Fast and memory-efficient clustering of long Molecular Dynamics
BitClust: Fast and memory-efficient clustering of long Molecular Dynamics
BitClust´s latest documentation is available here
BitClust is a Python command-line interface (CLI) conceived for fast clustering of relatively long Molecular Dynamics trajectories following Daura's algorithm . Retrieved clusters are roughly equivalent to those reported by VMD's internal command measure cluster but they are computed in a much faster way (see benchmark section for more details).
Nowadays very long simulations are carried on routinely. Enhanced sampling methods like metadynamics, REMD, and accelerated dynamics allow escaping from potential energy minima, returning trajectories that are conformationally sparsed and where every cluster can be potentially important to detect and analyze. Improvements on software designed to address this task is an important field of research.
BitClust offer is a classical tradeoff; RAM for speed. It can calculate all pairwise distances between frames to run a clustering job and then store them in memory instead of recalculating them whenever a cluster is found. It is worth noting that used memory has been deeply optimized by encoding similarity distances as bits (0 if the distance is less equal than a specified threshold, 1 otherwise). This encoding result in a storage reduction as high as 32X compared to similar algorithms that saves the same information as single-precision float values.
BitClust is built on the shoulders of two giants:
MDTraj software that allows a very fast calculation of RMSD pairwise distances between all frames of trajectories in a parallelized fashion and
bitarray third-party python library which offers a memory-efficient data structure of bit-vectors (bit arrays) and a set of bitwise operations that are the very heart of our clustering implementation.
If you make use of BitClust in your scientific work, BitCool and cite it ;)
BitClust is licensed under GNU General Public License v3.0.
 Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236–240.
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