High-throughput Binding Affinity Calculations at Extreme Scales

Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High- throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. As such, HTBAC advances the state of the art of binding affinity calculations and protocols

Large-scale binding affinity calculations on commodity compute clouds

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods

New Rosuvastatin Analogs Design for Cardiovascular Disease through Receptor Based Drug Design Methods

Rosuvastatin, is a member of the drug class of statins, used in combination with exercise, diet, and weight-loss to treat high cholesterol and related conditions, and to prevent cardiovascular disease.Rosuvastatin reduces levels of bad cholesterol and triglycerides in the blood. HMG-CoA reductase (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, officially abbreviated HMGCR) is the rate-controlling enzyme (NADH-dependent, NADPH-dependent)of the mevalonate pathway, the metabolic pathway that produces cholesterol and other isoprenoids. Normally in mammalian cells this enzyme is suppressed by cholesterol derived from the internalization and degradation of low density lipoprotein (LDL) via the LDL receptor as well as oxidized species of cholesterol.the potential target protein for Cardiovascular Disease is HMG-CoA REDUCTASE and Rosuvastatin analogs are potential inhibitors for HMG CoA Reductase. New Rosuvastatin analogs will be developed through Receptor Based Drug Designing Methods including binding affinity Calculations. Binding Affinity Calculations will be performed through Genomics, proteomics, MolecularModeling, Protein 3D structural analysis and Molecular Docking Methods The best inhibitor for HMG CoA Reductase will be identified through binding affinity calculation

Extending fragment-based free energy calculations with library Monte Carlo simulation: Annealing in interaction space

Pre-calculated libraries of molecular fragment configurations have previously been used as a basis for both equilibrium sampling (via "library-based Monte Carlo") and for obtaining absolute free energies using a polymer-growth formalism. Here, we combine the two approaches to extend the size of systems for which free energies can be calculated. We study a series of all-atom poly-alanine systems in a simple dielectric "solvent" and find that precise free energies can be obtained rapidly. For instance, for 12 residues, less than an hour of single-processor is required. The combined approach is formally equivalent to the "annealed importance sampling" algorithm; instead of annealing by decreasing temperature, however, interactions among fragments are gradually added as the molecule is "grown." We discuss implications for future binding affinity calculations in which a ligand is grown into a binding site

Calculation of absolute free energy of binding for theophylline and its analogs to RNA aptamer using nonequilibrium work values

The massively parallel computation of absolute binding free energy with a well-equilibrated system (MP-CAFEE) has been developed [H. Fujitani, Y. Tanida, M. Ito, G. Jayachandran, C. D. Snow, M. R. Shirts, E. J. Sorin, and V. S. Pande, J. Chem. Phys. ${\bf 123}$, 084108 (2005)]. As an application, we perform the binding affinity calculations of six theophylline-related ligands with RNA aptamer. Basically, our method is applicable when using many compute nodes to accelerate simulations, thus a parallel computing system is also developed. To further reduce the computational cost, the adequate non-uniform intervals of coupling constant $\lambda$, connecting two equilibrium states, namely bound and unbound, are determined. The absolute binding energies $\Delta G$ thus obtained have effective linear relation between the computed and experimental values. If the results of two other different methods are compared, thermodynamic integration (TI) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) by the paper of Gouda $et al$ [H. Gouda, I. D. Kuntz, D. A. Case, and P. A. Kollman, Biopolymers ${\bf 68}$, 16 (2003)], the predictive accuracy of the relative values $\Delta\Delta G$ is almost comparable to that of TI: the correlation coefficients (R) obtained are 0.99 (this work), 0.97 (TI), and 0.78 (MM-PBSA). On absolute binding energies meanwhile, a constant energy shift of $\sim$ -7 kcal/mol against the experimental values is evident. To solve this problem, several presumable reasons are investigated.Comment: 23 pages including 6 figure

A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane proteins constitute a large portion of the human proteome and perform a variety of important functions as membrane receptors, transport proteins, enzymes, signaling proteins, and more. The computational studies of membrane proteins are usually much more complicated than those of globular proteins. Here we propose a new continuum model for Poisson-Boltzmann calculations of membrane channel proteins. Major improvements over the existing continuum slab model are as follows: 1) The location and thickness of the slab model are fine-tuned based on explicit-solvent MD simulations. 2) The highly different accessibility in the membrane and water regions are addressed with a two-step, two-probe grid labeling procedure, and 3) The water pores/channels are automatically identified. The new continuum membrane model is optimized (by adjusting the membrane probe, as well as the slab thickness and center) to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations. Our optimization also shows that the widely adopted water probe of 1.4 {\AA} for globular proteins is a very reasonable default value for membrane protein simulations. It gives an overall minimum number of inconsistencies between the continuum and explicit representations of water distributions in membrane channel proteins, at least in the water accessible pore/channel regions that we focus on. Finally, we validate the new membrane model by carrying out binding affinity calculations for a potassium channel, and we observe a good agreement with experiment results.Comment: 40 pages, 6 figures, 5 table

Rapid, Accurate, Precise and Reproducible Binding Affinity Calculations using Ensembles of Molecular Dynamics Simulations

The accurate prediction of the binding affinities of ligands to proteins is a major goal in drug discovery and personalised medicine. The use of in silico methods to predict binding affinities has been largely confined to academic research until recently, primarily due to the lack of their reproducibility, as well as unaffordably longer time to solution. In this thesis, I mainly describe the ensemble based molecular dynamics approaches, ESMACS and TIES, that provide a route to reliable predictions of free energies meeting the requirements of speed, accuracy, precision and reliability. The performance of both these methods when applied to a diverse set of protein targets and ligands is reported. The results are in very good agreement with experimental data while the methods are repeatable by construction. Statistical uncertainties of the order of 0.5 kcal/mol or less are achieved. These methods have been further extended to incorporate enhanced sampling techniques based on replica exchange (also known as parallel tempering) to handle situations where conformational sampling is difficult using standard molecular dynamics. A critical assessment of free energy estimators like MBAR has been made for their application in binding affinity prediction. The methodologies described are shown to have a positive impact in the drug design process in the pharmaceutical domain as well as in personalised medicine, with concomitant potential major industrial and societal impact. Finally, our automated workflow, comprising the Binding Affinity Calculator (BAC) together with the FabSim are described. These tools and services help us complete the entire execution in 8 hours or less, depending on the high performance architecture and hardware available

Dual-Topology Hamiltonian-Replica-Exchange Overlap Histogramming Method to Calculate Relative Free Energy Difference in Rough Energy Landscape

A novel overlap histogramming method based on Dual-Topology Hamiltonian-Replica-Exchange simulation technique is presented to efficiently calculate relative free energy difference in rough energy landscape, in which multiple conformers coexist and are separated by large energy barriers. The proposed method is based on the realization that both DT-HERM exchange efficiency and confidence of free energy determination in overlap histogramming method depend on the same criteria: neighboring states' energy derivative distribution overlap. In this paper, we demonstrate this new methodology by calculating free energy difference between amino acids: Leucine and Asparagine, which is an identified chanllenging system for free energy simulations.Comment: 14 pages with 4 figure