To explain drug resistance by computer simulations at the molecular level, we first have to assess the accuracy of theoretical predictions. Herein we report an application of the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) technique to the ranking of binding affinities of the inhibitor saquinavir with the wild type (WT) and three resistant mutants of HIV-1 protease: L90M, G48V, and G48V/L90M. For each ligand-protein complex we report 10 ns of fully unrestrained molecular dynamics (MD) simulations with explicit solvent. We investigate convergence, internal consistency, and model dependency of MM/PBSA ligand binding energies. Converged enthalpy and entropy estimates produce ligand binding affinities within 1.5 kcal/mol of experimental values, with a remarkable level of correlation to the experimentally observed ranking of resistance levels. A detailed analysis of the enthalpic/entropic balance of drug-protease interactions explains resistance in L90M in terms of a higher vibrational entropy than in the WT complex, while G48V disrupts critical hydrogen bonds at the inhibitor's binding site and produces an altered, more unfavorable balance of Coulomb and polar desolvation energies.
The successful application of high throughput molecular simulations to determine biochemical properties would be of great importance to the biomedical community if such simulations could be turned around in a clinically relevant timescale. An important example is the determination of antiretroviral inhibitor efficacy against varying strains of HIV through calculation of drug-protein binding affinities. We describe the Binding Affinity Calculator (BAC), a tool for the automated calculation of HIV-1 protease-ligand binding affinities. The tool employs fully atomistic molecular simulations alongside the well established molecular mechanics Poisson-Boltzmann solvent accessible surface area (MMPBSA) free energy methodology to enable the calculation of the binding free energy of several ligand-protease complexes, including all nine FDA approved inhibitors of HIV-1 protease and seven of the natural substrates cleaved by the protease. This enables the efficacy of these inhibitors to be ranked across several mutant strains of the protease relative to the wildtype. BAC is a tool that utilizes the power provided by a computational grid to automate all of the stages required to compute free energies of binding: model preparation, equilibration, simulation, postprocessing, and data-marshaling around the generally widely distributed compute resources utilized. Such automation enables the molecular dynamics methodology to be used in a high throughput manner not achievable by manual methods. This paper describes the architecture and workflow management of BAC and the function of each of its components. Given adequate compute resources, BAC can yield quantitative information regarding drug resistance at the molecular level within 96 h. Such a timescale is of direct clinical relevance and can assist in decision support for the assessment of patient-specific optimal drug treatment and the subsequent response to therapy for any given genotype.
Patient-specific medical simulation holds the promise of determining tailored medical treatment based on the characteristics of an individual patient (for example, using a genotypic assay of a sequence of DNA). Decision-support systems based on patient-specific simulation can potentially revolutionize the way that clinicians plan courses of treatment for various conditions, ranging from viral infections to arterial abnormalities. Basing medical decisions on the results of simulations that use models derived from data specific to the patient in question means that the effectiveness of a range of potential treatments can be assessed before they are actually administered, preventing the patient from experiencing unnecessary or ineffective treatments. We illustrate the potential for patient-specific simulation by first discussing the scale of the evolving international grid infrastructure that is now available to underpin such applications. We then consider two case studies, one concerned with the treatment of patients with HIV/AIDS and the other addressing neuropathologies associated with the intracranial vasculature. Such patient-specific medical simulations require access to both appropriate patient data and the computational resources on which to perform potentially very large simulations. Computational infrastructure providers need to furnish access to a wide range of different types of resource, typically made available through heterogeneous computational grids, and to institute policies that facilitate the performance of patient-specific simulations on those resources. To support these kinds of simulations, where life and death decisions are being made, computational resource providers must give urgent priority to such jobs, for example by allowing them to pre-empt the queue on a machine and run straight away. We describe systems that enable such priority computing.
The stochastic Liouville formalism (SLE) allows for electronic spin resonance (ESR) spectra to be computed for proteins and lipids, using a stochastic model for the motion of the nitroxide spin probe. The present work reports a molecular dynamics (MD) study of spin-labeled T4 lysozyme, where information extracted from the atomically detailed trajectories is used within the framework of the Liouville equation. Sets of 10 MD trajectories, with two force fields, were produced for each of the T4 mutants N40C and K48C, previously used in ESR experiments. Fluctuations of the local relevant degrees of freedom, order parameters for the spin probe, free induction decay profiles, and finally high-field ESR line shapes were calculated for the two proteins. Sampling probabilities for the nitroxide rotational degrees of freedom are in agreement with the traditional model of Brownian rotational diffusion in a restricting potential. Present results suggest that improved sampling (longer time scale, more averaging over initial conditions) is needed for a reliable multicomponent fitting to experimental spectra from molecular dynamics trajectories.
Several attempts have been made to compute electron paramagnetic resonance (EPR) spectra of biomolecules, using motional models or simulated trajectories to describe dynamics. Ideally, the simulated trajectories should capture "fast" (picosecond) snapshots of spin-probe rotations accurately, while being lengthy enough to ensure a proper Fourier integration of the time-domain signal. It is the interplay of the two criteria that poses computational challenges to the method. In this context, an analysis of the spin-probe and protein conformational sampling and equilibration, with different force fields and with explicit solvent, may be a useful attempt. The present work reports a comparative study of the effect of the molecular dynamics (MD) force field on conformational sampling and equilibration in two spin-labeled T4 lysozyme (T4L) variants, N40C and K48C. Ensembles of 10x 3 ns-trajectories per variant and per force field (OPLS/AMBER and AMBER99) are analyzed for a reliable assessment of convergence and sampling. It is found that subtle site-dependent differences in spin-probe rotations and torsions are more readily captured in the AMBER99 trajectories than in the OPLS/AMBER simulations. On the other hand, sampling and equilibration are found to be better with the OPLS/AMBER force field at equal trajectory lengths.
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