RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics

Author:

Platero-Rochart Daniel1ORCID,González-Alemán Roy12ORCID,Hernández-Rodríguez Erix W34,Leclerc Fabrice2,Caballero Julio5ORCID,Montero-Cabrera Luis1

Affiliation:

1. Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana , La Habana 10400, Cuba

2. Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay , Gif-sur-Yvette F-91198, France

3. Laboratorio de Bioinformática y Química Computacional (LBQC), Facultad de Medicina, Universidad Católica del Maule , Talca 3460000, Chile

4. Escuela de Química y Farmacia, Facultad de Medicina, Universidad Católica del Maule , Talca 3460000, Chile

5. Departamento de Bioinformática, Facultad de Ingeniería, Centro de Bioinformática, Simulación y Modelado (CBSM), Universidad de Talca , Talca 3460000, Chile

Abstract

Abstract Motivation Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. Results Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. Availability and implementation The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Eiffel Scholarship Program of Excellence of Campus France

Project Hubert Curien-Carlos J. Finlay

Fondo Nacional de Desarrollo Científico y Tecnológico

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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