Progress and challenges in the automated construction of Markov state models for full protein systems.

By Gregory R. Bowman, Kyle A. Beauchamp, G. Boxer, Vijay S. Pande. Journal of Chemical Physics, 2009

390 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/5d310f49cd92b2b3a0c716592f6903fd9deb55c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Frank No'e
2. Hao Wu
3. Fabian Paul
4. Luca Pasquali
5. Andreas Mardt
6. Toni Giorgino
7. John D. Chodera
8. G. Pérez-Hernández

In Molecular Biology

1. Frank No'e
2. Hao Wu
3. Cecilia Clementi
4. Luca Pasquali
5. Andreas Mardt
6. Christof Schutte
7. John D. Chodera
8. Feliks Nuske

In Spectroscopy

1. Frank No'e
2. Hao Wu
3. Luca Pasquali
4. Andreas Mardt
5. Xuhui Huang
6. Siqin Cao
7. Wei Wang
8. Lizhe Zhu

Citations in the corpus

• VAMPnets for deep learning of molecular kinetics (2017, methodology): “A transition matrix or rate matrix describing the transition probabilities or rate between the discrete states at some lag time τ is then estimated [8, 12, 38, 39] (alternatively, a Koopman model can be built after the dimension reduction [27, 28]).”
• Markov state models of biomolecular conformational dynamics (2014, neutral): “Commonly, the transition matrix is only estimated over the largest connected subset of states [24,25] and proceeds via an iterative maximum likelihood estimator that respects detailed balance [26,7].”
• Markov models of molecular kinetics: generation and validation. (2011, methodology): “For macromolecular folding, heavy atom RMSD is a common metric as it can be calculated rapidly and is in principle able to distinguish relatively small differences anywhere in the molecule [39, 49].”
• Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules (2018, methodology): “As the direct symmetrization is not a good approximation when the samplings are statistically biased, a more accurate method is the maximum likelihood estimator (MLE) with the detailed balance constraint imposed.(28,101) Particularly, it is suggested to perform MLE on the maximal ergodic subgraph of”
• A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems (2012, background): “Applications include conformational rearrangements and folding of peptides, proteins and RNA [9, 29, 6, 28, 45, 27].”
• Identification of slow molecular order parameters for Markov model construction. (2013, background): “The TICA problem can easily be solved by subsequently solving two simple eigenvalue problems.”