In situ data analytics and indexing of protein trajectories

A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes

In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.

A survey of algorithms for transforming molecular dynamics data into metadata for in situ analytics based on machine learning methods

This paper presents the survey of three algorithms to transform atomic-level molecular snapshots from molecular dynamics (MD) simulations into metadata representations that are suitable for in situ analytics based on machine learning methods. MD simulations studying the classical time evolution of a molecular system at atomic resolution are widely recognized in the fields of chemistry, material sciences, molecular biology and drug design; these simulations are one of the most common simulations on supercomputers. Next-generation supercomputers will have a dramatically higher performance than current systems, generating more data that needs to be analysed (e.g. in terms of number and length of MD trajectories). In the future, the coordination of data generation and analysis can no longer rely on manual, centralized analysis traditionally performed after the simulation is completed or on current data representations that have been defined for traditional visualization tools. Powerful data preparation phases (i.e. phases in which original row data is transformed to concise and still meaningful representations) will need to proceed data analysis phases. Here, we discuss three algorithms for transforming traditionally used molecular representations into concise and meaningful metadata representations. The transformations can be performed locally. The new metadata can be fed into machine learning methods for runtime in situ analysis of larger MD trajectories supported by high-performance computing. In this paper, we provide an overview of the three algorithms and their use for three different applications: protein-ligand docking in drug design; protein folding simulations; and protein engineering based on analytics of protein functions depending on proteins' three-dimensional structures. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

A Novel Metric to Evaluate In Situ Workflows

Performance evaluation is crucial to understanding the behavior of scientific workflows and efficiently utilizing resources on high-performance computing architectures. In this study, we target an emerging type of workflow, called in situ workflows. Through an analysis of the state-of-the-art research on in situ workflows, we model a theoretical framework that helps characterize such workflows. We further propose a lightweight metric for assessing resource usage efficiency of an in situ workflow execution. By applying this metric to a simple, yet representative, synthetic workflow, we explore two possible scenarios (Idle Simulation and Idle Analyzer) for the execution of real in situ workflows. Experimental results show that there is no substantial difference in the performance of both the in transit placement (analytics on dedicated nodes) and the helper-core configuration (analytics co-allocated with simulation) on our target system.