New publication is out: “Atomistic insight into the kinetic pathways for Watson–Crick to Hoogsteen transitions in DNA”

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Title: Atomistic insight into the kinetic pathways for Watson-Crick to Hoogsteen transitions in DNA

Authors: Vreede J, Pérez de Alba Ortíz A, Bolhuis PG, and Swenson DWH

Nucleic Acids Research 2019, Vol. 47, No. 21, 11069–11076, DOI: 10.1093/nar/gkz837 (open access)

Synopsis

DNA predominantly contains Watson–Crick (WC) base pairs, but a non-negligible fraction of base pairs are in the Hoogsteen (HG) hydrogen bonding motif at any time. In the HG motif, the purine is “upside down” compared to the WC motif. Two classes of mechanism have been proposed for the transition between these motifs: one where the base pair stays inside the confines of the helical backbone, and one where one base flips outside of the helical backbone before returning in the “upside down” HG conformation. The transitions between WC and HG may play a role in recognition and replication, but are difficult to investigate because they occur quickly, but only rarely. To gain insight into the mechanisms for this process, researchers performed transition path sampling simulations on a model nucleotide sequence in which an adenine-thymine base pair changes from WC to HG, and found that the outside transition was strongly preferred. Simulated rates and free energy differences agree with experiments, the simulations provide highly detailed insights into the mechanisms of this process.

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Pyscal- A python module for structural analysis of atomic environments

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Description

pyscal is a python module for the calculation of local atomic structural environments including Steinhardt’s bond orientational order parameters[1] during post-processing of atomistic simulation data. The core functionality of pyscal is written in C++ with python wrappers using pybind11 which allows for fast calculations and easy extensions in python.

Practical Applications

Steinhardt’s order parameters are widely used for the identification of crystal structures [3]. They are also used to distinguish if an atom is in a solid or liquid environment [4]. pyscal is inspired by the BondOrderAnalysis code, but has since incorporated many additional features and modifications. The pyscal module includes the following functionalities:

  • calculation of Steinhardt’s order parameters and their averaged version [2].
  • links with the Voro++ code, for the calculation of Steinhardt parameters weighted using the face areas of Voronoi polyhedra [3].
  • classification of atoms as solid or liquid [4].
  • clustering of particles based on a user defined property.
  • methods for calculating radial distribution functions, Voronoi volumes of particles, number of vertices and face area of Voronoi polyhedra, and coordination numbers.

Background information

See the application documentation for full details. A paper about pyscal is also available in Ref. [5].

The utilisation of Dask within the project came about as a result of the E-CAM High Throughput Computing ESDW held in Turin in 2018 and 2019.

The software module was developed by Sarath Menon, Grisell Díaz Leines and Jutta Rogal, and is under a GNU General Public License v3.0.

References

[1] Steinhardt, P. J., Nelson, D. R., & Ronchetti, M. (1983). Physical Review B, 28.

[2] Lechner, W., & Dellago, C. (2008). The Journal of Chemical Physics, 129.

[3] (1, 2) Mickel, W., Kapfer, S. C., Schröder-Turk, G. E., & Mecke, K. (2013). The Journal of Chemical Physics, 138.

[4] (1, 2) Auer, S., & Frenkel, D. (2005). Advances in Polymer Science, 173.

[5] Menon, S., Díaz Leines, G., & Rogal, J.(2019). pyscal: A python module for structural analysis of atomic environments. Journal of Open Source Software, 4(43), 1824

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E-CAM Case Study: The development of the GC-AdResS scheme:

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from smooth coupling

to a direct interface (abrupt)

Dr. Christian Krekeler, Freie Universität Berlin

Abstract

GC-AdResS is a technique  that speeds up computations without loss of accuracy for key system properties by dividing the simulation box into two or more regions having different levels of resolution, for instance a high resolution region where the molecules of the system are treated at an atomistic level of detail, and other regions where molecules are treated at a coarse grained level, and transition regions where a weighted average of the two resolutions is used. The goal of the E-CAM GC-AdResS pilot project was to eliminate  the need of a transition region so as to significantly improve  performance, and to allow much greater flexibility. For example, the  low resolution region can be a particle reservoir (ranging in detail from coarse grained  to ideal gas particles) and a high resolution atomistic region with no transition region, as was needed hitherto.  The only requirement is that the two regions can exchange particles, and that a corresponding “thermodynamic” force is computed self-consistently, which it turns out is very simple to implement.

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PRACE/E-CAM successful collaboration produces task scheduling library for optimising time-scale molecular dynamics simulations

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Challenge

E-CAM is interested in the challenge of bridging up timescales. To study molecular dynamics with atomistic detail, timesteps must be used on the order of a femto-second. Many problems in biological chemistry, materials science, and other ends involve events that only spontaneously occur after a millisecond or longer (for example, biomolecular conformational changes, or nucleation processes). That means that around 1012 time steps would be needed to see a single millisecond-scale event. This is the problem of “rare events” in theoretical and computational chemistry. Modern supercomputers are beginning to make it possible to obtain trajectories long enough to observe some of these processes, but to fully characterize a transition with proper statistics, many examples are needed. And in order to obtain many examples, the same application must be run thousands of times with varying inputs. To manage this kind of computation, a task scheduling library is needed

Solution and benefits

The development of a python library, in collaboration with PRACE. This library builds on top of the scalable analytics framework Dask and enables it to resiliently manage multi-node and multiarchitecture environments. This offers exciting possibilities in the areas of interactive supercomputing and burst supercomputing. A white paper focused on the library was written in collaboration with PRACE and is available here.

The main elements of the mentioned scheduling library are: task de definition, a task scheduling (handled in Python) and task execution (facilitated by the MPI layer). While traditionally an HTC workload is looked down upon in the HPC space, the scientific use case for extreme-scale resources exists and algorithms that require a coordinated approach make efficient libraries that implement this approach increasingly important in the HPC space. The 5 Peta op booster technology of JURECA is an interesting concept with respect to this approach since the offloading approach of heavy computation marries perfectly to the concept outlined here.

Reference

Alan O’Cais, David Swenson, Mariusz Uchronski, & Adam Wlodarczyk. (2019, August 14). Task Scheduling Library for Optimising Time-Scale Molecular Dynamics Simulations. Zenodo. http://doi.org/10.5281/zenodo.3527643

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A Conversation on The Fourth Industrial Revolution: Opportunities & Trends for Particle Based Simulation

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Abstract

In the margins of a recent multiscale simulation workshop a discussion began between a prominent  pharmaceutical industry scientist, and E-CAM and EMMC regarding the unfolding Fourth Industrial Revolution and the role of particle based simulation and statistical methods there.  The impact of simulation  is predicted to become very significant.  This discussion is intended to create awareness of the general public, of how industry 4.0 is initiating in companies, and  how academic research will support that transformation.

Authors: Prof. Pietro Asinari (EMMC and Politecnico di Torino, denoted below as PA) and Dr. Donal MacKernan (E-CAM and University College Dublin, denoted below as  DM) , and a prominent  pharmaceutical industry scientist (name withheld at author’s request as  the view expressed is a personal one, denoted below as  IS)

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Multi-GPU version of DL_MESO_DPD

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This module implements the first version of the DL_MESO_DPD Mesoscale Simulation Package, with multiple NVidia Graphical Processing Units (GPUs).

In this module the main framework of a multi-GPU version of the DL_MESO_DPD code has been developed. The exchange of data between GPUs overlaps with the computation of the forces for the internal cells of each partition (a domain decomposition approach based on the MPI parallel version of DL_MESO_DPD has been followed). The current implementation is a proof of concept and relies on slow transfers of data from the GPU to the host and vice-versa. Faster implementations will be explored in future modules.

Future plans include benchmarking of the code with different data transfer implementations other than the current (trivial) GPU-host-GPU transfer mechanism. These are: of Peer To Peer communication within a node, CUDA-aware MPI, and CUDA-aware MPI with Direct Remote Memory Access (DRMA).

Practical application and exploitation of the code

Dissipative Particle Dynamics (DPD) is routinely used in an industrial context to find out the static and dynamic behaviour of soft-matter systems. Examples include colloidal dispersions, emulsions and other amphiphilic systems, polymer solutions, etc. Such materials are being produced or processed in industries like cosmetics, food, pharmaceutics, biomedicine, etc. Porting the method to GPUs is thus inherently useful in order to provide cheaper calculations.

See more information in the industry success story recently reported by E-CAM.

Software documentation and link to the source code can be found in our E-CAM software Library here.

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E-CAM State of the Art Workshop: CHALLENGES IN MULTIPHASE FLOWS

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We would like to draw your attention to a school cum workshop on

CHALLENGES IN MULTIPHASE FLOWS

that will run on Dec 9-12, 2019, at the Monash University Prato Center,
see http://monash.it/, in Tuscany. The event is an E-CAM state-of-the-art
workshop, and its aim is to focus on computer
simulation methods for multiphase systems and their dynamics, and
their strengths and shortcomings. This is a topic that is relevant in
physics, mathematics, chemistry, and engineering, and we are trying to
bring these communities together for a fruitful exchange. At the same
time, a set of advanced lectures at the school is intended to provide
a solid foundation of background knowledge. For more information (in
particular, the list of Invited Speakers), see the

Main web site for the event

Registration is now open. Regular participants need to pay a fee of
500 Australian Dollars (roughly 300 Euros) for meals etc.; however the
first 25 students (with proven status) who register may attend for free.

DEADLINE for registration and abstract submission is September 22.

Please do not hesitate to contact the organisers (contact information on the main website for the event) if you feel you need more information beyond what is provided on the web.

The Organisers

Burkhard Duenweg, Mainz
Ravi Prakash Jagadeeshan, Melbourne
Ignacio Pagonabarraga, Lausanne

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Integrating LAMMPS with OpenPathSampling

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This module shows how LAMMPS can be used as Molecular Dynamic (MD) engine in OpenPathSampling (OPS) and it also provide a benchmark for the impact of OPS overhead over the MD engine.

Practical application and exploitation of the code

OpenPathSampling uses OpenMM as default engine for calculating the sampled trajectories. Other engines as GROMACS and LAMMPS can be used (despite not yet available in the official release) allowing to exploit different computer architectures like hybrid CPU-GPU and to simulate more complex problems.

In this module we present the source code for the integration of OPS with LAMMPS as well as a benchmark for of a simple test case to show the impact on the performance due to OPS overhead.

Software documentation and link to the source code can be found in our E-CAM software Library here.

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FFTXlib, a rewrite and optimisation of earlier versions of FFT related routines inside QE pre-v6

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FFTXlib is mainly a rewrite and optimisation of earlier versions of FFT related routines inside Quantum ESPRESSO (QE) pre-v6; and finally their replacement. Despite many similarities, current version of FFTXlib dramatically changes the FFT strategy in the parallel execution, from 1D+2D FFT performed in QE pre v6 to a 1D+1D+1D one; to allow for greater flexibility in parallelisation.

Practical application and exploitation of the code

FFTXlib module is a collection of driver routines that allows the user to perform complex 3D fast Fourier transform (FFT) in the context of plane wave based electronic structure software. It contains routines to initialize the array structures, to calculate the desired grid shapes. It imposes underlying size assumptions and provides correspondence maps for indices between the two transform domains.

Once this data structure is constructed, forward or inverse in-place FFT can be performed. For this purpose FFTXlib can either use a local copy of an earlier version of FFTW (a commonly used open source FFT library), or it can also serve as a wrapper to external FFT libraries via conditional compilation using pre-processor directives. It supports both MPI and OpenMP parallelisation technologies.

FFTXlib is currently employed within Quantum Espresso package, a widely used suite of codes for electronic structure calculations and materials modeling in the nanoscale, based on planewave and pseudopotentials.

FFTXlib is also interfaced with “miniPWPP” module that solves the Kohn Sham equations in the basis of planewaves and soon to be released as a part of E-CAM Electronic Structure Library.

Software documentation and link to the source code can be found in our E-CAM software Library here.

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Issue 11 – June 2019

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E-CAM Newsletter of June 2019

Get the latest news from E-CAM, sign up for our quarterly newsletter.

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