ϕ torsional-angle restraints, the resulting set of 20 NMR model structures exhibits regular α-helical structure. However, the set slightly violates some measured NOE bounds and does not reproduce all 15 measured 3J(HN-HCα)-coupling constants, indicating that different conformers of GCN4p16–31 might be present in solution. With the aim to resolve structures compatible with all NOE upper distance bounds and 3J-coupling constants, we executed several structure refinement protocols employing unrestrained and restrained molecular dynamics (MD) simulations with two force fields. We find that only configurational ensembles obtained by applying simultaneously time-averaged NOE distance and 3J-coupling constant restraining with either force field reproduce all the experimental data. Additionally, analyses of the simulated ensembles show that the conformational variability of GCN4p16–31 in solution admitted by the available set of 187 measured NMR data is larger than represented by the set of the NMR model structures. The conformations of GCN4p16–31 in solution differ in the orientation not only of the side-chains but also of the backbone. The inconsistencies between the NMR model structures and the measured NMR data are due to the neglect of averaging effects and the inclusion of hydrogen-bond and torsional-angle restraints that have little basis in the primary, i.e. measured NMR data.

Assessment of enveloping distribution sampling to calculate relative free enthalpies of binding for eight netropsin-DNA duplex complexes in aqueous solution.

The performance of enveloping distribution sampling (EDS) simulations to estimate free enthalpy differences associated with seven alchemical transformations of A‐T into G‐C base pairs at the netropsin binding site in the minor groove of a 13‐base pair DNA duplex in aqueous solution is evaluated. It is demonstrated that sufficient sampling can be achieved with a two‐state EDS Hamiltonian even for large perturbations such as the simultaneous transformation of up to three A‐T into three G‐C base pairs. The two parameters required to define the EDS reference state Hamiltonian are obtained automatically using a modified version of a scheme presented in earlier work. The sensitivity of the configurational sampling to a variation of these parameters is investigated in detail. Although for relatively small perturbations, that is, one base pair, the free enthalpy estimate depends only weakly on the EDS parameters, the sensitivity is stronger for the largest perturbation. Yet, EDS offers various convenient measures to evaluate the degree of sampling and thus the reliability of the free enthalpy estimate and appears to be an efficient alternative to the conventional thermodynamic integration methodology to obtain free energy differences for molecular systems.

Molecular dynamics simulations shed light on the enthalpic and entropic driving forces that govern the sequence specific recognition between netropsin and DNA.

With the aim to gain a better understanding of the various driving forces that govern sequence specific DNA minor groove binding, we performed a thermodynamic analysis of netropsin binding to an AT-containing and to a set of six mixed AT/GC-containing binding sequences in the DNA minor groove. The relative binding free energies obtained using molecular dynamics simulations and free energy calculations show significant variations with the binding sequence. While the introduction of a GC base pair in the middle or close to the middle of the binding site is unfavorable for netropsin binding, a GC base pair at the end of the binding site appears to have no negative influence on the binding. The results of the structural and energetic analyses of the netropsin-DNA complexes reveal that the differences in the calculated binding affinities cannot be explained solely in terms of netropsin-DNA hydrogen-bonding or interaction energies. In addition, solvation effects and entropic contributions to the relative binding free energy provide a more complete picture of the various factors determining binding. Analysis of the relative binding entropy indicates that its magnitude is highly sequence-dependent, with the ratio |TDeltaDeltaS|/|DeltaDeltaH| ranging from 0.07 for the AAAGA to 1.7 for the AAGAG binding sequence, respectively.

Probing the Structure and Dynamics of Proteins by Combining Molecular Dynamics Simulations and Experimental NMR Data.

NMR experiments provide detailed structural information about biological macromolecules in solution. However, the amount of information obtained is usually much less than the number of degrees of freedom of the macromolecule. Moreover, the relationships between experimental observables and structural information, such as interatomic distances or dihedral angle values, may be multiple-valued and may rely on empirical parameters and approximations. The extraction of structural information from experimental data is further complicated by the time- and ensemble-averaged nature of NMR observables. Combining NMR data with molecular dynamics simulations can elucidate and alleviate some of these problems, as well as allow inconsistencies in the NMR data to be identified. Here, we use a number of examples from our work to highlight the power of molecular dynamics simulations in providing a structural interpretation of solution NMR data.

Exploring the trigger sequence of the GCN4 coiled-coil: Biased molecular dynamics resolves apparent inconsistencies in NMR measurements

Trigger sequences are indispensable elements for coiled‐coil formation. The monomeric helical trigger sequence of the yeast transcriptional activator GCN4 has been investigated recently using several solution NMR observables including nuclear Overhauser enhancement (NOE) intensities and 3J(HN,HCα)‐coupling constants, and a set of 20 model structures was proposed. Constrained to satisfy the NOE‐derived distance bounds, the NMR model structures do not appear to reproduce all the measured 3J(HN‐HCα)‐coupling constant values, indicating that the α‐helical propensity is not uniform along the GCN4 trigger sequence. A recent methodological study of unrestrained and restrained molecular dynamics (MD) simulations of the GCN4 trigger sequence in solution showed that only MD simulations incorporating time‐averaged NOE distance restraints and instantaneous or local‐elevation 3J‐coupling restraints could satisfy the entire set of the experimental data. In this report, we assess by means of cluster analyses the model structures characteristic of the two simulations that are compatible with the measured data and compare them with the proposed 20 NMR model structures. Striking characteristics of the MD model structures are the variability of the simulated configurations and the indication of entropic stability mediated by the aromatic N‐terminal residues 17Tyr and 18His, which are absent in the set of NMR model structures.