A network of molecular switches controls the activation of the two-component response regulator NtrC

Uncovering the Role of Distal Regions in PDK1 Allosteric Activation

Allosteric regulation is a pivotal mechanism governing a wide array of cellular functions. Essential to this process is a flexible biomolecule allowing distant sites to interact through coordinated or sequential conformational shifts. Phosphoinositide-dependent kinase 1 (PDK1) possesses a conserved allosteric binding site, the PIF-pocket, which regulates the kinase's ATP binding, catalytic activity, and substrate interactions. We elucidated the allosteric mechanisms of PDK1 by comparing conformational ensembles of the kinase bound with different small-molecule allosteric modulators in the PIF-pocket with that of the modulator-free kinase. Analysis of over 48 μs of simulations consistently shows that the allosteric modulators predominantly influence the conformational dynamics of specific distal regions from the PIF-pocket, driving allosteric activation. Furthermore, a recently developed advanced difference contact network community analysis is employed to elucidate allosteric communications. This approach integrates multiple conformational ensembles into a single community network, offering a valuable tool for future studies aimed at identifying function-related dynamics in proteins.

AllohubPy: Detecting Allosteric Signals Through An Information-theoretic Approach

Allosteric regulation is crucial for biological processes like signal transduction, transcriptional regulation, and metabolism, yet the mechanisms and macromolecular properties that govern it are still not well understood. Several methods have been developed over the years to study allosterism through different angles. Among the possible ways to study allosterism, information-theoretic approaches, like AlloHubMat or GSAtools, can be particularly effective due to their use of robust statistics and the possibility to be combined with graph analysis. These methods capture local conformational changes associated with global motions from molecular dynamics simulations through the use of a Structural Alphabet, which simplifies the complexity of the Cartesian space by reducing the dimensionality down to a string of encoded fragments, representing sets of internal coordinates that still capture the overall conformation changes. In this work, we present "AllohubPy," an improved and standardized methodology of AlloHubMat and GSAtools coded in Python. We analyse the performance, limitations and sampling requirements of AllohubPy by using extensive molecular dynamics simulations of model allosteric systems and apply convergence analysis techniques to estimate result reliability. Additionally, we expand the methodology to use different dimensionality reduction Structural Alphabets, such as the 3DI alphabet, and integrate Protein Language Models (PLMs) to refine allosteric hub communication detection by monitoring the detected evolutionary constraints. Overall, AllohubPy expands its preceding methods and simplifies the use and reliability of the method to effectively capture dynamic allosteric motions and residue pathways. AllohubPy is freely available on GitHub (https://github.com/Fraternalilab/AlloHubPy) as a package and as a Jupyter Notebook.

Formation of NifA-PII complex represses ammonium-sensitive nitrogen fixation in diazotrophic proteobacteria lacking NifL

Biological nitrogen fixation catalyzed by nitrogenase contributes greatly to the global nitrogen cycle. Nitrogenase expression is subject to regulation in response to nitrogen availability. However, the mechanism through which the transcriptional activator NifA regulates nitrogenase expression by interacting with PII nitrogen regulatory proteins remains unclear in diazotrophic proteobacteria lacking NifL. Here, we demonstrate that in Rhodopseudomonas palustris grown with ammonium, NifA bound deuridylylated PII proteins to form an inactive NifA-PII complex, thereby inhibiting the expression of nitrogenase. Upon nitrogen limitation, the dissociation of uridylylated PII proteins from NifA resulted in the full restoration of NifA activity, and, simultaneously, uridylylation of the significantly up-regulated PII protein GlnK2 led to the increased expression of NifA in R. palustris. This insight into how NifA interacts with PII proteins and controls nitrogenase expression sets the stage for creating highly efficient diazotrophs, reducing the need for energy-intensive chemical fertilizers and helping to diminish carbon emissions.

High-throughput screening for cell binding and repulsion peptides on multifunctionalized surfaces

The adhesion of cells to the extracellular matrix engages cell surface receptors such as integrins, proteoglycans and other types of cell adhesion molecules such as CD44. To closely examine the determinants of cell adhesion, herein we describe the generation of high-density peptide arrays and test the growth of cells on these multifunctionalized surfaces. The peptide library used consists of over 11,000 different sequences, either random or derived from existing proteins. By applying this screen to SW620 mCherry colorectal cancer cells, we select for peptides with both maximum cell adhesion and maximum cell repulsion. All of these extreme properties are based on unique combinations of amino acids. Here, we identify peptides with maximum cell repulsion on secreted frizzled- and Dickkopf-related proteins. Peptides with strong cell repulsion are found at the poles of the TNF-alpha homotrimer. The formation of cellular patterns on alternating highly repulsive and adhesive peptides are examined. Our screen allows the identification of peptides suitable for biomedical and tissue engineering applications.

Activation Mechanism of Strigolactone Receptors and Its Impact on Ligand Selectivity between Host and Parasitic Plants

Parasitic weeds such as Striga have led to significant losses in agricultural productivity worldwide. These weeds use the plant hormone strigolactone as a germination stimulant. Strigolactone signaling involves substrate hydrolysis followed by a conformational change of the receptor to a "closed" or "active" state that associates with a signaling partner, MAX2/D3. Crystal structures of active and inactive AtD14 receptors have helped elucidate the structural changes involved in activation. However, the mechanism by which the receptor activates remains unknown. The ligand dependence of AtD14 activation has been disputed by mutagenesis studies showing that enzymatically inactive receptors are able to associate with MAX2 proteins. Furthermore, activation differences between strigolactone receptor in Striga, ShHTL7, and AtD14 could contribute to the high sensitivity to strigolactones exhibited by parasitic plants. Using molecular dynamics simulations, we demonstrate that both AtD14 and ShHTL7 could adopt an active conformation in the absence of ligand. However, ShHTL7 exhibits a higher population in the inactive apo state as compared to the AtD14 receptor. We demonstrate that this difference in inactive state population is caused by sequence differences between their D-loops and interactions with the catalytic histidine that prevent full binding pocket closure in ShHTL7. These results indicate that ligand hydrolysis would enhance the active state population by destabilizing the inactive state in ShHTL7 as compared to AtD14. We also show that the mechanism of activation is more concerted in AtD14 than in ShHTL7 and that the main barrier to activation in ShHTL7 is closing of the binding pocket.

Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

A comprehensive mechanism for 5-carboxylcytosine-induced transcriptional pausing revealed by Markov state models

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Role of bacterial RNA polymerase gate opening dynamics in DNA loading and antibiotics inhibition elucidated by quasi-Markov State Model

To initiate transcription, the holoenzyme (RNA polymerase [RNAP] in complex with σ factor) loads the promoter DNA via the flexible loading gate created by the clamp and β-lobe, yet their roles in DNA loading have not been characterized. We used a quasi-Markov State Model (qMSM) built from extensive molecular dynamics simulations to elucidate the dynamics of Thermus aquaticus holoenzyme's gate opening. We showed that during gate opening, β-lobe oscillates four orders of magnitude faster than the clamp, whose opening depends on the Switch 2's structure. Myxopyronin, an antibiotic that binds to Switch 2, was shown to undergo a conformational selection mechanism to inhibit clamp opening. Importantly, we reveal a critical but undiscovered role of β-lobe, whose opening is sufficient for DNA loading even when the clamp is partially closed. These findings open the opportunity for the development of antibiotics targeting β-lobe of RNAP. Finally, we have shown that our qMSMs, which encode non-Markovian dynamics based on the generalized master equation formalism, hold great potential to be widely applied to study biomolecular dynamics.

Toggle switch residues control allosteric transitions in bacterial adhesins by participating in a concerted repacking of the protein core

Critical molecular events that control conformational transitions in most allosteric proteins are ill-defined. The mannose-specific FimH protein of Escherichia coli is a prototypic bacterial adhesin that switches from an 'inactive' low-affinity state (LAS) to an 'active' high-affinity state (HAS) conformation allosterically upon mannose binding and mediates shear-dependent catch bond adhesion. Here we identify a novel type of antibody that acts as a kinetic trap and prevents the transition between conformations in both directions. Disruption of the allosteric transitions significantly slows FimH's ability to associate with mannose and blocks bacterial adhesion under dynamic conditions. FimH residues critical for antibody binding form a compact epitope that is located away from the mannose-binding pocket and is structurally conserved in both states. A larger antibody-FimH contact area is identified by NMR and contains residues Leu-34 and Val-35 that move between core-buried and surface-exposed orientations in opposing directions during the transition. Replacement of Leu-34 with a charged glutamic acid stabilizes FimH in the LAS conformation and replacement of Val-35 with glutamic acid traps FimH in the HAS conformation. The antibody is unable to trap the conformations if Leu-34 and Val-35 are replaced with a less bulky alanine. We propose that these residues act as molecular toggle switches and that the bound antibody imposes a steric block to their reorientation in either direction, thereby restricting concerted repacking of side chains that must occur to enable the conformational transition. Residues homologous to the FimH toggle switches are highly conserved across a diverse family of fimbrial adhesins. Replacement of predicted switch residues reveals that another E. coli adhesin, galactose-specific FmlH, is allosteric and can shift from an inactive to an active state. Our study shows that allosteric transitions in bacterial adhesins depend on toggle switch residues and that an antibody that blocks the switch effectively disables adhesive protein function.

A theoretical study on the signal transduction process of bacterial photoreceptor PpSB1 based on the Markov state model

Light-oxygen-voltage (LOV) domains are blue light sensors and play an important role in signal transduction in many organisms. Generally, LOV domains use chromophores to absorb photons, and then photochemical reactions will occur to convert light energy into chemical energy and transduce it to the main chain of proteins. These reactions can cause conformational rearrangement of proteins, and thus leading to signal transduction. Therefore, it is important to study the signal transduction process of LOV domains for understanding the control mechanism of cellular functions. However, how small photochemical changes in the active sites of the LOV domains lead to large conformational rearrangements of proteins, which in turn lead to signal transduction, has been puzzling us for a long time. Currently, the LOV domains are mainly studied in plants. The signal transduction mechanism of LOV domains in bacteria is still unclear. In this work, the Markov state model (MSM) combined with molecular dynamics (MD) simulations was applied to investigate the signal transduction process of the LOV protein from pseudomonas putida (PpSB1-LOV). The present work will play an important role in understanding the signal transduction mechanism of PpSB1-LOV domains, which may provide theoretical basis for the design and improvement of LOV-based optogenetic tools.

Elucidating molecular mechanisms of functional conformational changes of proteins via Markov state models

Functional conformational changes of proteins can facilitate numerous biological events in cells. The Markov state model (MSM) built from molecular dynamics simulations provide a powerful approach to study them. We here introduce a protocol that is tailor-made for constructing MSMs to study the functional conformational changes of proteins. In this protocol, one of the important steps is to select proper molecular features that can collectively describe the slowest timescales of conformational changes of interest. We recommend spectral oASIS, the modified version of oASIS, as a promising approach for automatic feature selection. Recently developed deep learning methods could also serve efficient approaches for selecting features and finding collective variables. Using DNA repair enzymes and RNA polymerases as examples, we review recent applications of MSMs to elucidate molecular mechanisms of functional conformational changes. Finally, we discuss remaining challenges and future perspectives for constructing MSMs to study functional conformational changes of proteins.

Regulation of Sporangium Formation, Spore Dormancy, and Sporangium Dehiscence by a Hybrid Sensor Histidine Kinase in Actinoplanes missouriensis: Relationship with the Global Transcriptional Regulator TcrA

The rare actinomycete Actinoplanes missouriensis forms terminal sporangia containing a few hundred flagellated spores. In response to water, the sporangia open and release the spores into external environments. The orphan response regulator TcrA functions as a global transcriptional activator during sporangium formation and dehiscence. Here, we report the characterization of an orphan hybrid histidine kinase, HhkA. Sporangia of an hhkA deletion mutant contained many distorted or ectopically germinated spores and scarcely opened to release the spores under sporangium dehiscence-inducing conditions. These phenotypic changes are quite similar to those observed in a tcrA deletion mutant. Comparative RNA sequencing analysis showed that genes controlled by HhkA mostly overlap TcrA-regulated genes. The direct interaction between HhkA and TcrA was suggested by a bacterial two-hybrid assay, but this was not conclusive. The phosphorylation of TcrA using acetyl phosphate as a phosphate donor markedly enhanced its affinity for the TcrA box sequences in the electrophoretic mobility shift assay. Taking these observations together with other results, we proposed that HhkA and TcrA compose a cognate two-component regulatory system, which controls the transcription of the genes involved in many aspects of morphological development, including sporangium formation, spore dormancy, and sporangium dehiscence in A. missouriensisIMPORTANCEActinoplanes missouriensis goes through complex morphological differentiation, including formation of flagellated spore-containing sporangia, sporangium dehiscence, swimming of zoospores, and germination of zoospores to filamentous growth. Although the orphan response regulator TcrA globally activates many genes required for sporangium formation, spore dormancy, and sporangium dehiscence, its partner histidine kinase remained unknown. Here, we analyzed the function of an orphan hybrid histidine kinase, HhkA, and proposed that HhkA constitutes a cognate two-component regulatory system with TcrA. That HhkA and TcrA homologues are highly conserved among the genus Actinoplanes and several closely related rare actinomycetes indicates that this possible two-component regulatory system is employed for complex morphological development in sporangium- and/or zoospore-forming rare actinomycetes.

The impact of cell structure, metabolism and group behavior for the survival of bacteria under stress conditions

Microbes from diverse types of habitats are continuously exposed to external challenges, which may include acidic, alkaline, and toxic metabolites stress as well as nutrient deficiencies. To promote their own survival, bacteria have to rapidly adapt to external perturbations by inducing particular stress responses that typically involve genetic and/or cellular changes. In addition, pathogenic bacteria need to sense and withstand these environmental stresses within a host to establish and maintain infection. These responses can be, in principle, induced by changes in bacterial cell structure, metabolism and group behavior. Bacterial nucleic acids may serve as the core part of the stress response, and the cell envelope and ribosomes protect genetic structures from damage. Cellular metabolism and group behavior, such as quorum sensing system, can play a more important role in resisting stress than we have now found. Since bacteria survival can be only appreciated if we better understand the mechanisms behind bacterial stress response, here we review how morphological and physiological features may lead to bacterial resistance upon exposure to particular stress-inducing factors.

Laplacian score and genetic algorithm based automatic feature selection for Markov State Models in adaptive sampling based molecular dynamics

Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results.

Methods

In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes.

Results

Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.

Tuning the "violin" of protein kinases: The role of dynamics-based allostery

The intricacies of allosteric regulation of protein kinases continue to engage the research community. Allostery, or control from a distance, is seen as a fundamental biomolecular mechanism for proteins. From the traditional methods of conformational selection and induced fit, the field has grown to include the role of protein motions in defining a dynamics-based allosteric approach. Harnessing of these continuous motions in the protein to exert allosteric effects can be defined by a "violin" model that focuses on distributions of protein vibrations as opposed to concerted pathways. According to this model, binding of an allosteric modifier causes global redistribution of dynamics in the protein kinase domain that leads to changes in its catalytic properties. This model is consistent with the "entropy-driven allostery" mechanism proposed by Cooper and Dryden in 1984 and does not require, but does not exclude, any major structural changes. We provide an overview of practical implementation of the violin model and how it stands amidst the other known models of protein allostery. Protein kinases have been described as the biomolecules of interest. © 2019 IUBMB Life, 71(6):685-696, 2019.

SAXS-guided Enhanced Unbiased Sampling for Structure Determination of Proteins and Complexes

Molecular simulations can be utilized to predict protein structure ensembles and dynamics, though sufficient sampling of molecular ensembles and identification of key biologically relevant conformations remains challenging. Low-resolution experimental techniques provide valuable structural information on biomolecule at near-native conditions, which are often combined with molecular simulations to determine and refine protein structural ensembles. In this study, we demonstrate how small angle x-ray scattering (SAXS) information can be incorporated in Markov state model-based adaptive sampling strategy to enhance time efficiency of unbiased MD simulations and identify functionally relevant conformations of proteins and complexes. Our results show that using SAXS data combined with additional information, such as thermodynamics and distance restraints, we are able to distinguish otherwise degenerate structures due to the inherent ambiguity of SAXS pattern. We further demonstrate that adaptive sampling guided by SAXS and hybrid information can significantly reduce the computation time required to discover target structures. Overall, our findings demonstrate the potential of this hybrid approach in predicting near-native structures of proteins and complexes. Other low-resolution experimental information can be incorporated in a similar manner to collectively enhance unbiased sampling and improve the accuracy of structure prediction from simulation.

Suppressor mutations reveal an NtrC-like response regulator, NmpR, for modulation of Type-IV Pili-dependent motility in Myxococcus xanthus

Two-component signaling systems (TCS) regulate bacterial responses to environmental signals through the process of protein phosphorylation. Specifically, sensor histidine kinases (SK) recognize signals and propagate the response via phosphorylation of a cognate response regulator (RR) that functions to initiate transcription of specific genes. Signaling within a single TCS is remarkably specific and cross-talk between TCS is limited. However, regulation of the flow of information through complex signaling networks that include closely related TCS remains largely unknown. Additionally, many bacteria utilize multi-component signaling networks which provide additional genetic and biochemical interactions that must be regulated for signaling fidelity, input and output specificity, and phosphorylation kinetics. Here we describe the characterization of an NtrC-like RR that participates in regulation of Type-IV pilus-dependent motility of Myxococcus xanthus and is thus named NmpR, NtrC Modulator of Pili Regulator. A complex multi-component signaling system including NmpR was revealed by suppressor mutations that restored motility to cells lacking PilR, an evolutionarily conserved RR required for expression of pilA encoding the major Type-IV pilus monomer found in many bacterial species. The system contains at least four signaling proteins: a SK with a protoglobin sensor domain (NmpU), a hybrid SK (NmpS), a phospho-sink protein (NmpT), and an NtrC-like RR (NmpR). We demonstrate that ΔpilR bypass suppressor mutations affect regulation of the NmpRSTU multi-component system, such that NmpR activation is capable of restoring expression of pilA in the absence of PilR. Our findings indicate that pilus gene expression in M. xanthus is regulated by an extended network of TCS which interact to refine control of pilus function.

Enhanced unbiased sampling of protein dynamics using evolutionary coupling information

One of the major challenges in atomistic simulations of proteins is efficient sampling of pathways associated with rare conformational transitions. Recent developments in statistical methods for computation of direct evolutionary couplings between amino acids within and across polypeptide chains have allowed for inference of native residue contacts, informing accurate prediction of protein folds and multimeric structures. In this study, we assess the use of distances between evolutionarily coupled residues as natural choices for reaction coordinates which can be incorporated into Markov state model-based adaptive sampling schemes and potentially used to predict not only functional conformations but also pathways of conformational change, protein folding, and protein-protein association. We demonstrate the utility of evolutionary couplings in sampling and predicting activation pathways of the β 2-adrenergic receptor (β 2-AR), folding of the FiP35 WW domain, and dimerization of the E. coli molybdopterin synthase subunits. We find that the time required for β 2-AR activation and folding of the WW domain are greatly diminished using evolutionary couplings-guided adaptive sampling. Additionally, we were able to identify putative molybdopterin synthase association pathways and near-crystal structure complexes from protein-protein association simulations.

Conformational dynamics are a key factor in signaling mediated by the receiver domain of a sensor histidine kinase from Arabidopsis thaliana

Multistep phosphorelay (MSP) cascades mediate responses to a wide spectrum of stimuli, including plant hormonal signaling, but several aspects of MSP await elucidation. Here, we provide first insight into the key step of MSP-mediated phosphotransfer in a eukaryotic system, the phosphorylation of the receiver domain of the histidine kinase CYTOKININ-INDEPENDENT 1 (CKI1_RD) from Arabidopsis thaliana We observed that the crystal structures of free, Mg²⁺-bound, and beryllofluoridated CKI1_RD (a stable analogue of the labile phosphorylated form) were identical and similar to the active state of receiver domains of bacterial response regulators. However, the three CKI1_RD variants exhibited different conformational dynamics in solution. NMR studies revealed that Mg²⁺ binding and beryllofluoridation alter the conformational equilibrium of the β3-α3 loop close to the phosphorylation site. Mutations that perturbed the conformational behavior of the β3-α3 loop while keeping the active-site aspartate intact resulted in suppression of CKI1 function. Mechanistically, homology modeling indicated that the β3-α3 loop directly interacts with the ATP-binding site of the CKI1 histidine kinase domain. The functional relevance of the conformational dynamics observed in the β3-α3 loop of CKI1_RD was supported by a comparison with another A. thaliana histidine kinase, ETR1. In contrast to the highly dynamic β3-α3 loop of CKI1_RD, the corresponding loop of the ETR1 receiver domain (ETR1_RD) exhibited little conformational exchange and adopted a different orientation in crystals. Biochemical data indicated that ETR1_RD is involved in phosphorylation-independent signaling, implying a direct link between conformational behavior and the ability of eukaryotic receiver domains to participate in MSP.

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

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.

Detection of Side Chain Rearrangements Mediating the Motions of Transmembrane Helices in Molecular Dynamics Simulations of G Protein-Coupled Receptors

Structure and dynamics are essential elements of protein function. Protein structure is constantly fluctuating and undergoing conformational changes, which are captured by molecular dynamics (MD) simulations. We introduce a computational framework that provides a compact representation of the dynamic conformational space of biomolecular simulations. This method presents a systematic approach designed to reduce the large MD simulation spatiotemporal datasets into a manageable set in order to guide our understanding of how protein mechanics emerge from side chain organization and dynamic reorganization. We focus on the detection of side chain interactions that undergo rearrangements mediating global domain motions and vice versa. Side chain rearrangements are extracted from side chain interactions that undergo well-defined abrupt and persistent changes in distance time series using Gaussian mixture models, whereas global domain motions are detected using dynamic cross-correlation. Both side chain rearrangements and global domain motions represent the dynamic components of the protein MD simulation, and are both mapped into a network where they are connected based on their degree of coupling. This method allows for the study of allosteric communication in proteins by mapping out the protein dynamics into an intramolecular network to reduce the large simulation data into a manageable set of communities composed of coupled side chain rearrangements and global domain motions. This computational framework is suitable for the study of tightly packed proteins, such as G protein-coupled receptors, and we present an application on a seven microseconds MD trajectory of CC chemokine receptor 7 (CCR7) bound to its ligand CCL21.

Protein dynamics and function from solution state NMR spectroscopy

It is well-established that dynamics are central to protein function; their importance is implicitly acknowledged in the principles of the Monod, Wyman and Changeux model of binding cooperativity, which was originally proposed in 1965. Nowadays the concept of protein dynamics is formulated in terms of the energy landscape theory, which can be used to understand protein folding and conformational changes in proteins. Because protein dynamics are so important, a key to understanding protein function at the molecular level is to design experiments that allow their quantitative analysis. Nuclear magnetic resonance (NMR) spectroscopy is uniquely suited for this purpose because major advances in theory, hardware, and experimental methods have made it possible to characterize protein dynamics at an unprecedented level of detail. Unique features of NMR include the ability to quantify dynamics (i) under equilibrium conditions without external perturbations, (ii) using many probes simultaneously, and (iii) over large time intervals. Here we review NMR techniques for quantifying protein dynamics on fast (ps-ns), slow (μs-ms), and very slow (s-min) time scales. These techniques are discussed with reference to some major discoveries in protein science that have been made possible by NMR spectroscopy.

To ∼P or Not to ∼P? Non-canonical activation by two-component response regulators

Bacteria sense and respond to their environment through the use of two-component regulatory systems. The ability to adapt to a wide range of environmental stresses is directly related to the number of two-component systems an organism possesses. Recent advances in this area have identified numerous variations on the archetype systems that employ a sensor kinase and a response regulator. It is now evident that many orphan regulators that lack cognate kinases do not rely on phosphorylation for activation and new roles for unphosphorylated response regulators have been identified. The significance of recent findings and suggestions for further research are discussed.

Revealing Atomic-Level Mechanisms of Protein Allostery with Molecular Dynamics Simulations

Molecular dynamics (MD) simulations have become a powerful and popular method for the study of protein allostery, the widespread phenomenon in which a stimulus at one site on a protein influences the properties of another site on the protein. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric binding sites and the determination of the mechanistic basis for allostery. These results can provide a foundation for applications including rational drug design and protein engineering. Here, we provide an introduction to the investigation of protein allostery using molecular dynamics simulation. We emphasize the importance of designing simulations that include appropriate perturbations to the molecular system, such as the addition or removal of ligands or the application of mechanical force. We also demonstrate how the bidirectional nature of allostery-the fact that the two sites involved influence one another in a symmetrical manner-can facilitate such investigations. Through a series of case studies, we illustrate how these concepts have been used to reveal the structural basis for allostery in several proteins and protein complexes of biological and pharmaceutical interest.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Conformational heterogeneity of the calmodulin binding interface

Calmodulin (CaM) is a ubiquitous Ca(2+) sensor and a crucial signalling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signalling molecules including G-protein-coupled receptors, ion channels and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of CaM binding diversity. Our computational model indicates that in the absence of Ca(2+), sub-states in the folded ensemble of CaM's C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca(2+)-bound regime and, thus, a candidate for pharmacological intervention.

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

Protein Allostery and Conformational Dynamics

The functions of many proteins are regulated through allostery, whereby effector binding at a distal site changes the functional activity (e.g., substrate binding affinity or catalytic efficiency) at the active site. Most allosteric studies have focused on thermodynamic properties, in particular, substrate binding affinity. Changes in substrate binding affinity by allosteric effectors have generally been thought to be mediated by conformational transitions of the proteins or, alternatively, by changes in the broadness of the free energy basin of the protein conformational state without shifting the basin minimum position. When effector binding changes the free energy landscape of a protein in conformational space, the change affects not only thermodynamic properties but also dynamic properties, including the amplitudes of motions on different time scales and rates of conformational transitions. Here we assess the roles of conformational dynamics in allosteric regulation. Two cases are highlighted where NMR spectroscopy and molecular dynamics simulation have been used as complementary approaches to identify residues possibly involved in allosteric communication. Perspectives on contentious issues, for example, the relationship between picosecond-nanosecond local and microsecond-millisecond conformational exchange dynamics, are presented.

Prokaryotic 2-component systems and the OmpR/PhoB superfamily

In bacteria, 2-component regulatory systems (TCSs) are the critical information-processing pathways that link stimuli to specific adaptive responses. Signals perceived by membrane sensors, which are generally histidine kinases, are transmitted by response regulators (RRs) to allow cells to cope rapidly and effectively with environmental challenges. Over the past few decades, genes encoding components of TCSs and their responsive proteins have been identified, crystal structures have been described, and signaling mechanisms have been elucidated. Here, we review recent findings and interesting breakthroughs in bacterial TCS research. Furthermore, we discuss structural features, mechanisms of activation and regulation, and cross-regulation of RRs, with a focus on the largest RR family, OmpR/PhoB, to provide a comprehensive overview of these critically important signaling molecules.

Role of Protein Dynamics in Allosteric Control of the Catalytic Phosphoryl Transfer of Insulin Receptor Kinase

The catalytic and allosteric mechanisms of insulin receptor kinase (IRK) are investigated by a combination of ab initio and semiempirical quantum mechanical and molecular mechanical (QM/MM) methods and classical molecular dynamics (MD) simulations. The simulations reveal that the catalytic reaction proceeds in two steps, starting with the transfer of a proton from substrate Tyr to the catalytic Asp1132, followed by the phosphoryl transfer from ATP to substrate Tyr. The enhancement of the catalytic rate of IRK upon phosphorylations in the enzyme's activation loop is found to occur mainly via changes to the free energy landscape of the proton transfer step, favoring the proton transfer in the fully phosphorylated enzyme. In contrast, the effects of the phosphorylations on the phosphoryl transfer are smaller. Equilibrium MD simulations show that IRK phosphorylations affect the protein dynamics of the enzyme before the proton transfer to Asp1132 with only a minor effect after the proton transfer. This finding is consistent with the large change in the proton transfer free energy and the smaller change in the free energy barrier of phosphoryl transfer found by QM/MM simulations. Taken together, the present results provide details on how IRK phosphorylation exerts allosteric control of the catalytic activity via modifications of protein dynamics and free energy landscape of catalytic reaction. The results also highlight the importance of protein dynamics in connecting protein allostery and catalysis to control catalytic activity of enzymes.

Free energy landscape of activation in a signalling protein at atomic resolution

The interconversion between inactive and active protein states, traditionally described by two static structures, is at the heart of signaling. However, how folded states interconvert is largely unknown due to the inability to experimentally observe transition pathways. Here we explore the free energy landscape of the bacterial response regulator NtrC by combining computation and NMR, and discover unexpected features underlying efficient signaling. We find that functional states are defined purely in kinetic and not structural terms. The need of a well-defined conformer, crucial to the active state, is absent in the inactive state, which comprises a heterogeneous collection of conformers. The transition between active and inactive states occurs through multiple pathways, facilitated by a number of nonnative transient hydrogen bonds, thus lowering the transition barrier through both entropic and enthalpic contributions. These findings may represent general features for functional conformational transitions within the folded state.