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HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data

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Abstract

Research on biology has seen significant advances with the use of molecular dynamics (MD) simulations. The MD methodology enables explanation and discovery of molecular mechanisms in a wide range of natural processes and biological systems. The need to readily share the ever-increasing amount of MD data has been hindered by the lack of specialized bioinformatic tools. The difficulty lies in the efficient management of the data, i.e., in sending and processing 3D information for its visualization. In this work, we present HTMoL, a plug-in-free, secure GPU-accelerated web application specifically designed to stream and visualize MD trajectory data on a web browser. Now, individual research labs can publish MD data on the Internet, or use HTMoL to profoundly improve scientific reports by including supplemental MD data in a journal publication. HTMoL can also be used as a visualization interface to access MD trajectories generated on a high-performance computer center directly. Furthermore, the HTMoL architecture can be leveraged with educational efforts to improve learning in the fields of biology, chemistry, and physics.

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Data availability

HTMoL is available free of charge for academic use. All major browsers are supported. Online documentation, available at the HTMoL website http://htmol.tripplab.com, includes instructions for download, installation, configuration, and examples.

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Acknowledgements

The authors want to acknowledge all the persons involved in the review process. Their comments have helped to greatly improve this report and the functionality of the tool.

Funding

This work has been supported by the Consejo Nacional de Ciencia y Tecnología México (Grant Number 132376 to M.C.-T.).

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Correspondence to Mauricio Carrillo-Tripp.

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Carrillo-Tripp, M., Alvarez-Rivera, L., Lara-Ramírez, O.I. et al. HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data. J Comput Aided Mol Des 32, 869–876 (2018). https://doi.org/10.1007/s10822-018-0141-y

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  • DOI: https://doi.org/10.1007/s10822-018-0141-y

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