Volume-scaled common nearest neighbor clustering algorithm with free-energy hierarchy
Open Access
- 22 February 2021
- journal article
- research article
- Published by AIP Publishing in The Journal of Chemical Physics
- Vol. 154 (8), 084106
- https://doi.org/10.1063/5.0025797
Abstract
The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the algorithms for intermediate steps in the MSM workflow such as featurization and dimensionality reduction have been specifically adapted to MD datasets, conventional clustering methods are generally applied to the discretization step. This work adds to recent efforts to develop specialized density-based clustering algorithms for the Boltzmann-weighted data from MD simulations. We introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is an adapted version of the common nearest neighbor (CNN) algorithm. A major advantage of the proposed algorithm is that the introduced density-based criterion directly links to a free-energy notion via Boltzmann inversion. Such a free-energy perspective allows a straightforward hierarchical scheme to identify conformational clusters at different levels of a generally rugged free-energy landscape of complex molecular systems.Funding Information
- Swiss National Science Foundation (200021-178762)
- ETH Zurich (ETH-34 17-2)
This publication has 48 references indexed in Scilit:
- A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systemsComputer Physics Communications, 2013
- Protein folding kinetics and thermodynamics from atomistic simulationProceedings of the National Academy of Sciences of the United States of America, 2012
- Simple few-state models reveal hidden complexity in protein foldingProceedings of the National Academy of Sciences of the United States of America, 2012
- Efficient Construction of Mesostate Networks from Molecular Dynamics TrajectoriesJournal of Chemical Theory and Computation, 2012
- Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD TrajectoriesJournal of the American Chemical Society, 2011
- Concise Formulas for the Area and Volume of a Hyperspherical CapAsian Journal of Mathematics & Statistics, 2010
- Everything you wanted to know about Markov State Models but were afraid to askMethods, 2010
- Improved side‐chain torsion potentials for the Amber ff99SB protein force fieldProteins, 2010
- Hierarchical Grouping to Optimize an Objective FunctionJournal of the American Statistical Association, 1963
- HIERARCHICAL GROUPING TO OPTIMIZE AN OBJECTIVE FUNCTIONJournal of the American Statistical Association, 1962