Computational Explorations in Biomedicine: Unraveling Molecular Dynamics for Cancer, Drug Delivery, and Biomolecular Insights using LAMMPS Simulations

With the rapid advancement of computational techniques, Molecular Dynamics (MD) simulations have emerged as powerful tools in biomedical research, enabling in-depth investigations of biological systems at the atomic level. Among the diverse range of simulation software available, LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) has gained significant recognition for its versatility, scalability, and extensive range of functionalities. This literature review aims to provide a comprehensive overview of the utilization of LAMMPS in the field of biomedical applications. This review begins by outlining the fundamental principles of MD simulations and highlighting the unique features of LAMMPS that make it suitable for biomedical research. Subsequently, a survey of the literature is conducted to identify key studies that have employed LAMMPS in various biomedical contexts, such as protein folding, drug design, biomaterials, and cellular processes. The reviewed studies demonstrate the remarkable contributions of LAMMPS in understanding the behavior of biological macromolecules, investigating drug-protein interactions, elucidating the mechanical properties of biomaterials, and studying cellular processes at the molecular level. Additionally, this review explores the integration of LAMMPS with other computational tools and experimental techniques, showcasing its potential for synergistic investigations that bridge the gap between theory and experiment. Moreover, this review discusses the challenges and limitations associated with using LAMMPS in biomedical simulations, including the parameterization of force fields, system size limitations, and computational efficiency. Strategies employed by researchers to mitigate these challenges are presented, along with potential future directions for enhancing LAMMPS capabilities in the biomedical field.Comment: 39 pages- 10 figure

GPU fast multipole method with lambda-dynamics features

A significant and computationally most demanding part of molecular dynamics simulations is the calculation of long-range electrostatic interactions. Such interactions can be evaluated directly by the naïve pairwise summation algorithm, which is a ubiquitous showcase example for the compute power of graphics processing units (GPUS). However, the pairwise summation has O(N^2) computational complexity for N interacting particles; thus, an approximation method with a better scaling is required. Today, the prevalent method for such approximation in the field is particle mesh Ewald (PME). PME takes advantage of fast Fourier transforms (FFTS) to approximate the solution efficiently. However, as the underlying FFTS require all-to-all communication between ranks, PME runs into a communication bottleneck. Such communication overhead is negligible only for a moderate parallelization. With increased parallelization, as needed for high-performance applications, the usage of PME becomes unprofitable. Another PME drawback is its inability to perform constant pH simulations efficiently. In such simulations, the protonation states of a protein are allowed to change dynamically during the simulation. The description of this process requires a separate evaluation of the energies for each protonation state. This can not be calculated efficiently with PME as the algorithm requires a repeated FFT for each state, which leads to a linear overhead with respect to the number of states. For a fast approximation of pairwise Coulombic interactions, which does not suffer from PME drawbacks, the Fast Multipole Method (FMM) has been implemented and fully parallelized with CUDA. To assure the optimal FMM performance for diverse MD systems multiple parallelization strategies have been developed. The algorithm has been efficiently incorporated into GROMACS and subsequently tested to determine the optimal FMM parameter set for MD simulations. Finally, the FMM has been incorporated into GROMACS to allow for out-of-the-box electrostatic calculations. The performance of the single-GPU FMM implementation, tested in GROMACS 2019, achieves about a third of highly optimized CUDA PME performance when simulating systems with uniform particle distributions. However, the FMM is expected to outperform PME at high parallelization because the FMM global communication overhead is minimal compared to that of PME. Further, the FMM has been enhanced to provide the energies of an arbitrary number of titratable sites as needed in the constant-pH method. The extension is not fully optimized yet, but the first results show the strength of the FMM for constant pH simulations. For a relatively large system with half a million particles and more than a hundred titratable sites, a straightforward approach to compute alternative energies requires the repetition of a simulation for each state of the sites. The FMM calculates all energy terms only a factor 1.5 slower than a single simulation step. Further improvements of the GPU implementation are expected to yield even more speedup compared to the actual implementation.2021-11-1

Optimisation of the first principle code Octopus for massive parallel architectures: application to light harvesting complexes

[EN]: Computer simulation has become a powerful technique for assisting scientists in developing novel insights into the basic phenomena underlying a wide variety of complex physical systems. The work reported in this thesis is concerned with the use of massively parallel computers to simulate the fundamental features at the electronic structure level that control the initial stages of harvesting and transfer of solar energy in green plants which initiate the photosynthetic process. Currently available supercomputer facilities offer the possibility of using hundred of thousands of computing cores. However, obtaining a linear speed-up from HPC systems is far from trivial. Thus, great efforts must be devoted to understand the nature of the scientific code, the methods of parallel execution, data communication requirements in multi-process calculations, the efficient use of available memory, etc. This thesis deals with all of these themes, with a clear objective in mind: the electronic structure simulation of complete macro-molecular complexes, namely the Light Harvesting Complex II, with the aim of understanding its physical behaviour. In order to simulate this complex, we have used (with the assistance of the PRACE consortium) some of the most powerful supercomputers in Europe to run Octopus, a scientific software package for Density Functional Theory and TimeDependent Density Functional Theory calculations. Results obtained with Octopus have been analysed in depth in order to identify the main obstacles to optimal scaling using thousands of cores. Many problems have emerged, mainly the poor performance of the Poisson solver, high memory requirements, the transfer of high quantities of complex data structures among processes, and so on. Finally, all of these problems have been overcome, and the new version reaches a very high performance in massively parallel systems. Tests run efficiently up to 128K processors and thus we have been able to complete the largest TDDFT calculations performed to date. At the conclusion of this work it has been possible to study the Light Harvesting Complex II as originally envisioned.[EU]: Konputagailu bidezko simulazioa da, gaur egun, zientzialariek eskura duten tresnarik ahaltsuenetako bat sistema fisiko konplexuen portaera ulertzen saiatzeko. Oinarrizko fenomeno fisiko horiek simulatzeko superkonputagailuak erabili dira tesi honetan aurkezten den lanean. Konkretuki, punta-puntako konputagailuak erabili dira fotosintesiaren lehen urratsak ulertzeko, landare berdeetan eguzki-energiaren xurgatze-prozesua kontrolatzen duen molekula simulatuz. Superkonputazio-zentroek ehunka milaka prozesatze-nukleo dituzten makinak erabiltzeko aukera eskaintzen dute, baina ez da batere erraza azelerazio-faktore linealak lortzea halako konputagailuetan. Hori dela eta, ahalegin handiak egin behar dira, informatikaren ikuspegitik, sistema osoaren ezagutza ahalik eta sakonena lortzeko: kode zientifikoen izaera, beraren exekuzio paraleloen aukerak, prozesuen arteko datu-transmisioaren beharrak, sistemaren memoriaren erabilera eraginkorrena, eta abar. Tesi honek aurreko arazo guztiei aurre egiten die, helburu argi batekin: konplexu makromolekular osoen simulazioa, konkretuki Light Harvesting Complex II sistemaren egitura elektronikoaren simulazioa, beraren portaera fisikoa ulertu ahal izateko. Sistema hori simulatu ahal izateko bidean, Europako superkonputagailu azkarrenak erabili dira (PRACE partzuergoari esker) Octopus software-paketea exekutatzeko, zeina Density Functional Theory eta Time-Dependent Density Functional Theory izeneko teorien araberako simulazio elektronikoak egiten baititu. Lortutako emaitzak sakonki analizatu dira, milaka konputazio-nukleo eraginkorki erabiltzea oztopatzen zuten arazoak aurkitzeko. Problema ugari azaldu dira bide horretan, nagusiki Poisson ebazlearen errendimendu baxua, memoria eskaera handiak, datu-egitura konplexuen kopuru handiko transferentziak, eta abar. Azkenean, problema horiek guztiak ebatzi dira, eta bertsio berriak errendimendu handia lortu du superkonputagailu paraleloetan. Exekuzio eraginkorrak frogatu ahal izan ditugu 128K prozesadorera arte eta, ondorioz, inoizko TDDFT simulaziorik handienak egin ahal izan ditugu. Hala, lan honen amaieran, hasierako helburua bete ahal izan da: Light Harvesting Complex II sistema molekularraren azterketa egitea.University of the Basque Country, UPV/EHU, University of Coimbra, Red Española de Supercomputación (RES), Jülich Supercomputing Centre (JSC), Rechenzentrum Garching, Cineca, Barcelona Supercomputing Center (BSC), CeSViMa, European Research Council Advanced Grant DYNamo (ERC-2010-AdG-267374), Spanish Grant (FIS2013-46159-C3-1-P), Grupos Consolidados UPV/EHU del Gobierno Vasco (IT578-13), Grupos Consolidados UPV/EHU del Gobierno Vasco (IT395-10), European Community FP7 project CRONOS (Grant number 280879-2), COST Actions CM1204 (XLIC) and MP1306 (EUSpec), ALDAPA research group belongs to the Basque Advanced Informatics Laboratory (BAILab) supported by the University of the Basque Country UPV/EHU (grant UFI11/45).Peer Reviewe

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Molecular Dynamics Simulation

Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate ‘first-principles’ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardly—dealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...