Package: pygpcca
pyGPCCA - Generalized Perron Cluster Cluster AnalysisGeneralized Perron Cluster Cluster Analysis program to coarse-grain reversible and non-reversible Markov state models.Markov state models (MSM) enable the identification and analysis of metastable states and related kinetics in a
very instructive manner. They are widely used, e.g., to model molecular or cellular kinetics.Common state-of-the-art Markov state modeling methods and tools are very well suited to model reversible processes in
closed equilibrium systems. However, most are not well suited to deal with non-reversible or even non-autonomous
processes of non-equilibrium systems.To overcome this limitation, the Generalized Robust Perron Cluster Cluster Analysis (GPCCA or G-PCCA) was developed.
The GPCCA method implemented in thepyGPCCAprogram readily handles equilibrium as well as non-equilibrium data by
utilizing real Schur vectors instead of eigenvectors.pyGPCCAenables the semiautomatic coarse-graining of transition matrices representing the dynamics of the system
under study. UtilizingpyGPCCA, metastable states as well as cyclic kinetics can be identified and modeled.How to cite pyGPCCAIf you usepyGPCCAor parts of it to model molecular dynamics, e.g.
to coarse-grain protein conformational dynamics, cite [Reuter18] as:@article{Reuter18,author={Reuter, Bernhard and Weber, Marcus and Fackeldey, Konstantin and Röblitz, Susanna and Garcia, Martin E.},title={Generalized Markov State Modeling Method for Nonequilibrium Biomolecular Dynamics:
    Exemplified on Amyloid β Conformational Dynamics Driven by an Oscillating Electric Field},journal={Journal of Chemical Theory and Computation},volume={14},number={7},pages={3579-3594},year={2018},doi={10.1021/acs.jctc.8b00079},note={PMID: 29812922},}If you usepyGPCCAor parts of it in a more general context,
e.g. to model cellular dynamics, cite [Reuter19] as:@article{Reuter19,author={Reuter,Bernhard and Fackeldey,Konstantin and Weber,Marcus },title={Generalized Markov modeling of nonreversible molecular kinetics},journal={The Journal of Chemical Physics},volume={150},number={17},pages={174103},year={2019},doi={10.1063/1.5064530},}Please consider also citing [Reuter22].InstallationWe support multiple ways of installingpyGPCCA. If any problems arise, please consult thetroubleshootingsection in the documentation.CondapyGPCCAis available as aconda packageand can be installed as:conda install -c conda-forge pygpccaThis is the recommended way of installing, since this package also includesPETSc/SLEPclibraries.
We usePETSc/SLEPcinternally to speed up the computation of leading Schur vectors (both are optional).PyPIIn order to installpyGPCCAfromThe Python Package Index, run:pip install pygpcca
# or with libraries utilizing PETSc/SLEPc
pip install pygpcca[slepc]ExamplePlease refer to ourexample usagein the documentation.Key ContributorsBernhard Reuter: lead developer, maintainer.Michal Klein: developer, diverse contributions.Marius Lange: developer, diverse contributions.AcknowledgmentsWe thankMarcus Weberand the Computational Molecular Design (CMD) group at the Zuse Institute Berlin (ZIB)
for the longstanding and productive collaboration in the field of Markov modeling of non-reversible molecular dynamics.
M. Weber, together with Susanna Röblitz and K. Fackeldey, had the original idea to employ Schur
vectors instead of eigenvectors in the coarse-graining of non-reversible transition matrices.Further, we would like to thankFabian Paulfor valuable discussions regarding the sorting of Schur vectors and his
effort to translate the original sorting routine for real Schur forms,SRSchurpublished byJan Brandts,
from MATLAB intoPython code,
M. Weber andAlexander Sikorskifor pointing us toSLEPcfor sorted partial Schur decompositions,
and A. Sikorski for supplying us with ancode exampleand guidance how to interface SLEPc in Python.
The development ofpyGPCCAstarted - based on the originalGPCCAprogram written in MATLAB - at the beginning of
2020 in a fork ofMSMTools, since it was planned to integrate GPCCA into MSMTools at this time.
Due to this, some similarities in structure and code (indicated were evident) can be found.
Further, utility functions found inpygpcca/utils/_utils.pyoriginate from MSMTools.