Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory

By William Swope, J. Pitera, F. Suits. 2004

438 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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. Luca Pasquali
4. Andreas Mardt
5. Vijay S. Pande
6. Gregory R. Bowman
7. John D. Chodera
8. Russ B. Altman

In Molecular Biology

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

Citations in the corpus

• Deep Generative Markov State Models (2018, background): “are independent of the lag time τ at which K is estimated [27].”
• Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways (2013, background): “These rates, or implied time scales, should be unchanged when a system is Markovian (11), satisfying the Chapman-Kolmogorov test, and were monitored for 2000, 3000, and 4000 state systems, seen below in Supplementary Figure S5 with MFPTs shown in Supplementary Figure S6.”
• An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (2014, background): “which is often called the ith implied timescale [8, 42].”
• A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems (2012, methodology): “MSMs have been recently used a lot to model molecular dynamics processes, especially in conjunction with large amounts of distributedly simulated trajectories [42, 40, 9, 27, 8, 28].”