A local resampling trick for focused molecular dynamics

We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can lead to incorrect sampling if the selection depends on the configuration in a way that is not accounted for. We avoid this pitfall by introducing appropriate auxiliary variables. This results in an implementation that is closely related to configurational freezing and elastic barrier dynamical freezing. We implement the method and validate that it can be used to supplement conventional molecular dynamics in free energy calculations (absolute hydration and relative binding)

Computer Aided Drug Design : Methods and Applications

Computational tools are useful for studying biological systems with an atomistic level of detail. The focus of this work is to develop and apply methods for the accurate and efficient prediction of the binding free energy between receptors and small molecules. These computational tools may be useful during the rational development of novel pharmaceuticals and also for studying fundamental biological processes. In principle, an atomistic free energy calculation that uses an explicit representation of the solvent is one of the most rigorous methods available to determine the binding free energy between a small molecule and a target protein. In practice, inaccuracies in the force field, which models the interactions between all of the atoms in the system, or inadequate conformational sampling may cause the predicted results to differ from experiment. In this work, new methods were developed to improve conformational sampling during free energy calculations. These methods were applied to protein -ligand binding free energy calculations, and the results show significant improvement in the accuracy of the resulting free energy as a result of improved conformational sampling. These methods are complementary to one another; each may be applied depending the particular details of the system under study and combinations of these methods will be explored in future work. An alternative method that does not rely on explicit sampling of all relevant ligand conformations in a single simulation was also explored. The results suggest that this method is a possible alternative when the ligand conformations are known prior to running the simulation. A deficiency in the force field was also found, which was corrected by using a different charge model leading to improved agreement with experimental results. Free energy calculations were also used to investigate potential inhibitors of the bacterial enzyme Undecaprenyl Diphosphate Synthase, which may be useful in the development of a new antibiotic. This study combined free energy calculations with in silico predictions of molecular physical properties to guide the rational design of potential inhibitors of this enzyme. Thus, this work encompasses the development of new methods and an application of free energy calculations for computer aided drug desig

Accelerated adaptive integration method.

Conformational changes that occur upon ligand binding may be too slow to observe on the time scales routinely accessible using molecular dynamics simulations. The adaptive integration method (AIM) leverages the notion that when a ligand is either fully coupled or decoupled, according to λ, barrier heights may change, making some conformational transitions more accessible at certain λ values. AIM adaptively changes the value of λ in a single simulation so that conformations sampled at one value of λ seed the conformational space sampled at another λ value. Adapting the value of λ throughout a simulation, however, does not resolve issues in sampling when barriers remain high regardless of the λ value. In this work, we introduce a new method, called Accelerated AIM (AcclAIM), in which the potential energy function is flattened at intermediate values of λ, promoting the exploration of conformational space as the ligand is decoupled from its receptor. We show, with both a simple model system (Bromocyclohexane) and the more complex biomolecule Thrombin, that AcclAIM is a promising approach to overcome high barriers in the calculation of free energies, without the need for any statistical reweighting or additional processors

Accelerated Adaptive Integration Method

Conformational changes that occur upon ligand binding may be too slow to observe on the time scales routinely accessible using molecular dynamics simulations. The adaptive integration method (AIM) leverages the notion that when a ligand is either fully coupled or decoupled, according to λ, barrier heights may change, making some conformational transitions more accessible at certain λ values. AIM adaptively changes the value of λ in a single simulation so that conformations sampled at one value of λ seed the conformational space sampled at another λ value. Adapting the value of λ throughout a simulation, however, does not resolve issues in sampling when barriers remain high regardless of the λ value. In this work, we introduce a new method, called Accelerated AIM (AcclAIM), in which the potential energy function is flattened at intermediate values of λ, promoting the exploration of conformational space as the ligand is decoupled from its receptor. We show, with both a simple model system (Bromocyclohexane) and the more complex biomolecule Thrombin, that AcclAIM is a promising approach to overcome high barriers in the calculation of free energies, without the need for any statistical reweighting or additional processors

Improving the Efficiency of Free Energy Calculations in the Amber Molecular Dynamics Package.

Alchemical transformations are widely used methods to calculate free energies. Amber has traditionally included support for alchemical transformations as part of the sander molecular dynamics (MD) engine. Here we describe the implementation of a more efficient approach to alchemical transformations in the Amber MD package. Specifically we have implemented this new approach within the more computational efficient and scalable pmemd MD engine that is included with the Amber MD package. The majority of the gain in efficiency comes from the improved design of the calculation, which includes better parallel scaling and reduction in the calculation of redundant terms. This new implementation is able to reproduce results from equivalent simulations run with the existing functionality, but at 2.5 times greater computational efficiency. This new implementation is also able to run softcore simulations at the λ end states making direct calculation of free energies more accurate, compared to the extrapolation required in the existing implementation. The updated alchemical transformation functionality will be included in the next major release of Amber (scheduled for release in Q1 2014) and will be available at http://ambermd.org, under the Amber license

Protocols utilizing constant pH molecular dynamics to compute pH-dependent binding free energies.

In protein-ligand binding, the electrostatic environments of the two binding partners may vary significantly in bound and unbound states, which may lead to protonation changes upon binding. In cases where ligand binding results in a net uptake or release of protons, the free energy of binding is pH-dependent. Nevertheless, conventional free energy calculations and molecular docking protocols typically do not rigorously account for changes in protonation that may occur upon ligand binding. To address these shortcomings, we present a simple methodology based on Wyman's binding polynomial formalism to account for the pH dependence of binding free energies and demonstrate its use on cucurbit[7]uril (CB[7]) host-guest systems. Using constant pH molecular dynamics and a reference binding free energy that is taken either from experiment or from thermodynamic integration computations, the pH-dependent binding free energy is determined. This computational protocol accurately captures the large pKa shifts observed experimentally upon CB[7]:guest association and reproduces experimental binding free energies at different levels of pH. We show that incorrect assignment of fixed protonation states in free energy computations can give errors of >2 kcal/mol in these host-guest systems. Use of the methods presented here avoids such errors, thus suggesting their utility in computing proton-linked binding free energies for protein-ligand complexes