Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

Schwantes, Christian R.; Pande, Vijay S.

doi:10.1021/ct300878a

Cited by 604 publications

(847 citation statements)

References 58 publications

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“…The data, however, suggest that second shell coordination does not appear to be important in the ion retention seen in our simulations. Trajectories projected into the solvent-shell space were preprocessed using time-structure independent component analysis (15,16) and were then assigned to a discrete set of states using K-centers clustering. From the state-to-state transition counts, we constructed a MSM.…”

Section: Resultsmentioning

confidence: 99%

Stochastic steps in secondary active sugar transport

Adelman

Ghezzi

Bisignano

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Secondary active transporters, such as those that adopt the leucinetransporter fold, are found in all domains of life, and they have the unique capability of harnessing the energy stored in ion gradients to accumulate small molecules essential for life as well as expel toxic and harmful compounds. How these proteins couple ion binding and transport to the concomitant flow of substrates is a fundamental structural and biophysical question that is beginning to be answered at the atomistic level with the advent of high-resolution structures of transporters in different structural states. Nonetheless, the dynamic character of the transporters, such as ion/substrate binding order and how binding triggers conformational change, is not revealed from static structures, yet it is critical to understanding their function. Here, we report a series of molecular simulations carried out on the sugar transporter vSGLT that lend insight into how substrate and ions are released from the inward-facing state of the transporter. Our simulations reveal that the order of release is stochastic. Functional experiments were designed to test this prediction on the human homolog, hSGLT1, and we also found that cytoplasmic release is not ordered, but we confirmed that substrate and ion binding from the extracellular space is ordered. Our findings unify conflicting published results concerning cytoplasmic release of ions and substrate and hint at the possibility that other transporters in the superfamily may lack coordination between ions and substrate in the inward-facing state.T ransport of sugar molecules across membranes is virtually ubiquitous in biology, and it is of central importance in human health. The uptake of glucose is especially crucial due to its pivotal role in cellular metabolism and energy production. In mammals, glucose is absorbed in the small intestine and kidney via sodium-dependent glucose transporters (SGLTs), which localize to the apical membrane and concentrate glucose in the epithelia. SGLTs fall into the large leucine-transporter (LeuT) structural family of secondary active transporters that have evolved to concentrate a wide array of substrates across membranes using the energy stored in the Na + electrochemical potential gradient. For symporters in this family, transport occurs by an alternating access mechanism (1) in which the transporter first binds ligands in an outward-facing conformation, and then transitions to an inwardfacing conformation that releases the cargo to the cytoplasm. The order of ion and substrate binding and unbinding is likely tied to the function of the transporter, making it possible to convert the energy stored in the ion gradient into a substrate gradient, and vice versa when these proteins operate in reverse.Extensive biochemical uptake assays and electrophysiological studies of SGLTs have led to a view of Na + /glucose cotransport in which Na + binding precedes sugar binding on the external face of the transporter. Kinetic models adhering to this mechanism satisfactorily account fo...

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Section: Resultsmentioning

confidence: 99%

Stochastic steps in secondary active sugar transport

Adelman

Ghezzi

Bisignano

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…[12][13][14]16,[19][20][21][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Building a MSM involves decomposing the phase space sampled by one or more MD trajectories into a set of discrete states and estimating the (conditional) transition probabilities between each pair of states. However, there are many ways to perform the state decomposition, and the choice of MSM building protocol can introduce subjectivity into the analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimized parameter selection reveals trends in Markov state models for protein folding

Husic

McGibbon

Sultan

et al. 2016

The Journal of Chemical Physics

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119

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As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this analysis by describing a system's states and the transitions between them. A recently established variational theorem for Markov state models now enables modelers to systematically determine the best way to describe a system's dynamics. In the context of the variational theorem, we analyze ultra-long folding simulations for a canonical set of twelve proteins [K. Lindorff-Larsen et al., Science 334, 517 (2011)] by creating and evaluating many types of Markov state models. We present a set of guidelines for constructing Markov state models of protein folding; namely, we recommend the use of cross-validation and a kinetically motivated dimensionality reduction step for improved descriptions of folding dynamics. We also warn that precise kinetics predictions rely on the features chosen to describe the system and pose the description of kinetic uncertainty across ensembles of models as an open issue. Published by AIP Publishing. [http://dx

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“…The appropriate choice of discretization is a topic of ongoing research. 24,31,32 The error incurred by the discretization and by the subsequent approximation of the jump-process as a Markov process can be systematically controlled and evaluated. 7,[33][34][35] a) B. Trendelkamp-Schroer and H. Wu contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Estimation and uncertainty of reversible Markov models

Trendelkamp-Schroer

Paul

et al. 2015

The Journal of Chemical Physics

129

153

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Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference.

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Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

Cited by 604 publications

References 58 publications

Stochastic steps in secondary active sugar transport

Stochastic steps in secondary active sugar transport

Optimized parameter selection reveals trends in Markov state models for protein folding

Estimation and uncertainty of reversible Markov models

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