MSMExplorer: visualizing Markov state models for biomolecule folding simulations

Cronkite-Ratcliff, Bryce; Pande, Vijay S.

doi:10.1093/bioinformatics/btt051

Cited by 24 publications

(25 citation statements)

References 10 publications

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“…Both the EMMA [38] [https://simtk.org/home/emma] and MSMBuilder [25] [http://msmbuilder.org/] software packages facilitate the construction and validation of Markov state models from molecular simulation data in various trajectory formats. The MSMExplorer software package [https://simtk.org/home/msmexplorer] allows for the visualization of MSMs using graph and network diagrams, scatterplots of state properties, and interactive structure visualization [39]. …”

Section: Recent Theoretical and Methodological Advancesmentioning

confidence: 99%

Markov state models of biomolecular conformational dynamics

Chodera

Noé

2014

Current Opinion in Structural Biology

732

731

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It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges.

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Section: Recent Theoretical and Methodological Advancesmentioning

confidence: 99%

Markov state models of biomolecular conformational dynamics

Chodera

Noé

2014

Current Opinion in Structural Biology

732

731

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“…4 compares the performance of MELD + CPI against unconstrained MD simulations (20). Although this comparison is not an apples-to-apples comparison, because those simulations were not all initiated from fully unfolded states, used a different solvation model, and were targeted at questions of kinetics (21), there are few simulations more relevant for comparison. MELD + CPI is up to five orders of magnitude faster than the estimated folding times from those unrestrained simulations (Fig.…”

Section: Final Physical Refinementmentioning

confidence: 99%

“…Our interest is in understanding how MELD + CPI and REMD help to guide and accelerate folding, rather than trying to understand the physical folding kinetics, which do not make sense in our REMD scheme. We track p-fold values, replica indexes, and rmsd for each of the states identified by MSMBuilder and then use MSMExplorer (21) to visualize the resulting pathways.…”

Section: How Can We Measure the Performance Of Constrained Conformatimentioning

confidence: 99%

Accelerating molecular simulations of proteins using Bayesian inference on weak information

Pérez

MacCallum

Dill

2015

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

102

161

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Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," "to form good secondary structures," or "to seek a compact state." This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3.2 Å from native for all 20 and correctly chooses the native structures (<4 Å) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest.protein folding | molecular dynamics | integrative structural biology | Bayesian inference C omputer modeling is an important source of insights into the properties of protein molecules. There are two main approaches, each with different main areas of applicability: comparative modeling and atomistic molecular dynamics (MD) simulations. Comparative modeling draws inferences from a database of the more than 100,000 known native structures of proteins (1); it is an information-centric approach. A key area of applicability is in predicting the native structures of previously unknown proteins. These methods are often tested in the community-wide blind event for predicting native protein structures, called community assessment of structure prediction (2, 3). In contrast, physics-based atomistic simulations are aimed at computing proper relative populations of the many different states of a system; this type of modeling is an energy-centric approach. Computing proper populations (or, correspondingly, free energies) is essential for elucidating stabilities, motions, and mechanistic actions of protein molecules.Physical simulations offer important advantages in the long run, providing a principled and transferrable basis for understanding properties; the capability to go beyond just native structures alone to dynamics, binding, folding, and mechanisms; applicability where databases are limited, including membrane proteins or other foldable polymers, such as peptoids (4); and extensibility to other temperatures, solvents, and binding conditions, for example. A proper physical model requires a plausible physical energy function that can accurately predict native structures (validation); that app...

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“…Built-in utilities of gromacs such as, g_mindist and g_rmsf, were used to process the trajectory data. Free energy landscape plot was generated using MSMExplorer 84 . All protein visualization was done using PyMOL.…”

Section: Preparation Of Histidine Phosphorylated Myoglobin Standardmentioning

confidence: 99%

Extensive non-canonical phosphorylation in human cells revealed using strong-anion exchange-mediated phosphoproteomics

Hardman

Perkins

Ruan³

et al. 2017

Preprint

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Protein phosphorylation is a ubiquitous post-translational modification (PTM) that regulates all aspects of life. To date, investigation of human cell signalling has focussed on canonical phosphorylation of serine (Ser), threonine (Thr) and tyrosine (Tyr) residues. However, mounting evidence suggests that phosphorylation of histidine also plays a central role in regulating cell biology. Phosphoproteomics workflows rely on acidic conditions for phosphopeptide enrichment, which are incompatible with the analysis of acid-labile phosphorylation such as histidine. Consequently, the extent of non-canonical phosphorylation is likely to be under-estimated. We report an Unbiased Phosphopeptide enrichment strategy based on Strong Anion Exchange (SAX) chromatography (UPAX), which permits enrichment of acid-labile phosphopeptides for characterisation by mass spectrometry. Using this approach, we identify extensive and positional phosphorylation patterns on histidine, arginine, lysine, aspartate and glutamate in human cell extracts, including 310 phosphohistidine and >1000 phospholysine sites of protein modification. Remarkably, the extent of phosphorylation on individual non-canonical residues vastly exceeds that of basal phosphotyrosine. Our study reveals the previously unappreciated diversity of protein phosphorylation in human cells, and opens up avenues for exploring roles of acid-labile phosphorylation in any proteome using mass spectrometry.

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MSMExplorer: visualizing Markov state models for biomolecule folding simulations

Abstract: MSMExplorer is available for download from https://simtk.org/home/msmexplorer. The source code is made available under the GNU Lesser General Public License at https://github.com/SimTk/msmexplorer.

Cited by 24 publications

References 10 publications

Markov state models of biomolecular conformational dynamics

Markov state models of biomolecular conformational dynamics

Accelerating molecular simulations of proteins using Bayesian inference on weak information

Extensive non-canonical phosphorylation in human cells revealed using strong-anion exchange-mediated phosphoproteomics

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