Development and analysis of Tinker-OpenMM as a GPU-based free energy perturbation engine

The utilization of computational technologies for the lead optimization process is one of the biggest challenges in the computational chemistry field. In this dissertation, I describe the addition of GPU-based absolute and relative free energy calculation methods using polarizable force field AMOEBA to Tinker-OpenMM. I then proceed to test the capabilities of this platform by studying the binding free energy and binding structures of derivatives of the MELK inhibitor IN17. Also, I present the implementation of virial-based pressure control to the Tinker-OpenMM platform that is needed for performing isobaric simulations.Cellular and Molecular Biolog

Force Field Optimization, Advanced Sampling, And Free Energy Methods With Gpu-Optimized Monte Carlo (gomc) Software

In this work, to address the sampling problem for systems at high densities and low temperatures, a generalized identity exchange algorithm is developed for grand canonical Monte Carlo simulations. The algorithm, referred to as Molecular Exchange Monte Carlo (MEMC), is implemented in the GPU-Optimized Monte Carlo (GOMC) software and may be applied to multicomponent systems of arbitrary molecular topology, and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion/deletion of the large molecules, and the pros and cons of each method are discussed. Next, the MEMC method is extended to Gibbs ensemble Monte Carlo (GEMC). The utility of the MEMC method is demonstrated through the calculation of the free energies of transfer of n-alkanes from vapor into liquid 1-octanol, n-hexadecane, and 2,2,4-trimethylpentane, using isobaric-isothermal GEMC simulations. Alternatively, for system with strong inter-molecular interaction (e.g. hydrogen bonds), it’s more efficient to calculate the free energies of transfer, using standard thermodynamic integration (TI) and free energy perturbation (FEP) methods. The TI and FEP free energy calculation methods are implemented in GOMC and utility of these methods are demonstrated by calculating the hydration and solvation free energies of fluorinated 1-octanol, to understand the role of fluorination on the interactions and partitioning of alcohols in aqueous and organic environments. Additionally, using GOMC, a transferable united-atom (UA) force field, based on Mie potentials, is optimized for alkynes to accurately reproduce experimental phase equilibrium properties. The performance of the optimized Mie potential parameters is assessed for 1-alkynes and 2-alkynes using grand canonical histogram-reweighting Monte Carlo simulations. For each compound, vapor-liquid coexistence curves, vapor pressures, heats of vaporization, critical properties, and normal boiling points are predicted and compared to experiment

Computational Study of pKa shift of Aspartate residue in Thioredoxin: Role of Configurational Sampling and Solvent Model

Alchemical free energy calculations are widely used in predicting pKa, and binding free energy calculations in biomolecular systems. These calculations are carried out using either Free Energy Perturbation (FEP) or Thermodynamic Integration (TI). Numerous efforts have been made to improve the accuracy and efficiency of such calculations, especially by boosting conformational sampling. In this paper, we use a technique that enhances the conformational sampling by temperature acceleration of collective variables for alchemical transformations and applies it to the prediction of pKa of the buried Asp 26 residue in thioredoxin protein. We discuss the importance of enhanced sampling in the pKa calculations. The effect of the solvent models in the computed pKa values is also presented.Comment: 29 pages with 13 figure

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Refactoring the UrQMD model for many-core architectures

Ultrarelativistic Quantum Molecular Dynamics is a physics model to describe the transport, collision, scattering, and decay of nuclear particles. The UrQMD framework has been in use for nearly 20 years since its first development. In this period computing aspects, the design of code, and the efficiency of computation have been minor points of interest. Nowadays an additional issue arises due to the fact that the run time of the framework does not diminish any more with new hardware generations. The current development in computing hardware is mainly focused on parallelism. Especially in scientific applications a high order of parallelisation can be achieved due to the superposition principle. In this thesis it is shown how modern design criteria and algorithm redesign are applied to physics frameworks. The redesign with a special emphasise on many-core architectures allows for significant improvements of the execution speed. The most time consuming part of UrQMD is a newly introduced relativistic hydrodynamic phase. The algorithm used to simulate the hydrodynamic evolution is the SHASTA. As the sequential form of SHASTA is successfully applied in various simulation frameworks for heavy ion collisions its possible parallelisation is analysed. Two different implementations of SHASTA are presented. The first one is an improved sequential implementation. By applying a more concise design and evading unnecessary memory copies, the execution time could be reduced to the half of the FORTRAN version’s execution time. The usage of memory could be reduced by 80% compared to the memory needed in the original version. The second implementation concentrates fully on the usage of many-core architectures and deviates significantly from the classical implementation. Contrary to the sequential implementation, it follows the recalculate instead of memory look-up paradigm. By this means the execution speed could be accelerated up to a factor of 460 on GPUs. Additionally a stability analysis of the UrQMD model is presented. Applying metapro- gramming UrQMD is compiled and executed in a massively parallel setup. The resulting simulation data of all parallel UrQMD instances were hereafter gathered and analysed. Hence UrQMD could be proven of high stability to the uncertainty of experimental data. As a further application of modern programming paradigms a prototypical implementa- tion of the worldline formalism is presented. This formalism allows for a direct calculation of Feynman integrals and constitutes therefore an interesting enhancement for the UrQMD model. Its massively parallel implementation on GPUs is examined

Computational Methods in Science and Engineering : Proceedings of the Workshop SimLabs@KIT, November 29 - 30, 2010, Karlsruhe, Germany

In this proceedings volume we provide a compilation of article contributions equally covering applications from different research fields and ranging from capacity up to capability computing. Besides classical computing aspects such as parallelization, the focus of these proceedings is on multi-scale approaches and methods for tackling algorithm and data complexity. Also practical aspects regarding the usage of the HPC infrastructure and available tools and software at the SCC are presented

ATOMISTIC SIMULATIONS OF INTRINSICALLY DISORDERED PROTEIN FOLDING AND DYNAMICS

Intrinsically disordered proteins (IDPs) are crucial in biology and human diseases, necessitating a comprehensive understanding of their structure, dynamics, and interactions. Atomistic simulations have emerged as a key tool for unraveling the molecular intricacies and establishing mechanistic insights into how these proteins facilitate diverse biological functions. However, achieving accurate simulations requires both an appropriate protein force field capable of describing the energy landscape of functionally relevant IDP conformations and sufficient conformational sampling to capture the free energy landscape of IDP dynamics. These factors are fundamental in comprehending potential IDP structures, dynamics, and interactions. I first conducted explicit solvent simulations to assess the performance of two state-of-the-art protein force fields, namely CHARMM36m and a99SB-disp, in capturing the stability of small protein-protein interactions. To evaluate their accuracy, I selected a set of 46 amino acid backbone and side chain pairs with representative configurations and computed the free energy profiles of their interactions. The results demonstrated that CHARMM36m consistently predicted stronger protein-protein interactions compared to a99SB-disp. Notably, the most significant overestimation in CHARMM36m occurred in charged pairs involving Arg and Glu side chains, with an overestimation of up to 2.9 kcal/mol. Through free energy decomposition analysis, I determined that these overestimations were primarily driven by protein-water electrostatic interactions rather than van der Waals (vdW) interactions. Consequently, these findings suggest that careful rebalancing of electrostatic interactions should be considered in the further optimization of protein force fields. In order to enhance the conformational sampling of IDPs, I developed an integrated approach that combines an improved implicit solvent model called Generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) with a multiscale enhanced sampling (MSES) technique. To make this approach more efficient, I implemented it as a standalone OpenMM plugin on Graphics Processing Units (GPUs). The results demonstrated that the GPU-GBMV2/SA model achieved numerical equivalence to the original CPU-GBMV2/SA models, while providing a remarkable ~60x speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins. This significant acceleration greatly facilitated the application of the approach in biomolecular simulations. In addition, I conducted an evaluation of the reliability of GBMV2/SA models in simulating both folded and unfolded proteins. The results revealed that the GBMV2/SA model accurately describes small proteins, but its applicability is limited when it comes to larger proteins such as KID and p53-TAD proteins. This limitation can be attributed to the absence of long-range solute-solvent dispersion interactions in the model. To address this issue, I introduced a comprehensive treatment of nonpolar solvation free energy called GBMV2/NP model. Unfortunately, the GBMV2/NP model exhibited a destabilizing effect on well-folded proteins, particularly larger ones, due to an inaccurate representation of the repulsive solvent accessible surface area (SASA) model caused by the utilization of unphysical van der Waals volume. This observation highlights the need for further improvements in accurately describing the nonpolar term in the model