Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

A novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) recently emerged and is rapidly spreading in humans, causing COVID-191,2. A key to tackling this pandemic is to understand the receptor recognition mechanism of the virus, which regulates its infectivity, pathogenesis and host range. SARS-CoV-2 and SARS-CoV recognize the same receptor—angiotensin-converting enzyme 2 (ACE2)—in humans3,4. Here we determined the crystal structure of the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 (engineered to facilitate crystallization) in complex with ACE2. In comparison with the SARS-CoV RBD, an ACE2-binding ridge in SARS-CoV-2 RBD has a more compact conformation; moreover, several residue changes in the SARS-CoV-2 RBD stabilize two virus-binding hotspots at the RBD–ACE2 interface. These structural features of SARS-CoV-2 RBD increase its ACE2-binding affinity. Additionally, we show that RaTG13, a bat coronavirus that is closely related to SARS-CoV-2, also uses human ACE2 as its receptor. The differences among SARS-CoV-2, SARS-CoV and RaTG13 in ACE2 recognition shed light on the potential animal-to-human transmission of SARS-CoV-2. This study provides guidance for intervention strategies that target receptor recognition by SARS-CoV-2. The crystal structure of the receptor-binding domain of the SARS-CoV-2 spike in complex with human ACE2, compared with the receptor-binding domain of SARS-CoV, sheds light on the structural features that increase its binding affinity to ACE2.

Structural basis of receptor recognition by SARS-CoV-2.

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Structure and nanostructure in ionic liquids.

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

/pdf/cp2k-an-electronic-structure-and-molecular-dynamics-software-4domuefs67.pdf

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations

We present TRAVIS (“TRajectory Analyzer and VISualizer”), a free program package for analyzing and visualizing Monte Carlo and molecular dynamics trajectories. The aim of TRAVIS is to collect as many analyses as possible in one program, creating a powerful tool and making it unnecessary to use many different programs for evaluating simulations. This should greatly rationalize and simplify the workflow of analyzing trajectories. TRAVIS is written in C++, open-source freeware and licensed under the terms of the GNU General Public License (GPLv3). It is easy to install (platform independent, no external libraries) and easy to use. In this article, we present some of the algorithms that are implemented in TRAVIS - many of them widely known for a long time, but some of them also to appear in literature for the first time. All shown analyses only require a standard MD trajectory as input data.

/pdf/travis-a-free-analyzer-and-visualizer-for-monte-carlo-and-3hzfu1x0j1.pdf

TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories.

We review several methods for the calculation of vibrational spectra from ab initio molecular dynamics (AIMD) simulations and we present a new implementation in the trajectory analyzer TRAVIS. In particular, we show mass-weighted power spectra, infrared spectra, and Raman spectra with corresponding depolarization ratios, which are based on time-correlation functions of velocities, dipole moments, and polarizabilities, respectively. Using the four organic molecules methanol, acetone, nitromethane, and pinacol as test systems, we compare the spectra from AIMD simulations of the isolated molecules in gas phase to static calculations relying on the harmonic approximation and to experimental spectra recorded in a nonpolar solvent. The AIMD approach turns out to give superior results when anharmonicity effects are of particular importance. Using the example of methanol, we demonstrate the application to bulk phase systems, which are not directly accessible by static calculations, but for which the AIMD spectra also provide a very good approximation to experimental data. Finally, we investigate the influence of simulation time and temperature in the AIMD on the resulting spectra.

Computing vibrational spectra from ab initio molecular dynamics

TRAVIS (“Trajectory Analyzer and Visualizer”) is a program package for post-processing and analyzing trajectories from molecular dynamics and Monte Carlo simulations, mostly focused on molecular condensed phase systems. It is an open source free software licensed under the GNU GPL, is platform independent, and does not require any external libraries. Nine years after the original publication of TRAVIS, we highlight some of the recent new functions and features in this article. At the same time, we shortly present some of the underlying algorithms in TRAVIS, which contribute to make trajectory analysis more efficient. Some modern visualization techniques such as Sankey diagrams are also demonstrated. Many analysis functions are implemented, covering structural analyses, dynamical analyses, and functions for predicting vibrational spectra from molecular dynamics simulations. While some of the analyses are known since several decades, others are very recent. For example, TRAVIS has been used to compute the first ab initio predictions in the literature of bulk phase vibrational circular dichroism spectra, bulk phase Raman optical activity spectra, and bulk phase resonance Raman spectra within the last few years.

TRAVIS-A free analyzer for trajectories from molecular simulation.

We present TRAVIS ("TRajectory Analyzer and VISualizer"), a free program package for analyzing and visualizing Monte Carlo and molecular dynamics trajectories [1]. The aim of TRAVIS is to collect as many analyses as possible in one program, creating a powerful tool and making it unnecessary to use many different programs for evaluating simulations. This should greatly rationalize and simplify the workflow of analyzing trajectories. TRAVIS is written in C++, open-source freeware and licensed under the terms of the GNU General Public License (GPLv3). It is easy to install (platform independent, no external libraries) and easy to use. On this poster, we present some of the algorithms that are implemented in TRAVIS - many of them widely known for a long time, but some of them also to appear in literature for the first time. All shown analyses only require a standard MD trajectory as input data.



Figure 1

/pdf/travis-a-free-analyzer-and-visualizer-for-monte-carlo-and-vpbzk8war2.pdf

TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories

The ionic liquid 1-ethyl-3-methylimidazolium acetate [C2C1Im][OAc] shows a great potential to dissolve strongly hydrogen bonded materials, related with the presence of a strong hydrogen bond network in the pure liquid. A first step towards understanding the solvation process is characterising the hydrogen bonding ability of the ionic liquid. The description of hydrogen bonds in ionic liquids is a question under debate, given the complex nature of this media. The purpose of the present article is to rationalise not only the existence of hydrogen bonds in ionic liquids, but also to analyse their influence on the structure of the pure liquid and how the presence of water, an impurity inherent to ionic liquids, affects this type of interaction. We perform an extensive study using ab initio molecular dynamics on the structure of mixtures of the ionic liquid 1-ethyl-3-methylimidazolium acetate with water, at different water contents. Hydrogen bonds are present in the pure liquid, and the presence of water modifies and largely disturbs the hydrogen bond network of the ionic liquid, and also affects the formation of other impurities (carbenes) and the dipole moment of the ions. The use of ab initio molecular dynamics is the recommended tool to explore hydrogen bonding in ionic liquids, as an explicit electronic structure calculation is combined with the study of the condensed phase.

Proton transfer and polarity changes in ionic liquid–water mixtures: a perspective on hydrogen bonds from ab initio molecular dynamics at the example of 1-ethyl-3-methylimidazolium acetate–water mixtures—Part 1

Martin Brehm

Papers

TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories.

Computing vibrational spectra from ab initio molecular dynamics

TRAVIS-A free analyzer for trajectories from molecular simulation.

TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories

Proton transfer and polarity changes in ionic liquid–water mixtures: a perspective on hydrogen bonds from ab initio molecular dynamics at the example of 1-ethyl-3-methylimidazolium acetate–water mixtures—Part 1