701 research outputs found
Transferable neural networks for enhanced sampling of protein dynamics
Variational auto-encoder frameworks have demonstrated success in reducing
complex nonlinear dynamics in molecular simulation to a single non-linear
embedding. In this work, we illustrate how this non-linear latent embedding can
be used as a collective variable for enhanced sampling, and present a simple
modification that allows us to rapidly perform sampling in multiple related
systems. We first demonstrate our method is able to describe the effects of
force field changes in capped alanine dipeptide after learning a model using
AMBER99. We further provide a simple extension to variational dynamics encoders
that allows the model to be trained in a more efficient manner on larger
systems by encoding the outputs of a linear transformation using time-structure
based independent component analysis (tICA). Using this technique, we show how
such a model trained for one protein, the WW domain, can efficiently be
transferred to perform enhanced sampling on a related mutant protein, the GTT
mutation. This method shows promise for its ability to rapidly sample related
systems using a single transferable collective variable and is generally
applicable to sets of related simulations, enabling us to probe the effects of
variation in increasingly large systems of biophysical interest.Comment: 20 pages, 10 figure
A review of High Performance Computing foundations for scientists
The increase of existing computational capabilities has made simulation
emerge as a third discipline of Science, lying midway between experimental and
purely theoretical branches [1, 2]. Simulation enables the evaluation of
quantities which otherwise would not be accessible, helps to improve
experiments and provides new insights on systems which are analysed [3-6].
Knowing the fundamentals of computation can be very useful for scientists, for
it can help them to improve the performance of their theoretical models and
simulations. This review includes some technical essentials that can be useful
to this end, and it is devised as a complement for researchers whose education
is focused on scientific issues and not on technological respects. In this
document we attempt to discuss the fundamentals of High Performance Computing
(HPC) [7] in a way which is easy to understand without much previous
background. We sketch the way standard computers and supercomputers work, as
well as discuss distributed computing and discuss essential aspects to take
into account when running scientific calculations in computers.Comment: 33 page
Machine learning in the analysis of biomolecular simulations
Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.Peer reviewe
Computational Methods in Science and Engineering : Proceedings of the Workshop SimLabs@KIT, November 29 - 30, 2010, Karlsruhe, Germany
In this proceedings volume we provide a compilation of article contributions equally covering applications from different research fields and ranging from capacity up to capability computing. Besides classical computing aspects such as parallelization, the focus of these proceedings is on multi-scale approaches and methods for tackling algorithm and data complexity. Also practical aspects regarding the usage of the HPC infrastructure and available tools and software at the SCC are presented
Adaptive GPU-accelerated force calculation for interactive rigid molecular docking using haptics
Molecular docking systems model and simulate in silico the interactions of intermolecular binding. Haptics-assisted docking enables the user to interact with the simulation via their sense of touch but a stringent time constraint on the computation of forces is imposed due to the sensitivity of the human haptic system. To simulate high fidelity smooth and stable feedback the haptic feedback loop should run at rates of 500 Hz to 1 kHz. We present an adaptive force calculation approach that can be executed in parallel on a wide range of Graphics Processing Units (GPUs) for interactive haptics-assisted docking with wider applicability to molecular simulations. Prior to the interactive session either a regular grid or an octree is selected according to the available GPU memory to determine the set of interatomic interactions within a cutoff distance. The total force is then calculated from this set. The approach can achieve force updates in less than 2 ms for molecular structures comprising hundreds of thousands of atoms each, with performance improvements of up to 90 times the speed of current CPU-based force calculation approaches used in interactive docking. Furthermore, it overcomes several computational limitations of previous approaches such as pre-computed force grids, and could potentially be used to model receptor flexibility at haptic refresh rates
Simulating molecular docking with haptics
Intermolecular binding underlies various metabolic and regulatory processes of the
cell, and the therapeutic and pharmacological properties of drugs. Molecular docking
systems model and simulate these interactions in silico and allow the study of the
binding process. In molecular docking, haptics enables the user to sense the interaction
forces and intervene cognitively in the docking process. Haptics-assisted docking
systems provide an immersive virtual docking environment where the user can interact
with the molecules, feel the interaction forces using their sense of touch, identify
visually the binding site, and guide the molecules to their binding pose. Despite a
forty-year research e�ort however, the docking community has been slow to adopt this
technology. Proprietary, unreleased software, expensive haptic hardware and limits
on processing power are the main reasons for this. Another signi�cant factor is the
size of the molecules simulated, limited to small molecules.
The focus of the research described in this thesis is the development of an interactive
haptics-assisted docking application that addresses the above issues, and enables
the rigid docking of very large biomolecules and the study of the underlying interactions.
Novel methods for computing the interaction forces of binding on the CPU
and GPU, in real-time, have been developed. The force calculation methods proposed
here overcome several computational limitations of previous approaches, such as precomputed
force grids, and could potentially be used to model molecular
exibility
at haptic refresh rates. Methods for force scaling, multipoint collision response, and
haptic navigation are also reported that address newfound issues, particular to the
interactive docking of large systems, e.g. force stability at molecular collision. The
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result is a haptics-assisted docking application, Haptimol RD, that runs on relatively
inexpensive consumer level hardware, (i.e. there is no need for specialized/proprietary
hardware)
An automated image analysis framework for segmentation and division plane detection of single live Staphylococcus aureus cells which can operate at millisecond sampling time scales using bespoke Slimfield microscopy
Staphylococcus aureus is an important pathogen, giving rise to antimicrobial resistance in cell strains such as Methicillin Resistant S. aureus (MRSA). Here we report an image analysis framework for automated detection and image segmentation of cells in S. aureus cell clusters, and explicit identification of their cell division planes. We use a new combination of several existing analytical tools of image analysis to detect cellular and subcellular morphological features relevant to cell division from millisecond time scale sampled images of live pathogens at a detection precision of single molecules. We demonstrate this approach using a fluorescent reporter GFP fused to the protein EzrA that localises to a mid-cell plane during division and is involved in regulation of cell size and division. This image analysis framework presents a valuable platform from which to study candidate new antimicrobials which target the cell division machinery, but may also have more general application in detecting morphologically complex structures of fluorescently labelled proteins present in clusters of other types of cells
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