One quarter to one third of all proteins require metals to function but the description of metal ions in standard force fields is still quite primitive. In this case study and interview an E-CAM project to develop a suitable parameterisation using machine learning is described. The training scheme combines classical simulation with electronic structure calculations to produce a force field comprising standard classical force fields with additional terms for the metal ion-water and metal ion-protein interactions. The approach allows simulations to run as fast as standard molecular dynamics codes, and is suitable for efficient massive parallelism scale-up.
The power of advanced simulation combined with statistical theory , experimental know-how and high performance computing is used to design a protein based molecular switch sensor with remarkable sensitivity and significant industry potential. The sensor technology has applications across commercial markets including diagnostics, immuno-chemistry, and therapeutics.
The publication “Probing spatial locality in ionic liquids with the grand canonical adaptive resolution molecular dynamics technique (GC-AdResS)“ by the Theoretical and Mathematical Physics in Molecular Simulation group of the Freie Universität Berlin, lead by Prof.Luigi Delle Site, E-CAM partner, describes the use of the GC-AdResS molecular dynamics technique to test the spatial locality of the ionic liquid 1-ethyl 3-methyl imidazolium chloride liquid. The main aspect of GC-AdResS is the possibility to couple two simulation boxes together and combine the advantages of classical atomistic simulations with those from coarse gained simulations.
The publication post-print version is open access and can be downloaded directly from the Zenodo repository here. The publisher AIP version can be found at http://aip.scitation.org/doi/10.1063/1.5009066.
E-CAM currently runs a pilot project on the development of the GC-AdResS scheme and one of its goals is to develop a library or recipe with which GC-AdResS can be implemented in any MD Code. The current focus is to adjust the implemented version of GC-AdResS in GROMACS. The long-term goal of this project is to promote and stimulate the community to use it as a tool for multiscale simulations and analysis. More information about this pilot project can be found here.
Title: Probing spatial locality in ionic liquids with the grand canonical adaptive resolution molecular dynamics technique
Authors: B. Shadrack Jabes, C. Krekeler, R. Klein and L. Delle Site
Abstract: We employ the Grand Canonical Adaptive Resolution Simulation (GC-AdResS) molecular dynamics technique to test the spatial locality of the 1-ethyl 3-methyl imidazolium chloride liquid. In GC-AdResS, atomistic details are kept only in an open sub-region of the system while the environment is treated at coarse-grained level; thus, if spatial quantities calculated in such a sub-region agree with the equivalent quantities calculated in a full atomistic simulation, then the atomistic degrees of freedom outside the sub-region play a negligible role. The size of the sub-region fixes the degree of spatial locality of a certain quantity. We show that even for sub-regions whose radius corresponds to the size of a few molecules, spatial properties are reasonably reproduced thus suggesting a higher degree of spatial locality, a hypothesis put forward also by other researchers and that seems to play an important role for the characterization of fundamental properties of a large class of ionic liquids.
Check out our program of events for this year, running from April 2018 to February 2019:
See the workshop details to learn how to apply. E-CAM events are part of the annual CECAM flagship program, and are hosted at the different CECAM Nodes locations.
E-CAM runs three types of events every year:
For their definition see here. If you require any further information contact us at email@example.com
This paper from E-CAM partners working in Scuola Normale Superiore (Pisa, Italy) describes a novel statistical procedure, developed to optimize the parameters of non-bonded force fields of metal ions in soft matter. The paper is open access and can be downloaded directly from ACS’s page at http://pubs.acs.org/doi/10.1021/acs.jctc.7b00779.
This work was performed in the context of the E-CAM pilot project on Quantum Mechanical Parameterisation of Metal Ions in Proteins, which is a collaboration with BiKi Technologies. The list of software modules associated to the pilot project (and this publication) can be found here.
Title: Force Field Parametrization of Metal Ions from Statistical Learning Techniques
Authors: Francesco Fracchia, Gianluca Del Frate, Giordano Mancini, Walter Rocchia, and Vincenzo Barone
Abstract: A novel statistical procedure has been developed to optimize the parameters of non-bonded force fields of metal ions in soft matter. The criterion for the optimization is the minimization of the deviations from ab initio forces and energies calculated for model systems. The method exploits the combination of the linear ridge regression and the cross-validation techniques with the di˙erential evolution algorithm. Wide freedom in the choice of the functional form of the force fields is allowed since both linear and non-linear parameters can be optimized. In order to maximize the information content of the data employed in the fitting procedure, the composition of the training set is entrusted to a combinatorial optimization algorithm which maximizes the dissimilarity of the included instances. The methodology has been validated using the force field parametrization of five metal ions (Zn2+, Ni2+, Mg2+, Ca2+, and Na+) in water as test cases.
DL_MESO_DPD, is the Dissipative Particle Dynamics (DPD) code from the mesoscopic simulation package DL_MESO, developed by Dr. Michael Seaton at Daresbury Laboratory (UK). This open source code is available from Science and Technology Facilities Council (STFC) under both academic (free) and commercial (paid) licenses.
The development of a new methodology, known as Accurate NeurAl networK engINe for Molecular Energies (ANAKIN-ME, or ANI for short), is able, it is claimed, to describe the forces in molecules as accurately as density functional theory (DFT), but hundreds of thousands of times faster. This combination of speed and accuracy could allow researchers to tackle problems that were previously impossible, leading to breakthroughs in the arenas of drug discovery and materials science. Details of the method by J. S. Smith O. Isayev and A. E. Roitberg built on earlier work of Michele Parrinello are available in a 2017 publication entitled “ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost” arXiv:1610.08935v4 .
The methodology has been ported to NVIDIA, and will be the subject of a webinar hosted by NVIDIA, University of Florida, University of North Carolina, on 20 September 2017 from 10am-11am PST . Sign up for the webinar here.
E-CAM strongly recommends software developers interested in HPC to attend the webinar series on Best Practices for HPC organized by the IDEAS project in collaboration with the DOE/ASCR computing facilities (ALCF, NERSC, and OLCF), and the Exascale Computing Project (ECP).
Information on the webinars programme available here.
An event to bring together the quantum information and HPC communities to discuss their specific expertise and outline the bridges that will eventually identify: (1) the future role of quantum technologies in scientific fields where HPC is currently dominant; (2) the use of existing HPC platforms to demonstrate the potentialities of future quantum technologies to simulate materials and biological systems.
E-CAM WP leader Sara Bonella and the industrial partner from IBM, Ivano Tavernelli, are co-organizers of this event.
For more information on this workshops that will take place in ETH Zurich 22-24 August 2017 see here.