Structural changes involved in protein binding correlate with intrinsic motions of proteins in the unbound state

How the Ligand-Induced Reorganization of Protein Internal Energies Is Coupled to Conformational Events

Here, we introduce a novel computational method to identify the protein substructures most likely to support the functionally oriented structural deformations that occur upon ligand-binding. To this aim, we study the modulation of protein energetics along the trajectory of a molecular dynamics simulation of different proteins in the presence and in the absence of their respective ligands, namely, human FGF, human second PDZ from human PTP1E/PTPL1, and the N terminal domain of human Hsp90. The method is based on the idea that a subset of protein residues (hotspots) may initiate the global response via the disassembly and reassembly of interactions, which is reflected in the modulation of the overall protein energetics. To identify structural hotspots and dynamic states linked to the onset of functionally relevant conformational transitions, we define an energy profile to monitor the protein energetics, based on a previously introduced approach that highlights the essential nonbonded couplings among all residues. The energy profiles are calculated along the trajectory to yield a time-dependent evolution, and their relative population in the presence and absence of the ligand is evaluated by means of a clustering procedure. It is found that interconversion between clusters, as well as their population and the density of specific energy profiles in the vicinity of structural transitions, provides specific information on the impact of the ligand in driving the protein conformational response. This analysis also highlights the hotspot residues that are most responsive to the presence of the ligand. Importantly, identified hotspots are in agreement with experimental evidence in the three considered systems. We propose that this approach can be generally used in the prediction of "allosteric hotspots" and ligand-induced conformational responses, as well as to select conformations more likely to support functional transitions (e.g., in the framework of adaptive sampling approaches).

Dynamical comparison between myoglobin and hemoglobin

Myoglobin and hemoglobin are globular hemeproteins, when the former is a monomer and the latter a heterotetramer. Despite the structural similarity of myoglobin to α and β subunits of hemoglobin, there is a functional difference between the two proteins, owing to the quaternary structure of hemoglobin. The effect of the quaternary structure of hemoglobin on the intrinsic dynamics of its subunits is explored by dynamical comparison of the two proteins. Anisotropic Network Model modes of motion were calculated for hemoglobin and myoglobin. Dynamical comparison between the proteins was performed using global and local Anisotropic Network Model mode alignment algorithms based on the algorithms of Smith-Waterman and Needleman-Wunsch for sequence comparison. The results indicate that the quaternary structure of hemoglobin substantially alters the intrinsic dynamics of its subunits, an effect that may contribute to the functional difference between the two proteins. Local dynamics similarity between the proteins is still observed at the major exit route of the ligand.

Reversible and Tunable Photoswitching of Protein Function through Genetic Encoding of Azobenzene Amino Acids in Mammalian Cells

The genetic encoding of three different azobenzene phenylalanines with different photochemical properties was achieved in human cells by using an engineered pyrrolysyl tRNA/tRNA synthetase pair. In order to demonstrate reversible light control of protein function, azobenzenes were site-specifically introduced into firefly luciferase. Computational strategies were applied to guide the selection of potential photoswitchable sites that lead to a reversibly controlled luciferase enzyme. In addition, the new azobenzene analogues provide enhanced thermal stability, high photoconversion, and responsiveness to visible light. These small-molecule photoswitches can reversibly photocontrol protein function with excellent spatiotemporal resolution, and preferred sites for incorporation can be computationally determined, thus providing a new tool for investigating biological processes.

DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins

Despite efforts during the past decades, loop modeling remains a difficult part of protein structure modeling. Several approaches have been developed in the framework of crystal structures. However, for homology models, the modeling of loops is still far from being solved. We propose DaReUS-Loop, a data-based approach that identifies loop candidates mining the complete set of experimental structures available in the Protein Data Bank. Candidate filtering relies on local conformation profile-profile comparison, together with physico-chemical scoring. Applied to three different template-based test sets, DaReUS-Loop shows significant increase in the number of high-accuracy loops, and significant enhancement for modeling long loops. A special advantage is that our method proposes a prediction confidence score that correlates well with the expected accuracy of the loops. Strikingly, over 50% of successful loop models are derived from unrelated proteins, indicating that fragments under similar constraints tend to adopt similar structure, beyond mere homology.

E-hooks provide guidance and a soft landing for the microtubule binding domain of dynein

Macromolecular binding is a complex process that involves sensing and approaching the binding partner, adopting the proper orientation, and performing the physical binding. We computationally investigated the role of E-hooks, which are intrinsically disordered regions (IDRs) at the C-terminus of tubulin, on dynein microtubule binding domain (MTBD) binding to the microtubule as a function of the distance between the MTBD and its binding site on the microtubule. Our results demonstrated that the contacts between E-hooks and the MTBD are dynamical; multiple negatively charted patches of amino acids on the E-hooks grab and release the same positively charged patches on the MTBD as it approaches the microtubule. Even when the distance between the MTBD and the microtubule was greater than the E-hook length, the E-hooks sensed and guided MTBD via long-range electrostatic interactions in our simulations. Moreover, we found that E-hooks exerted electrostatic forces on the MTBD that were distance dependent; the force pulls the MTBD toward the microtubule at long distances but opposes binding at short distances. This mechanism provides a "soft-landing" for the MTBD as it binds to the microtubule. Finally, our analysis of the conformational states of E-hooks in presence and absence of the MTBD indicates that the binding process is a mixture of the induced-fit and lock-and-key macromolecular binding hypotheses. Overall, this novel binding mechanism is termed "guided-soft-binding" and could have broad-reaching impacts on the understanding of how IDRs dock to structured proteins.

A computational model for controlling conformational cooperativity and function in proteins

We present a computational model that allows for rapid prediction of correlations among a set of residue pairs when the fluctuations of another set of residues are perturbed. The simple theory presented here is based on the knowledge of the fluctuation covariance matrix only. In this sense, the theory is model independent and therefore universal. Perturbation of any set of fluctuations and the resulting response of the remaining set are calculated using conditional probabilities of a multivariate normal distribution. The model is expected to rapidly and accurately map the consequences of mutations in proteins, as well as allosteric activity and ligand binding. Knowledge of triple correlations of fluctuations of residues i, j, and k, 〈 Δ R i Δ R j Δ R k 〉 emerges as the necessary source of information for controlling residue pairs by perturbing a distant residue. Triple correlations have not received wide attention in literature. Perturbation-response-function relations for ubiquitin (UBQ) are discussed as an example. Covariance matrix for UBQ obtained from the Gaussian Network Model combined with the present computational algorithm is able to reflect the millisecond molecular dynamics correlations and observed NMR results. © 2018 Wiley Periodicals, Inc.

Electrochemical Biosensing of Algal Toxins in Water: The Current State-of-the-Art

Due to increasing stringency of water legislation and extreme consequences that failure to detect some contaminants in water can involve, there has been a strong interest in developing electrochemical biosensors for algal toxin detection during the past decade, evidenced by literature increasing from 2 journal papers pre-2009 to 24 between 2009 and 2018. In this context, this review has summarized recent progress of successful algal toxin detection in water using electrochemical biosensing techniques. Satisfactory detection recoveries using real environmental water samples and good sensor repeatability and reproducibility have been achieved, along with some excellent limit-of-detection (LOD) reported. Recent electrochemical biosensor literature in algal toxin detection is compared and discussed to cover three major design components: (1) biorecognition elements, (2) electrochemical read-out techniques, and (3) sensor electrodes and signal amplification strategy. The recent development of electrochemical biosensors has provided one more step further toward quick in situ detection of algal toxins in the contamination point of the water source. In the end, we have also critically reviewed the current challenges and research opportunities regarding electrochemical biosensors for algal toxin detection that need to be addressed before they attain commercial viability.

Ancient thioredoxins evolved to modern-day stability-function requirement by altering native state ensemble

Thioredoxins (THRXs)-small globular proteins that reduce other proteins-are ubiquitous in all forms of life, from Archaea to mammals. Although ancestral thioredoxins share sequential and structural similarity with the modern-day (extant) homologues, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern-day THRXs') native state ensemble, as quantified by the dynamic flexibility index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resemble an alternative classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the α3, α4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant THRXs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low-flexibility (called hinges) and high-flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Structural dynamics is a determinant of the functional significance of missense variants

Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Normal mode analysis (NMA) can facilitate quick and systematic investigation of protein dynamics using data from the Protein Data Bank (PDB). We developed an elastic network model-based NMA program using dihedral angles as independent variables. Compared to the NMA programs that use Cartesian coordinates as independent variables, key attributes of the proposed program are as follows: (1) chain connectivity related to the folding pattern of a polypeptide chain is naturally embedded in the model; (2) the full-atom system is acceptable, and owing to a considerably smaller number of independent variables, the PDB data can be used without further manipulation; (3) the number of variables can be easily reduced by some of the rotatable dihedral angles; (4) the PDB data for any molecule besides proteins can be considered without coarse-graining; and (5) individual motions of constituent subunits and ligand molecules can be easily decomposed into external and internal motions to examine their mutual and intrinsic motions. Its performance is illustrated with an example of a DNA-binding allosteric protein, a catabolite activator protein. In particular, the focus is on the conformational change upon cAMP and DNA binding, and on the communication between their binding sites remotely located from each other. In this illustration, NMA creates a vivid picture of the protein dynamics at various levels of the structures, i.e., atoms, residues, secondary structures, domains, subunits, and the complete system, including DNA and cAMP. Comparative studies of the specific protein in different states, e.g., apo- and holo-conformations, and free and complexed configurations, provide useful information for studying structurally and functionally important aspects of the protein.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

Solution NMR views of dynamical ordering of biomacromolecules

BACKGROUND

To understand the mechanisms related to the 'dynamical ordering' of macromolecules and biological systems, it is crucial to monitor, in detail, molecular interactions and their dynamics across multiple timescales. Solution nuclear magnetic resonance (NMR) spectroscopy is an ideal tool that can investigate biophysical events at the atomic level, in near-physiological buffer solutions, or even inside cells.

SCOPE OF REVIEW

In the past several decades, progress in solution NMR has significantly contributed to the elucidation of three-dimensional structures, the understanding of conformational motions, and the underlying thermodynamic and kinetic properties of biomacromolecules. This review discusses recent methodological development of NMR, their applications and some of the remaining challenges.

MAJOR CONCLUSIONS

Although a major drawback of NMR is its difficulty in studying the dynamical ordering of larger biomolecular systems, current technologies have achieved considerable success in the structural analysis of substantially large proteins and biomolecular complexes over 1MDa and have characterised a wide range of timescales across which biomolecular motion exists. While NMR is well suited to obtain local structure information in detail, it contributes valuable and unique information within hybrid approaches that combine complementary methodologies, including solution scattering and microscopic techniques.

GENERAL SIGNIFICANCE

For living systems, the dynamic assembly and disassembly of macromolecular complexes is of utmost importance for cellular homeostasis and, if dysregulated, implied in human disease. It is thus instructive for the advancement of the study of the dynamical ordering to discuss the potential possibilities of solution NMR spectroscopy and its applications. This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato.

Dynamic Modulation of Binding Affinity as a Mechanism for Regulating Interferon Signaling

How structural dynamics affects cytokine signaling is under debate. Here, we investigated the dynamics of the type I interferon (IFN) receptor, IFNAR1, and its effect on signaling upon binding IFN and IFNAR2 using a combination of structure-based mechanistic studies, in situ binding, and gene induction assays. Our study reveals that IFNAR1 flexibility modulates ligand-binding affinity, which, in turn, regulates biological signaling. We identified the hinge sites and key interactions implicated in IFNAR1 inter-subdomain (SD1-SD4) movements. We showed that the predicted cooperative movements are essential to accommodate intermolecular interactions. Engineered disulfide bridges, computationally predicted to interfere with IFNAR1 dynamics, were experimentally confirmed. Notably, introducing disulfide bonds between subdomains SD2 and SD3 modulated IFN binding and activity in accordance with the relative attenuation of cooperative movements with varying distance from the hinge center, whereas locking the SD3-SD4 interface flexibility in favor of an extended conformer increased activity.

Recent advances in measuring the kinetics of biomolecules by NMR relaxation dispersion spectroscopy

Protein function can be modulated or dictated by the amplitude and timescale of biomolecular motion, therefore it is imperative to study protein dynamics. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique capable of studying timescales of motion that range from those faster than molecular reorientation on the picosecond timescale to those that occur in real-time. Across this entire regime, NMR observables can report on the amplitude of atomic motion, and the kinetics of atomic motion can be ascertained with a wide variety of experimental techniques from real-time to milliseconds and several nanoseconds to picoseconds. Still a four orders of magnitude window between several nanoseconds and tens of microseconds has remained elusive. Here, we highlight new relaxation dispersion NMR techniques that serve to cover this "hidden-time" window up to hundreds of nanoseconds that achieve atomic resolution while studying the molecule under physiological conditions.

Dynamical differences of hemoglobin and the ionotropic glutamate receptor in different states revealed by a new dynamics alignment method

A new algorithm for comparison of protein dynamics is presented. Compared protein structures are superposed and their modes of motions are calculated using the anisotropic network model. The obtained modes are aligned using the dynamic programming algorithm of Needleman and Wunsch, commonly used for sequence alignment. Dynamical comparison of hemoglobin in the T and R2 states reveals that the dynamics of the allosteric effector 2,3-bisphosphoglycerate binding site is different in the two states. These differences can contribute to the selectivity of the effector to the T state. Similar comparison of the ionotropic glutamate receptor in the kainate+(R,R)-2b and ZK bound states reveals that the kainate+(R,R)-2b bound states slow modes describe upward motions of ligand binding domain and the transmembrane domain regions. Such motions may lead to the opening of the receptor. The upper lobes of the LBDs of the ZK bound state have a smaller interface with the amino terminal domains above them and have a better ability to move together. The present study exemplifies the use of dynamics comparison as a tool to study protein function. Proteins 2017; 85:1507-1517. © 2014 Wiley Periodicals, Inc.

Dynamical persistence of active sites identified in maltose-binding protein

This study identifies dynamical properties of maltose-binding protein (MBP) useful in unveiling active site residues susceptible to ligand binding. The described methodology has been previously used in support of novel topological techniques of persistent homology and statistical inference in complex, multi-scale, high-dimensional data often encountered in computational biophysics. Here we outline a computational protocol that is based on the anisotropic elastic network models of 14 all-atom three-dimensional protein structures. We introduce the notion of dynamical distance matrices as a measure of correlated interactions among 370 amino acid residues that constitute a single protein. The dynamical distance matrices serve as an input for a persistent homology suite of codes to further distinguish a small subset of residues with high affinity for ligand binding and allosteric activity. In addition, we show that ligand-free closed MBP structures require lower deformation energies than open MBP structures, which may be used in categorization of time-evolving molecular dynamics structures. Analysis of the most probable allosteric coupling pathways between active site residues and the protein exterior is also presented.

Effect of Dimerization on the Dynamics of Neurotransmitter:Sodium Symporters

Dimerization is a common feature among the members of the neurotransmitter:sodium symporter (NSS) family of membrane proteins. Yet, the effect of dimerization on the mechanism of action of NSS members is not fully understood. In this study, we examined the collective dynamics of two members of the family, leucine transporter (LeuT) and dopamine transporter (DAT), to assess the significance of dimerization in modulating the functional motions of the monomers. We used to this aim the anisotropic network model (ANM), an efficient and robust method for modeling the intrinsic motions of proteins and their complexes. Transporters belonging to the NSS family are known to alternate between outward-facing (OF) and inward-facing (IF) states, which enables the uptake and release of their substrate (neurotransmitter) respectively, as the substrate is transported from the exterior to the interior of the cell. In both LeuT and DAT, dimerization is found to alter the collective motions intrinsically accessible to the individual monomers in favor of the functional transitions (OF ↔ IF), suggesting that dimerization may play a role in facilitating transport.

Specificity and stability of transient protein-protein interactions

Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors.

A reduced mechanical model for cAMP-modulated gating in HCN channels

We developed an in silico mechanical model to analyze the process of cAMP-induced conformational modulations in hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which conduct cations across the membrane of mammalian heart and brain cells. The structural analysis reveals a quaternary twist in the cytosolic parts of the four subunits in the channel tetramer. This motion augments the intrinsic dynamics of the very same protein structure. The pronounced differences between the cAMP bound and unbound form include a mutual interaction between the C-linker of the cyclic nucleotide binding domain (CNBD) and the linker between the S4 and S5 transmembrane domain of the channel. This allows a mechanistic annotation of the twisting motion in relation to the allosteric modulation of voltage-dependent gating of this channel by cAMP.

Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms

Network modeling and percolation analysis of conformational dynamics and energetics of regulatory mechanisms in cyclin-dependent kinases.

Fingerprints of Conformational States of Human Hsp70 at Sub-THz Frequencies

Large multidomain proteins occur in different conformational states to function. Detection and monitoring of these different structural states are of crucial interest for understanding the mechanics of proteins. Using computational methods, we show that different protein conformational states of the two-domain 70 kDa human Heat-shock protein (hHsp70), with similar vibrational density of states, lead to remarkably different far-IR spectra at acoustical frequencies (ν < 300 GHz). We found that the slow damped motions of the positively charged residues of hHsp70 contribute the most to collective IR active modes at low frequencies (ν < 300 GHz). We predicted that different structural states and functional modes of large proteins, such as hHsp70, might be detected in the sub-THz frequency range by single-molecule spectroscopy similar to the recent extraordinary acoustic Raman spectroscopy (Wheaton S.; Nat. Photonics2015, 9, 68-72).

Theoretical Insights into Sub-Terahertz Acoustic Vibrations of Proteins Measured in Single-Molecule Experiments

Proteins are an important class of nanobioparticles with acoustical modes in the sub-THz frequency range. There is considerable interest to measure and establish the role of these acoustical vibrations for biological function. So far, the technique providing the most detailed information about the acoustical modes of proteins is the very recent Extraordinary Acoustic Raman (EAR) spectroscopy. In this technique, proteins are trapped in nanoholes and excited by two optical lasers of slightly different wavelengths producing an electric field at low frequency (<100 GHz). We demonstrate that the acoustical modes of proteins studied by EAR spectroscopy are both infrared- and Raman-active modes, and we provided interpretation of the spectroscopic fingerprints measured at the single-molecule level. A combination of the present calculations with techniques based on the excitation of a single nanobioparticle by an electric field, such as EAR spectroscopy, should provide a wealth of information on the role of molecular dynamics for biological function.

Using persistent homology and dynamical distances to analyze protein binding

Persistent homology captures the evolution of topological features of a model as a parameter changes. The most commonly used summary statistics of persistent homology are the barcode and the persistence diagram. Another summary statistic, the persistence landscape, was recently introduced by Bubenik. It is a functional summary, so it is easy to calculate sample means and variances, and it is straightforward to construct various test statistics. Implementing a permutation test we detect conformational changes between closed and open forms of the maltose-binding protein, a large biomolecule consisting of 370 amino acid residues. Furthermore, persistence landscapes can be applied to machine learning methods. A hyperplane from a support vector machine shows the clear separation between the closed and open proteins conformations. Moreover, because our approach captures dynamical properties of the protein our results may help in identifying residues susceptible to ligand binding; we show that the majority of active site residues and allosteric pathway residues are located in the vicinity of the most persistent loop in the corresponding filtered Vietoris-Rips complex. This finding was not observed in the classical anisotropic network model.

Modeling global changes induced by local perturbations to the HIV-1 capsid

The HIV-1 capsid is a conical protein shell made up of hexamers and pentamers of the capsid protein. The capsid houses the viral genome and replication machinery, and its opening, or uncoating, within the host cell marks a critical step in the HIV-1 lifecycle. Binding of host factors such as TRIM5α and cyclophilin A (CypA) can alter the capsid's stability, accelerating or delaying the onset of uncoating and disrupting infectivity. We employ coarse-grained computational modeling to investigate the effects of point mutations and host factor binding on HIV-1 capsid stability. We find that the largest fluctuations occur in the low-curvature regions of the capsid, and that its structural dynamics are affected by perturbations at the inter-hexamer interfaces and near the CypA binding loop, suggesting roles for these features in capsid stability. Our models show that linking capsid proteins across hexamers attenuates vibration in the low-curvature regions of the capsid, but that linking within hexamers does not. These results indicate a possible mechanism through which CypA binding alters capsid stability and highlight the utility of coarse-grained network modeling for understanding capsid mechanics.

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment.

Theory of Acoustic Raman Modes in Proteins

We present a theoretical analysis that associates the resonances of extraordinary acoustic Raman (EAR) spectroscopy [Wheaton et al., Nat. Photonics 9, 68 (2015)] with the collective modes of proteins. The theory uses the anisotropic elastic network model to find the protein acoustic modes, and calculates Raman intensity by treating the protein as a polarizable ellipsoid. Reasonable agreement is found between EAR spectra and our theory. Protein acoustic modes have been extensively studied theoretically to assess the role they play in protein function; this result suggests EAR spectroscopy as a new experimental tool for studies of protein acoustic modes.

The Sampling of Conformational Dynamics in Ambient-Temperature Crystal Structures of Arginine Kinase

Arginine kinase provides a model for functional dynamics, studied through crystallography, enzymology, and nuclear magnetic resonance. Structures are now solved, at ambient temperature, for the transition state analog (TSA) complex. Analysis of quasi-rigid sub-domain displacements show that differences between the two TSA structures average about 5% of changes between substrate-free and TSA forms, and they are nearly co-linear. Small backbone hinge rotations map to sites that also flex on substrate binding. Anisotropic atomic displacement parameters (ADPs) are refined using rigid-body TLS constraints. Consistency between crystal forms shows that they reflect intrinsic molecular properties more than crystal lattice effects. In many regions, the favored directions of thermal/static displacement are appreciably correlated with movements on substrate binding. Correlation between ADPs and larger substrate-associated movements implies that the latter approximately follow paths of low-energy intrinsic motions.

Pushing the Backbone in Protein-Protein Docking

Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis. All methods tested rarely sample near-bound states from the unbound conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. Seventy percent of docking targets succeed when the unbound backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

Addressing the Role of Conformational Diversity in Protein Structure Prediction

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

The principle of conformational signaling

Specific conformations of signaling proteins can serve as “signals” in signal transduction by being recognized by receptors.

Using THz Spectroscopy, Evolutionary Network Analysis Methods, and MD Simulation to Map the Evolution of Allosteric Communication Pathways in c-Type Lysozymes

It is now widely accepted that protein function is intimately tied with the navigation of energy landscapes. In this framework, a protein sequence is not described by a distinct structure but rather by an ensemble of conformations. And it is through this ensemble that evolution is able to modify a protein's function by altering its landscape. Hence, the evolution of protein functions involves selective pressures that adjust the sampling of the conformational states. In this work, we focus on elucidating the evolutionary pathway that shaped the function of individual proteins that make-up the mammalian c-type lysozyme subfamily. Using both experimental and computational methods, we map out specific intermolecular interactions that direct the sampling of conformational states and accordingly, also underlie shifts in the landscape that are directly connected with the formation of novel protein functions. By contrasting three representative proteins in the family we identify molecular mechanisms that are associated with the selectivity of enhanced antimicrobial properties and consequently, divergent protein function. Namely, we link the extent of localized fluctuations involving the loop separating helices A and B with shifts in the equilibrium of the ensemble of conformational states that mediate interdomain coupling and concurrently moderate substrate binding affinity. This work reveals unique insights into the molecular level mechanisms that promote the progression of interactions that connect the immune response to infection with the nutritional properties of lactation, while also providing a deeper understanding about how evolving energy landscapes may define present-day protein function.

Cofilin reduces the mechanical properties of actin filaments: approach with coarse-grained methods

An actin filament is an essential cytoskeleton protein in a cell. Various proteins bind to actin for cell functions such as migration, division, and shape control. ADF/cofilin is a protein that severs actin filaments and is related to their dynamics. Actin is known to have excellent mechanical properties. Binding cofilin reduces its mechanical properties, and is related to the severing process. In this research, we applied a coarse-grained molecular dynamics simulation (CGMD) method to obtain actin filaments and cofilin-bound actin (cofilactin) filaments. Using these two obtained models, we constructed an elastic network model-based structure and conducted a normal mode analysis. Based on the low-frequency normal modes of the filament structure, we applied the continuum beam theory to calculate the mechanical properties of the actin and cofilactin filaments. The CGMD method provided structurally accurate actin and cofilactin filaments in relation to the mechanical properties, which showed good agreement with the established experimental results.

Molecular dynamics simulations: advances and applications

Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.

iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics

Gaussian network model (GNM) is a simple yet powerful model for investigating the dynamics of proteins and their complexes. GNM analysis became a broadly used method for assessing the conformational dynamics of biomolecular structures with the development of a user-friendly interface and database, iGNM, in 2005. We present here an updated version, iGNM 2.0 http://gnmdb.csb.pitt.edu/, which covers more than 95% of the structures currently available in the Protein Data Bank (PDB). Advanced search and visualization capabilities, both 2D and 3D, permit users to retrieve information on inter-residue and inter-domain cross-correlations, cooperative modes of motion, the location of hinge sites and energy localization spots. The ability of iGNM 2.0 to provide structural dynamics data on the large majority of PDB structures and, in particular, on their biological assemblies makes it a useful resource for establishing the bridge between structure, dynamics and function.

Comparison of the Internal Dynamics of Metalloproteases Provides New Insights on Their Function and Evolution

Metalloproteases have evolved in a vast number of biological systems, being one of the most diverse types of proteases and presenting a wide range of folds and catalytic metal ions. Given the increasing understanding of protein internal dynamics and its role in enzyme function, we are interested in assessing how the structural heterogeneity of metalloproteases translates into their dynamics. Therefore, the dynamical profile of the clan MA type protein thermolysin, derived from an Elastic Network Model of protein structure, was evaluated against those obtained from a set of experimental structures and molecular dynamics simulation trajectories. A close correspondence was obtained between modes derived from the coarse-grained model and the subspace of functionally-relevant motions observed experimentally, the later being shown to be encoded in the internal dynamics of the protein. This prompted the use of dynamics-based comparison methods that employ such coarse-grained models in a representative set of clan members, allowing for its quantitative description in terms of structural and dynamical variability. Although members show structural similarity, they nonetheless present distinct dynamical profiles, with no apparent correlation between structural and dynamical relatedness. However, previously unnoticed dynamical similarity was found between the relevant members Carboxypeptidase Pfu, Leishmanolysin, and Botulinum Neurotoxin Type A, despite sharing no structural similarity. Inspection of the respective alignments shows that dynamical similarity has a functional basis, namely the need for maintaining proper intermolecular interactions with the respective substrates. These results suggest that distinct selective pressure mechanisms act on metalloproteases at structural and dynamical levels through the course of their evolution. This work shows how new insights on metalloprotease function and evolution can be assessed with comparison schemes that incorporate information on protein dynamics. The integration of these newly developed tools, if applied to other protein families, can lead to more accurate and descriptive protein classification systems.

Adaptability of protein structures to enable functional interactions and evolutionary implications

Several studies in recent years have drawn attention to the ability of proteins to adapt to intermolecular interactions by conformational changes along structure-encoded collective modes of motions. These so-called soft modes, primarily driven by entropic effects, facilitate, if not enable, functional interactions. They represent excursions on the conformational space along principal low-ascent directions/paths away from the original free energy minimum, and they are accessible to the protein even before protein-protein/ligand interactions. An emerging concept from these studies is the evolution of structures or modular domains to favor such modes of motion that will be recruited or integrated for enabling functional interactions. Structural dynamics, including the allosteric switches in conformation that are often stabilized upon formation of complexes and multimeric assemblies, emerge as key properties that are evolutionarily maintained to accomplish biological activities, consistent with the paradigm sequence→structure→dynamics→function where 'dynamics' bridges structure and function.

An ordered water channel in Staphylococcus aureus FabI: unraveling the mechanism of substrate recognition and reduction

One third of all drugs in clinical use owe their pharmacological activity to the functional inhibition of enzymes, highlighting the importance of enzymatic targets for drug development. Because of the close relationship between inhibition and catalysis, understanding the recognition and turnover of enzymatic substrates is essential for rational drug design. Although the Staphylococcus aureus enoyl-acyl carrier protein reductase (saFabI) involved in bacterial fatty acid biosynthesis constitutes a very promising target for the development of novel, urgently needed anti-staphylococcal agents, the substrate binding mode and catalytic mechanism remained unclear for this enzyme. Using a combined crystallographic, kinetic, and computational approach, we have explored the chemical properties of the saFabI binding cavity, obtaining a consistent mechanistic model for substrate binding and turnover. We identified a water-molecule network linking the active site with a water basin inside the homo-tetrameric protein, which seems to be crucial for the closure of the flexible substrate binding loop as well as for an effective hydride and proton transfer during catalysis. On the basis of our results, we also derive a new model for the FabI-ACP complex that reveals how the ACP-bound acyl-substrate is injected into the FabI binding crevice. These findings support the future development of novel FabI inhibitors that target the FabI-ACP interface leading to the disruption of the interaction between these two proteins.

Ligand-induced protein responses and mechanical signal propagation described by linear response theories

In this study, a general linear response theory (LRT) is formulated to describe time-dependent and -independent protein conformational changes upon CO binding with myoglobin. Using the theory, we are able to monitor protein relaxation in two stages. The slower relaxation is found to occur from 4.4 to 81.2 picoseconds and the time constants characterized for a couple of aromatic residues agree with those observed by UV Resonance Raman (UVRR) spectrometry and time resolved x-ray crystallography. The faster "early responses", triggered as early as 400 femtoseconds, can be best described by the theory when impulse forces are used. The newly formulated theory describes the mechanical propagation following ligand-binding as a function of time, space and types of the perturbation forces. The "disseminators", defined as the residues that propagate signals throughout the molecule the fastest among all the residues in protein when perturbed, are found evolutionarily conserved and the mutations of which have been shown to largely change the CO rebinding kinetics in myoglobin.

On the relationship between low-frequency normal modes and the large-scale conformational changes of proteins

Normal mode analysis is a computational technique that allows to study the dynamics of biological macromolecules. It was first applied to small protein cases, more than thirty years ago. The interest in this technique then raised when it was realized that it can provide insights about the large-scale conformational changes a protein can experience, for instance upon ligand binding. As it was also realized that studying highly simplified protein models can provide similar insights, meaning that this kind of analysis can be both quick and simple to handle, several applications were proposed, in the context of various structural biology techniques. This review focuses on these applications, as well as on how the functional relevance of the lowest-frequency modes of proteins was established.

Structure, dynamics, assembly, and evolution of protein complexes

The assembly of individual proteins into functional complexes is fundamental to nearly all biological processes. In recent decades, many thousands of homomeric and heteromeric protein complex structures have been determined, greatly improving our understanding of the fundamental principles that control symmetric and asymmetric quaternary structure organization. Furthermore, our conception of protein complexes has moved beyond static representations to include dynamic aspects of quaternary structure, including conformational changes upon binding, multistep ordered assembly pathways, and structural fluctuations occurring within fully assembled complexes. Finally, major advances have been made in our understanding of protein complex evolution, both in reconstructing evolutionary histories of specific complexes and in elucidating general mechanisms that explain how quaternary structure tends to evolve. The evolution of quaternary structure occurs via changes in self-assembly state or through the gain or loss of protein subunits, and these processes can be driven by both adaptive and nonadaptive influences.