Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning

Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development

Dynamic Density Functional Theory of Multicomponent Cellular Membranes

We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This framework represents a paradigm shift by enabling simulations that can access cellular length-scales ($\mu$m) and time-scales on the order of seconds, all while maintaining near-fidelity to the underlying MD models. Membrane interactions with RAS, a potentially oncogenic protein, are considered as an application. Simulation results are presented and verified with MD simulations, and implications of this new capability are discussed

Scalable Data-Privatization Threading for Hybrid MPI/OpenMP Parallelization of Molecular Dynamics

Abstract — Calculation of the Coulomb potential in the molecular dynamics code ddcMD has been parallelized based on a hybrid MPI/OpenMP scheme. The explicit pair kernel of the particleparticle/particle-mesh algorithm is multi-threaded using OpenMP, while communication between multicore nodes is handled by MPI. We have designed a load balancing spanning forest (LBSF) partitioning algorithm, which combines: 1) finegrain dynamic load balancing; and 2) minimal memory-footprint data privatization via nucleation-growth allocation. This algorithm reduces the memory requirement for thread-private data from O(np) to O(n + p 1/3 n 2/3)—amounting to 75 % memory saving for p = 16 threads working on n = 8,192 particles, while maintaining the average thread-level load-imbalance less than 5%. Strong-scaling speedup for the kernel is 14.4 with 16-way threading on a four quad-core AMD Opteron node. In addition, our MPI/OpenMP code shows 2.58! and 2.16! speedups over the MPI-only implementation, respectively, for 0.84 and 1.68 million particles systems on 32,768 cores of BlueGene/P

Performance Characteristics of Hardware Transactional Memory for Molecular Dynamics Application on BlueGene/Q: Toward Efficient Multithreading Strategies for Large-Scale Scientific Applications

Abstract—We have investigated the performance characteristics of hardware transactional memory (HTM) on the BlueGene/Q computer in comparison with conventional concurrency control mechanisms, using a molecular dynamics application as an example. Benchmark tests, along with overhead-cost and scalability analysis, quantify relative performance advantages of HTM over other mechanisms. We found that the bookkeeping cost of HTM is high but that the rollback cost is low. We propose transaction fusion and spatially-compact scheduling techniques to reduce the overhead of HTM with minimal programming. A strong scalability benchmark shows that the fused HTM has the shortest runtime among various concurrency control mechanisms without extra memory. Based on the performance characterization, we derive a decision tree in the concurrencycontrol design space for multithreading application. Keywords-Hardware transactional memory; molecular dynamics; BlueGene/Q; multithreading I

LLNL-PROC-400532 Quantum-Based Atomistic Simulation of Metals at Extreme Conditions QUANTUM-BASED ATOMISTIC SIMULATION OF METALS AT EXTREME CONDITIONS

Abstract First-principles generalized pseudopotential theory (GPT) provides a fundamental basis for bridging the quantum-atomistic gap from density-functional quantum mechanics to large scale atomistic simulation in metals and alloys. In directionally-bonded bcc transition metals, advanced generation model GPT or MGPT potentials based on canonical d bands have been developed for Ta, Mo and V and successfully applied to a wide range of thermodynamic and mechanical properties at both ambient and extreme conditions of pressure and temperature, including high-pressure phase transitions, multiphase equation of state; melting and solidification; thermoelasticity; and the atomistic simulation of point defects, dislocations and grain boundaries needed for the multiscale modeling of plasticity and strength. Recent algorithm improvements have also allowed an MGPT implementation beyond canonical bands to achieve increased accuracy, extension to f-electron actinide metals, and high computational speed. A further advance in progress is the development temperature-dependent MGPT potentials that subsume electron-thermal contributions to high-temperature properties

Robust quantum-based interatomic potentials for multiscale modeling in transition metals

ABSTRACT First-principles generalized pseudopotential theory (GPT) provides a fundamental basis for transferable multi-ion interatomic potentials in transition metals and alloys within density-functional quantum mechanics. In the central bcc metals, where multi-ion angular forces are important to materials properties, simplified model GPT or MGPT potentials have been developed based on canonical d bands to allow analytic forms and large-scale atomistic simulations. Robust, advanced-generation MGPT potentials have now been obtained for Ta and Mo and successfully applied to a wide range of structural, thermodynamic, defect and mechanical properties at both ambient and extreme conditions. Selected applications to multiscale modeling discussed here include dislocation core structure and mobility, atomistically informed dislocation dynamics simulations of plasticity, and thermoelasticity and high-pressure strength modeling. Recent algorithm improvements have provided a more general matrix representation of MGPT beyond canonical bands, allowing improved accuracy and extension to f-electron actinide metals, an order of magnitude increase in computational speed for dynamic simulations, and the development of temperature-dependent potentials