Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation

Bowman, Gregory R.

doi:10.1002/jcc.23973

Cited by 57 publications

(76 citation statements)

References 66 publications

(82 reference statements)

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“…15 Therefore, the 6.5 μs of simulation data we collected for each variant should be sufficient to construct a quantitatively predictive map of the native-state ensemble. 17 Analysis of our FAST simulations reveals that M182T prefers to act as an N-terminal capping residue to helix 9. This conclusion comes from quantifying the probabilities of all the different contacts Thr182's side chain can form.…”

Section: Acs Central Sciencementioning

confidence: 85%

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Zimmerman

Hart

Sibbald

et al. 2018

Biophysical Journal

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Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein's stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relative populations. For example, the M182T substitution in TEM β-lactamase, an enzyme that confers antibiotic resistance to bacteria, is stabilizing but the precise mechanism remains unclear. Here, we employ Markov state models (MSMs) to uncover how M182T shifts the distribution of different structures that TEM adopts. We find that M182T stabilizes a helix that is a key component of a domain interface. We then predict the effects of other mutations, including a novel stabilizing mutation, and experimentally test our predictions using a combination of stability measurements, crystallography, NMR, and in vivo measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design. ■ INTRODUCTIONStudying the evolution of antibiotic resistance has provided many insights into how proteins acquire new functions, but the mechanistic basis for how mutations alter a protein's activity and stability often remains unclear. For example, studying how bacteria evolve variants of TEM β-lactamase that confer resistance to new antibiotics by degrading these drugs has revealed that many of the mutations that give rise to new functions are destabilizing. Therefore, it is common for proteins to acquire one or more mutations that alter their function and then to acquire additional mutations that restore stability. 1M182T is one such stabilizing mutation in TEM, and it has appeared in numerous clinical isolates and directed evolution experiments.2−4 This substitution occurs far from the active site ( Figure 1A) and, on its own, has little effect on TEM's activity. It is often called a global suppressor because of its ability to counterbalance the destabilizing effects of a wide variety of other substitutions that do alter TEM's activity.3 Despite over two decades of work on this variant, the mechanism of stabilization by M182T is not understood well enough to predict new stabilizing mutations. Elucidating the mechanism underlying this stabilization would provide a basis for predicting other global suppressors and eventually developing quantitative design principles.A mechanistic understanding of how M182T stabilizes TEM remains elusive because of a lack of methods that provide both a detailed structural model of the relevant species and their relative populations. Spectroscopic studies have revealed that TEM-1, which we will refer to as wild-type TEM, populates at least three states at equilibrium: a native state (N), an intermediate state (I), and an unfolded state (U).5 Introducing the M182T substitution appears to reduce the number of equilibrium states to two. 4 However, there is debate over whether this results from M182T stabilizing the native state or destabilizing the intermediate. Moreove...

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Section: Acs Central Sciencementioning

confidence: 85%

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Zimmerman

Hart

Sibbald

et al. 2018

Biophysical Journal

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“…lack of convergence of time correlation function due to insufficient conformational/configurational sampling. Long time convergence (100's of ns) of simulations are important for statistical agreement with experimental data for processes on a nanosecond timescale (67). Nonetheless, Figure 4A shows a good correlation between the computational and experimental data for the majority of the basic side chains (18 out of 21), for which the root mean squared difference was 0.19.…”

Section: Resultsmentioning

confidence: 99%

Changes in conformational dynamics of basic side chains upon protein–DNA association

et al. 2016

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Basic side chains play major roles in recognition of nucleic acids by proteins. However, dynamic properties of these positively charged side chains are not well understood. In this work, we studied changes in conformational dynamics of basic side chains upon protein–DNA association for the zinc-finger protein Egr-1. By nuclear magnetic resonance (NMR) spectroscopy, we characterized the dynamics of all side-chain cationic groups in the free protein and in the complex with target DNA. Our NMR order parameters indicate that the arginine guanidino groups interacting with DNA bases are strongly immobilized, forming rigid interfaces. Despite the strong short-range electrostatic interactions, the majority of the basic side chains interacting with the DNA phosphates exhibited high mobility, forming dynamic interfaces. In particular, the lysine side-chain amino groups exhibited only small changes in the order parameters upon DNA-binding. We found a similar trend in the molecular dynamics (MD) simulations for the free Egr-1 and the Egr-1–DNA complex. Using the MD trajectories, we also analyzed side-chain conformational entropy. The interfacial arginine side chains exhibited substantial entropic loss upon binding to DNA, whereas the interfacial lysine side chains showed relatively small changes in conformational entropy. These data illustrate different dynamic characteristics of the interfacial arginine and lysine side chains.

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“…67 This combination of software and parameters was selected because it has proven reliable in our past studies of both protein folding 68 and structural fluctuations within folded proteins. 63,69,70 …”

Section: Theory and Methodsmentioning

confidence: 99%

Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS

Singh

Bowman

2017

J. Chem. Theory Comput.

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Allosteric (i.e. long-range) communication within proteins is crucial for many biological processes, such as the activation of signaling cascades in response to specific stimuli. However, the physical basis for this communication remains unclear. Existing computational methods for identifying allostery focus on the role of concerted structural changes, but recent experimental work demonstrates that disorder is also an important factor. Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a protein. To quantify disorder, we draw inspiration from methods for quantifying “dynamic heterogeneity” from chemical physics to classify segments of a dihedral’s time evolution as being in either ordered or disordered regimes. To demonstrate the utility of this approach, we apply CARDS to the Catabolite Activator Protein (CAP), a transcriptional activator that is regulated by Cyclic Adenosine MonoPhosphate (cAMP) binding. We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs). Importantly, CARDS reveals that this coupling is dominated by disorder-mediated correlations, consistent with NMR experiments that establish allosteric coupling between the CBDs occurs without a concerted structural change. CARDS also recapitulates an enhanced role for disorder in the communication between the DNA-Binding Domains (DBDs) and CBDs in the S62F variant of CAP. Finally, we demonstrate that using CARDS to find communication hotspots identifies regions of CAP that are in allosteric communication without foreknowledge of their identities. Therefore, we expect CARDS to be of great utility for both understanding and predicting allostery.

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Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation

Cited by 57 publications

References 66 publications

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models

Changes in conformational dynamics of basic side chains upon protein–DNA association

Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS

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