Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

“…111,451,1161 We emphasize that there is no emerging state-of-the art simulation protocol for the aggregation of IDPs, even though coupling allatom simulations with Markov state chain modelling approaches is becoming popular at least on small amyloid peptides and aggregate sizes. 450,1162 Overall, we face multiple key issues such as the force field accuracy and the sampling and concentration bottleneck over a wide range of time scales towards efficient elucidation of IDPs aggregation kinetics and thermodynamics and accurate predictions from atomistic and coarse-grained simulations. Whether combining deep learning and statististical mechanics through Boltzmann generators can help solve the sampling issue for amyloid aggregation remains to be determined.…”

“…The dimer has been subjected to many studies. [439][440][441][442][443][444][445][446][447][448][449][450] Zhang et al 440 combined MD and MC pulling simulations with AFM experiments to obtain models for Aβ42 dimers. First, various structures were generated using MD and then an external force was applied to the Cα atom of the first Cys residue of each monomer to pull them away at a constant speed.…”

“…Combining a hybrid-resolution model and adaptive sampling techniques, Cao et al449 carried out a 2.7 ms simulation in order to investigate the mechanisms of formation of Aβ40 dimers. They then developed a Markov state model (MSM) to characterize transition pathways and related kinetics, finding hairpin-containing and parallel in-register structures resembling fibrils (see recent review450 on the application of MSM to study the aggregation of amyloid peptides). Hairpin-like structures occur through one step nucleation, in which two preformed β-hairpins spanning residues 16−35 occasionally associate, preserving the β-hairpin conformation.…”

Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor

“…As a result of computation and algorithmic promotion, MD simulations have become an important source of complementary information in crystallography and a primary tool for mechanism research [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] . Furthermore, MD simulations in combination with Markov state models (MSM) have widely applied to explore the thermodynamics and kinetics of biomolecules [66] , [67] and to investigate a slew of biophysical problems, such as protein folding [68] , allosteric regulation [69] , and molecular mechanism of conformational transition [70] .…”

Slow Escape from a Helical Misfolded State of the Pore-Forming Toxin Cytolysin A

“…On the one hand, experimental techniques are now able to monitor folding kinetics on shorter and shorter time scales, down to the folding speed limit in the microsecond range; 11 on the other hand, simulations have extended their reach to longer and longer time scales, into the microsecond and millisecond range even for all-atom molecular dynamics. 12 − 15 In contrast, the detailed folding mechanisms of larger proteins are still challenging to investigate, since the relevant time scales can extend to seconds, minutes, or even longer, often owing to the population of partially folded intermediates, 16 which can be prone to misfolding and aggregation. 17 , 18 Consequently, many large or multimeric proteins do not refold reversibly after dilution from denaturant solutions, and kinetically trapped states and competition with irreversible aggregate formation lead to complex kinetics and complicate quantitative analysis.…”