Development Of Advanced Computer Hardware And Software

National Heart, Lung, And Blood Institute

Investigators

Linked publications, trials & patents

Abstract

HL001052-25. Development of Advanced Computer Hardware and Software A Software Tool for Fast PDB-to-Parameter Generation for Molecular Dynamics Simulations We have developed a stand-alone tool called prepareforleap, implemented in the widely-used and freely-available software CPPTRAJ (which now has over 3k citations) that facilitates the preparation of structures for molecular dynamics simulations with the Amber Biomolecular simulation package. This tool parses a given PDB file and automatically handles disulfides, alternate atom locations, carbohydrates (forms, chirality, and linkages), and will change residue/atom names accordingly for use with Amber force fields. In addition to being a stand-alone program that requires no internet access, the preparation process requires no user intervention. The tool provides a curated PDB as well as the necessary commands (for e.g. bonding carbohydrates and/or creating any disulfide bonds) required to build the final system using Ambers LEaP program. We expect this tool to both help to minimize errors associated with the manual assignment of glycan parameters and considerably decrease the time-to-simulation burden. Quantifying the Effects of Lossy Compression on Energies Calculated from Molecular Dynamics Trajectories Molecular dynamics (MD) simulations can now be simulated on large systems (> 100,000 atoms) for increasingly longer time scales due to recent advances in computer software and hardware, in particular because of the adaptation of MD software to graphics processing units. Modern MD simulations often generate hundreds of gigabytes of data. As a result, there is great interest in being able to store these trajectories in as efficient a way as possible without sacrificing too much precision. We have explored how quantization and compression affects the precision of not only atomic positions (as is typically done), but also the energies calculated from such trajectories, and have compared to a wide variety of new and existing trajectory formats (21 total). We found that in particular bond energies to hydrogen are quite sensitive to precision loss, so energies calculated for systems using flexible water models (which have a large number of such bonds) require a higher precision than those using rigid water models. Based on our testing we have developed a quantization-based compression scheme using NetCDF 4/HDF5 based on the popular Amber NetCDF trajectory format that utilizes the underlying HDF5 framework to allow compression and decompression to be done on-the-fly. This new format compresses to about 66% of the size of the original NetCDF trajectory, has a positional accuracy of 5x10-5 , has an energy root-mean-square error of less than 0.1 kcal/mol. GPU-parallelization of Time-consuming Calculations in CPPTRAJ Over the past decade, graphical processing units (GPUs) have been used to increase the speed of MD simulations by several orders of magnitude. We have undertaken efforts to use GPUs not only for MD, but for the analysis of MD simulation data as well. The radial distribution function (RDF) calculation describes how density varies as a function of distance from a target particle, and is particularly important means of comparing MD simulation results to experimental results (e.g. the oxygen to oxygen RDF of a given water model). This calculation can be particularly time-consuming however since it requires evaluating a large number of distances. We have implemented a version of the RDF calculation in the freely-available analysis software CPPTRAJ on GPUs using the CUDA programming language. This enabled us to get an initial speedup of 2 orders of magnitude over the existing multi-threaded (OpenMP) CPU calculation, with some room for further optimization. In future work we plan to plan to implement more time-consuming analyses onto GPUs (such as the volumetric density map calculation). Enhancements to Cluster Analysis Calculations in CPPTRAJ Cluster analysis is a data-mining technique that can be applied to any collection of data points where a function is available to measure the distance (i.e. similarity) of those points. In the context of MD simulations, this typically means identifying important and unique conformations from trajectories that typically contain thousands to sometimes millions of structures. As such, cluster analysis is a very important tool in the analysis of MD simulations for teasing out the relevant data (and filtering the noise) from extremely large data sets. We have greatly improved the cluster analysis algorithm implemented in the freely-available analysis software CPPTRAJ in several ways. First, we have implemented the quaternion-based root-mean-square deviation (RMSD) calculation of Theobald et al., which improves the speed of calculating pairwise distances via RMSD by 10-20%. Second, we have improved the flexibility of the clustering calculation itself by allowing users to cluster using any data. This means that for example a user can cluster based on RMSD combined with energy and radius of gyration if desired, and weight each of these separately. In addition, clustering calculations can make use of user-provided distances, and clustering calculations can be restarted using previous results, or seeded with user-specified clusters. Taken together, these changes greatly expand the types of clustering analysis possible with CPPTRAJ. Autogeneration of image angles and dihedrals for crystal simulation CHARMM has been enhanced and extended to allow the autogeneration of primary-image angle and dihedral terms for all crystal types. This allows the setup and simulation of complex materials. The new code has been employed within CHARMM-GUI for setup of nanomaterials. Equilibrated lipid bilayer simulations on the GPUs We had earlier developed the Extended-eighth-shell based scheme for MPI-based parallelization of P21 periodic boundary conditions over multiple CPUs. P21 PBC is useful for the equilibration simulation of lipid bilayers as it allows the exchange of lipids between the layers during the course of simulation. In this work, we have implemented a CUDA based version of P21 PBC on the GPU. Direct space nonbonded calculations are modified via the building of the neighbor list in accordance with the half-screw rotation along the X-axis. This implementation will help in the wider adoption of the P21 PBC for lipid bilayer simulations. Multistate simulation on the GPUs We have developed a general framework to drive simulation of a reference state based on parametric weighting of using multiple states. We use a composite design pattern based scheme to handle forces from states defined by multiple PSFs and parameters. Through only python-level scripting, this method can be used for a number of interesting free-energy based methods like enveloping distribution sampling, common-core serial-atom-insertion, constant pH simulation etc. Polyrate and LAT The Polyrate software package sets a reference for the calculation of rate constants using variational transition state theory with multidimensional tunneling. The code has been rewritten to bring it to modern fortran90 standards. The code has been updated to perform least-action tunneling transmission coefficients, allowing to computing tunneling transmission coefficients. Other recent developments include new implementations of the analytical potential energy surfaces for various systems. The new implementation also includes a new interface with other programs, including to electronic structure software packages. For LoBoS hardware advancements for FY22 We have expanded our GPU pool with 20 dual A100 compute nodes featuring AMD Epyc 8 core cpus and 25G Ethernet. Along with a system feature dualing AMD Radeon 6700XT GPUs which we will use for testing code that target AMD GPUs.