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Protein space is characterized by extensive recurrence, or "reuse," of parts, suggesting that new proteins and domains can evolve by mixing-and-matching of existing segments. From an evolutionary perspective, for a given combination to persist, the protein segments should presumably not only match geometrically but also dynamically communicate with each other to allow concerted motions that are key to function. Evidence from protein space supports the premise that domains indeed combine in this manner; we explore whether a similar phenomenon can be observed at the sub-domain level. To this end, we use Gaussian Network Models (GNMs) to calculate the so-called soft modes, or low-frequency modes of motion for a dataset of 150 protein domains. Modes of motion can be used to decompose a domain into segments of consecutive amino acids that we call "dynamic elements", each of which belongs to one of two parts that move in opposite senses. We find that, in many cases, the dynamic elements, detected based on GNM analysis, correspond to established "themes": Sub-domain-level segments that have been shown to recur in protein space, and which were detected in previous research using sequence similarity alone (i.e. completely independently of the GNM analysis). This statistically significant correlation hints at the importance of dynamics in evolution. Overall, the results are consistent with an evolutionary scenario where proteins have emerged from themes that need to match each other both geometrically and dynamically, e.g. to facilitate allosteric regulation. [ABSTRACT FROM AUTHOR]

Human monoamine transporters (MATs) are critical to regulating monoaminergic neurotransmission by translocating their substrates from the synaptic space back into the presynaptic neurons. As such, their primary substrate binding site S1 has been targeted by a wide range of compounds for treating neuropsychiatric and neurodegenerative disorders including depression, ADHD, neuropathic pain, and anxiety disorders. We present here a comparative study of the structural dynamics and ligand-binding properties of two MATs, dopamine transporter (DAT) and serotonin transporter (SERT), with focus on the allosteric modulation of their transport function by drugs or substrates that consistently bind a secondary site S2, proposed to serve as an allosteric site. Our systematic analysis of the conformational space and dynamics of a dataset of 50 structures resolved for DAT and SERT in the presence of one or more ligands/drugs reveals the specific residues playing a consistent role in coordinating the small molecules bound to subsites S2–I and S2-II within S2, such as R476 and Y481 in dDAT and E494, P561, and F556 in hSERT. Further analysis reveals how DAT and SERT differ in their two principal modes of structural changes, PC1 and PC2. Notably, PC1 underlies the transition between outward- and inward-facing states of the transporters as well as their gating; whereas PC2 supports the rearrangements of TM helices near the S2 site. Finally, the examination of cross-correlations between structural elements lining the respective sites S1 and S2 point to the crucial role of coupled motions between TM6a and TM10. In particular, we note the involvement of hSERT residues F335 and G338, and E493-E494-T497 belonging to these two respective helices, in establishing the allosteric communication between S1 and S2. These results help understand the molecular basis of the action of drugs that bind to the S2 site of DAT or SERT. They also provide a basis for designing allosteric modulators that may provide better control of specific interactions and cellular pathways, rather than indiscriminately inhibiting the transporter by targeting its orthosteric site.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development. [ABSTRACT FROM AUTHOR]

At the very deepest molecular level, the mechanisms of life depend on the operation of proteins, the so-called "workhorses" of the cell. Proteins are nanoscale machines that transform energy into useful cellular work, such as ion or nutrient transport, information processing, or energy transformation. Behind every biological task, there is a nanometer-sized molecule whose shape and intrinsic motions, binding, and sensing properties have been evolutionarily polished for billions of years. With the emergence of structural biology, the most crucial property of biomolecules was thought to be their 3D shape, but how this relates to function was unclear. During the past years, Elastic Network Models have revealed that protein shape, motion and function are deeply intertwined, so that each structure displays robustly shape-encoded functional movements that can be extraordinarily conserved across the tree of life. Here, we briefly review the growing literature exploring the interplay between sequence evolution, protein shape, intrinsic motions and function, and highlight examples from our research in which fundamental movements are conserved from bacteria to mammals or selected by cancer cells to modulate function. [ABSTRACT FROM AUTHOR]

Lytic polysaccharide monooxygenases (LPMOs), which are found in fungi, bacteria, and viruses, are redox enzymes utilizing copper to break glycosidic bonds in recalcitrant crystalline form of polysaccharides, such as chitin and cellulose. They are classified by the Carbohydrate-Active enZYmes (CAZy) database under various families. LPMOs's structure with a flat substrate binding region has been shown to contribute to its function, however, the role that LPMOs structural dynamics play during polysaccharide degradation and its mechanism of binding towards substrate are relatively unknown. Here, we report an exhaustive implementation of coarse-grained simulations using Elastic Network Models on multiple LPMO structures to shed light on how their structural dynamics contribute to their chemical function. Using Gaussian network models and Anisotropic network models, we show that the substrate binding region is highly flexible with significant and sustained micro-scale level conformational changes. Significantly, the loops on the binding side of the substrate are most mobile, in concert with the dynamic modes influencing the motions during binding. We also observed dynamic differences between four families of LPMO, namely AA9, AA10, AA11, and AA13 that consist of more than one structure. Specifically, the patterns of motion in the loop regions among the AA9 structures are distinct from those in the AA10 structures.

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold. [ABSTRACT FROM AUTHOR]

At the very deepest molecular level, the mechanisms of life depend on the operation of proteins, the so-called “workhorses” of the cell. Proteins are nanoscale machines that transform energy into useful cellular work, such as ion or nutrient transport, information processing, or energy transformation. Behind every biological task, there is a nanometer-sized molecule whose shape and intrinsic motions, binding, and sensing properties have been evolutionarily polished for billions of years. With the emergence of structural biology, the most crucial property of biomolecules was thought to be their 3D shape, but how this relates to function was unclear. During the past years, Elastic Network Models have revealed that protein shape, motion and function are deeply intertwined, so that each structure displays robustly shape-encoded functional movements that can be extraordinarily conserved across the tree of life. Here, we briefly review the growing literature exploring the interplay between sequence evolution, protein shape, intrinsic motions and function, and highlight examples from our research in which fundamental movements are conserved from bacteria to mammals or selected by cancer cells to modulate function.

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it. [ABSTRACT FROM AUTHOR]

The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics. [ABSTRACT FROM AUTHOR]

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.

Crystallographic B‐factors provide direct dynamical information on the internal mobility of proteins that is closely linked to function, and are also widely used as a benchmark in assessing elastic network models. A significant question in the field is: what is the exact amount of thermal vibrations in protein crystallographic B‐factors? This work sets out to answer this question. First, we carry out a thorough, statistically sound analysis of crystallographic B‐factors of over 10 000 structures. Second, by employing a highly accurate all‐atom model based on the well‐known CHARMM force field, we obtain computationally the magnitudes of thermal vibrations of nearly 1000 structures. Our key findings are: (i) the magnitude of thermal vibrations, surprisingly, is nearly protein‐independent, as a corollary to the universality for the vibrational spectra of globular proteins established earlier; (ii) the magnitude of thermal vibrations is small, less than 0.1 Å2 at 100 K; (iii) the percentage of thermal vibrations in B‐factors is the lowest at low resolution and low temperature (<10%) but increases to as high as 60% for structures determined at high resolution and at room temperature. The significance of this work is that it provides for the first time, using an extremely large dataset, a thorough analysis of B‐factors and their thermal and static disorder components. The results clearly demonstrate that structures determined at high resolution and at room temperature have the richest dynamics information. Since such structures are relatively rare in the PDB database, the work naturally calls for more such structures to be determined experimentally. [ABSTRACT FROM AUTHOR]

Coarse‐grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff‐based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay‐based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B‐factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non‐linear optimization problem whose parameters describe both rigid‐body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes. [ABSTRACT FROM AUTHOR]

DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.

An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design. [ABSTRACT FROM AUTHOR]

Insects devote a major part of their metabolic resources to the production of odorant binding proteins (OBPs). Although initially, these proteins were implicated in the solubilisation, binding and transport of semiochemicals to olfactory receptors, it is now recognised that they may play diverse, as yet uncharacterised, roles in insect physiology. The structures of these OBPs, the majority of which are known as “classical” OBPs, have shed some light on their potential functional roles. However, the dynamic properties of these proteins have received little attention despite their functional importance. Structural dynamics are encoded in the native protein fold and enable the adaptation of proteins to substrate binding. This paper provides a comparative review of the structural and dynamic properties of OBPs, making use of sequence/structure analysis, statistical and theoretical physics-based methods. It provides a new layer of information and additional methodological tools useful in unravelling the relationship between structure, dynamics and function of insect OBPs. The dynamic properties of OBPs, studied by means of elastic network models, reflect the similarities/dissimilarities observed in their respective structures and provides insights regarding protein motions that may have important implications for ligand recognition and binding. Furthermore, it was shown that the OBPs studied in this paper share conserved structural ‘core’ that may be of evolutionary and functional importance.

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR‐Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well‐defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR‐Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source. [ABSTRACT FROM AUTHOR]

• Intrinsic motions reveal couplings between ionotropic glutamate receptor domains. • Allosteric communication between domains selectively exploits pre-existing motions. • Global dynamics facilitates activation, allosteric signaling and desensitization. • Elastic network models efficiently elucidate the pre-existing dynamics of iGluRs. Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that are key players in synaptic transmission and plasticity. They are composed of four subunits, each containing four functional domains, the quaternary packing and collective structural dynamics of which are important determinants of their molecular mechanism of function. With the explosion of structural studies on different members of the family, including the structures of activated open channels, the mechanisms of action of these central signaling machines are now being elucidated. We review the current state of computational studies on two major members of the family, AMPA and NMDA receptors, with focus on molecular simulations and elastic network model analyses that have provided insights into the coupled movements of extracellular and transmembrane domains. We describe the newly emerging mechanisms of activation, allosteric signaling and desensitization, as mainly a selective triggering of pre-existing soft motions, as deduced from computational models and analyses that leverage structural data on intact AMPA and NMDA receptors in different states. [ABSTRACT FROM AUTHOR]

The term allostery was originally developed to describe structural changes in one binding site induced by the interaction of a partner molecule with a distant binding site, and it has been studied in depth in the field of enzymology. Here, we discuss the concept of action at a distance in relation to the folding and function of the solenoid class of tandem-repeat proteins such as tetratricopeptide repeats (TPRs) and ankyrin repeats. Distantly located repeats fold cooperatively, even though only nearest-neighbour interactions exist in these proteins. A number of repeat-protein scaffolds have been reported to display allosteric effects, transferred through the repeat array, that enable them to direct the activity of the multi-subunit enzymes within which they reside. We also highlight a recently identified group of tandem-repeat proteins, the RRPNN subclass of TPRs, recent crystal structures of which indicate that they function as allosteric switches to modulate multiple bacterial quorum-sensing mechanisms. We believe that the folding cooperativity of tandem-repeat proteins and the biophysical mechanisms that transform them into allosteric switches are intimately intertwined. This opinion piece aims to combine our understanding of the two areas and develop ideas on their common underlying principles. [ABSTRACT FROM AUTHOR]

The LeuT-fold superfamily includes secondary active transporters from different functional families, which share a common tertiary structure, despite having a remarkably low sequence similarity. By identifying the common structural and dynamical features upon principal component analysis of a comprehensive ensemble of 90 experimentally resolved structures and anisotropic network model evaluation of collective motions, we provide a unified point of view for understanding the reasons why this particular fold has been selected by evolution to accomplish such a broad spectrum of functions. The parallel identification of conserved sequence features, localized at specific sites of transmembrane helices, sheds light on the role of broken helices (TM1 and TM6 in LeuT) in promoting ion/substrate binding and allosteric interconversion between the outward- and inward-facing conformations of transporters. Finally, the determination of the dynamics landscape for the structural ensemble provides a promising framework for the classification of transporters based on their dynamics, and the characterization of the collective movements that favour multimerization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Predicting the binding affinity between protein monomers is of paramount importance for the understanding of thermodynamical and structural factors that guide the formation of a complex. Several numerical techniques have been developed for the calculation of binding affinities with different levels of accuracy. Approaches such as thermodynamic integration and Molecular Mechanics/Poisson‐Boltzmann Surface Area (MM/PBSA) methodologies which account for well defined physical interactions offer good accuracy but are computationally demanding. Methods based on the statistical analysis of experimental structures are much cheaper but good performances have only been obtained throughout consensus energy functions based on many different molecular descriptors. In this study we investigate the importance of the contribution to the binding free energy of the entropic term due to the fluctuations around the equilibrium structures. This term, which we estimated employing an elastic network model, is usually neglected in most statistical approaches. Our method crucially relies on a novel calibration procedure of the elastic network force constant. The residue mobility profile is fitted to the one obtained through a short all‐atom molecular dynamics simulation on a subset of residues only. Our results show how the proper consideration of vibrational entropic contributions can improve the quality of the prediction on a set of non‐obligatory protein complexes whose binding affinity is known. [ABSTRACT FROM AUTHOR]

Intracellular aggregation of proteins is thought to be involved in the aetiology of various neurodegenerative diseases. In particular, mutations in the SOD1 gene are linked to the familial form of amyotrophic lateral sclerosis (ALS). Recently, we developed a regression model for estimating the survival time of ALS patients carrying mutations in SOD1. This model was built based on an analysis of the stability of hydrogen bonds formed in SOD1 mutant proteins during a molecular dynamics (MD) simulation. In the present paper, the regression model was improved by taking into account a new hydrogen-bond property that reflects the conservation measure of a hydrogen bond in the space of protein conformational states. Conformational conservation of hydrogen bonds, being obtained with elastic network (EN) models, allowed us to find eight hydrogen bonds that might affect the pathogenic SOD1 mutants’ properties in addition to the bonds that were found via MD in our previous work. The correlation coefficient between survival time of patients with ALS-linked mutations in SOD1 predicted within the improved model and that observed in the literature was 0.91. SOD1 amino acid residues forming these pathogenic hydrogen bonds are found in zinc-binding and electrostatic loops as well as at zinc-binding sites and are in contact with SOD1 aggregates, which implies that these regions are sensitive to perturbations from pathogenic mutations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ABSTRACT It is known that over half of the proteins encoded by most organisms function as oligomeric complexes. Oligomerization confers structural stability and dynamics changes in proteins. We investigate the effects of oligomerization on protein dynamics and its functional significance for a set of 145 multimeric proteins. Using coarse-grained elastic network models, we inspect the changes in residue fluctuations upon oligomerization and then compare with residue conservation scores to identify the functional significance of these changes. Our study reveals conservation of about ½ of the fluctuations, with ¼ of the residues increasing in their mobilities and ¼ having reduced fluctuations. The residues with dampened fluctuations are evolutionarily more conserved and can serve as orthosteric binding sites, indicating their importance. We also use triosephosphate isomerase as a test case to understand why certain enzymes function only in their oligomeric forms despite the monomer including all required catalytic residues. To this end, we compare the residue communities (groups of residues which are highly correlated in their fluctuations) in the monomeric and dimeric forms of the enzyme. We observe significant changes to the dynamical community architecture of the catalytic core of this enzyme. This relates to its functional mechanism and is seen only in the oligomeric form of the protein, answering why proteins are oligomeric structures. Proteins 2017; 85:1422-1434. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We investigate the relationship between the motions of the same peptide loop segment incorporated within a protein structure and motions of free or end-constrained peptides. As a reference point we also compare against alanine chains having the same length as the loop. Both the analysis of atomic molecular dynamics trajectories and structure-based elastic network models, reveal no general dependence on loop length or on the number of solvent exposed residues. Rather, the whole structure affects the motions in complex ways that depend strongly and specifically on the tertiary structure of the whole protein. Both the Elastic Network Models and Molecular Dynamics confirm the differences in loop dynamics between the free and structured contexts; there is strong agreement between the behaviors observed from molecular dynamics and the elastic network models. There is no apparent simple relationship between loop mobility and its size, exposure, or position within a loop. Free peptides do not behave the same as the loops in the proteins. Surface loops do not behave as if they were random coils, and the tertiary structure has a critical influence upon the apparent motions. This strongly implies that entropy evaluation of protein loops requires knowledge of the motions of the entire protein structure.

PR65, a horseshoe-shaped scaffold composed of 15 HEAT (observed in Huntingtin, elongation factor 3, protein phosphatase 2A, and the yeast kinase TOR1) repeats, forms, together with catalytic and regulatory subunits, the heterotrimeric protein phosphatase PP2A. We examined the role of PR65 in enabling PP2A enzymatic activity with computations at various levels of complexity, including hybrid approaches that combine full-atomic and elastic network models. Our study points to the high flexibility of this scaffold allowing for end-to-end distance fluctuations of 40–50 Å between compact and extended conformations. Notably, the intrinsic dynamics of PR65 facilitates complexation with the catalytic subunit and is retained in the PP2A complex enabling PR65 to engage the two domains of the catalytic subunit and provide the mechanical framework for enzymatic activity, with support from the regulatory subunit. In particular, the intra-repeat coils at the C-terminal arm play an important role in allosterically mediating the collective dynamics of PP2A, pointing to target sites for modulating PR65 function. [Display omitted] • PR65 is a highly elastic scaffold facilitating the allosteric events within PP2A • PR65 intra-repeat coils stiffen upon participation in the PP2A trimer • The intrinsic dynamics of PR65 predispose it to binding the catalytic subunit C • Global modes of the core enzyme AC facilitate its complexation events Kaynak et al. show that the repeat protein PR65 plays a critical role in mediating the catalytic activity and interactions of PP2A, whose dysregulation is implicated in human cancers. The study demonstrates the unique ability of PR65 architecture to adapt to functional changes in conformation and to mechanical stress. [ABSTRACT FROM AUTHOR]

Ubiquitin-specific protease 7 (USP7) is one of the most abundant deubiquitinases and plays an important role in various malignant tumors. However, the molecular mechanisms underlying USP7's structures, dynamics, and biological significance are yet to be investigated. In this study, we constructed the full-length models of USP7 in both the extended and compact state, and applied elastic network models (ENM), molecular dynamics (MD) simulations, perturbation response scanning (PRS) analysis, residue interaction networks as well as allosteric pocket prediction to investigate allosteric dynamics in USP7. Our analysis of intrinsic and conformational dynamics revealed that the structural transition between the two states is characterized by global clamp motions, during which the catalytic domain (CD) and UBL4-5 domain exhibit strong negative correlations. The PRS analysis, combined with the analysis of disease mutations and post-translational modifications (PTMs) further highlighted the allosteric potential of the two domains. The residue interaction network based on MD simulations captured an allosteric communication path which starts at CD domain and ends at UBL4-5 domain. Moreover, we identified a pocket at the TRAF-CD interface as a high-potential allosteric site for USP7. Overall, our studies not only provide molecular insights into the conformational changes of USP7, but also aid in the design of allosteric modulators that target USP7., Competing Interests: Declaration of competing interest The authors confirm that there are no conflicts of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

ABSTRACT Motivated by single molecule experiments and recent molecular dynamics (MD) studies, we propose a simple and computationally efficient method based on a tensorial elastic network model to investigate the unfolding pathways of proteins under temperature variation. The tensorial elastic network model, which relies on the native state topology of a protein, combines the anisotropic network model, the bond bending elasticity, and the backbone twist elasticity to successfully predicts both the isotropic and anisotropic fluctuations in a manner similar to the Gaussian network model and anisotropic network model. The unfolding process is modeled by breaking the native contacts between residues one by one, and by assuming a threshold value for strain fluctuation. Using this method, we simulated the unfolding processes of four well-characterized proteins: chymotrypsin inhibitor, barnase, ubiquitein, and adenalyate kinase. We found that this step-wise process leads to two or more cooperative, first-order-like transitions between partial denaturation states. The sequence of unfolding events obtained using this method is consistent with experimental and MD studies. The results also imply that the native topology of proteins 'encrypts' information regarding their unfolding process. Proteins 2016; 84:1767-1775. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT In multi-resolution simulations, different system components are simultaneously modeled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of ligand binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modeled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity by exploring the reproduction of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function. Proteins 2016; 84:1902-1913. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Can one infer the amino acids of the enzymes that are responsible for the stability or the level of the catalytic activity by computationally experimenting on the inhibited enzyme in the enzyme–inhibitor complex? In this article, we answer this question positively both by designing molecular dynamics simulations and by devising coarse-grained methodologies on the subtilisin serine protease. Both methodologies are based on the cross-correlations of the fluctuations of the residues, obtained either by monitoring the trajectories from the simulation or by constructing the inverse Laplacian of the elastic network model, of the complex. A perturbation scanning is applied to the complex using these correlations. The results indicate that the two methods almost point out the same regions on the flexible of the enzyme. These regions are: (i) 50–61, (ii) 155–164 and (iii) 192–194, all of which are designated to be important by experimental studies in the literature. [ABSTRACT FROM AUTHOR]

Summary Ionotropic glutamate receptors (iGluRs) are tetrameric ion channels that mediate excitatory neurotransmission. Recent structures of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) and N -methyl-D-aspartate (NMDA) receptors permit a comparative analysis of whole-receptor dynamics for the first time. Despite substantial differences in the packing of their two-domain extracellular region, the two iGluRs share similar dynamics, elucidated by elastic network models. Motions accessible to either structure enable conformational interconversion, such as compression of the AMPA receptor toward the more tightly packed NMDA receptor conformation, which has been linked to allosteric regulation. Pivoting motions coupled to concerted rotations of the transmembrane ion channel are prominent between dimers of distal N-terminal domains in the loosely packed AMPA receptor. The occurrence and functional relevance of these motions is verified by cross-linking experiments designed to probe the computationally predicted distance changes. Together with the identification of hotspot residues acting as mediators of allosteric communication, our data provide a glimpse into the dynamic spectrum of iGluRs. [ABSTRACT FROM AUTHOR]

Background Elastic network models (ENMs) are based on the simple idea that a protein can be described as a set of particles connected by springs, which can then be used to describe its intrinsic flexibility using, for example, normal mode analysis. Since the introduction of the first ENM by Monique Tirion in 1996, several variants using coarser protein models have been proposed and their reliability for the description of protein intrinsic dynamics has been widely demonstrated. Lately an increasing number of studies have focused on the meaning of slow dynamics for protein function and its potential conservation through evolution. This leads naturally to comparisons of the intrinsic dynamics of multiple protein structures with varying levels of similarity. Scope of review We describe computational strategies for calculating and comparing intrinsic dynamics of multiple proteins using elastic network models, as well as a selection of examples from the recent literature. Major conclusions The increasing interest for comparing dynamics across protein structures with various levels of similarity, has led to the establishment and validation of reliable computational strategies using ENMs. Comparing dynamics has been shown to be a viable way for gaining greater understanding for the mechanisms employed by proteins for their function. Choices of ENM parameters, structure alignment or similarity measures will likely influence the interpretation of the comparative analysis of protein motion. General significance Understanding the relation between protein function and dynamics is relevant to the fundamental understanding of protein structure–dynamics–function relationship. This article is part of a Special Issue entitled Recent developments of molecular dynamics. [ABSTRACT FROM AUTHOR]

Background Normal mode analysis (NMA) using elastic network models is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. Further insight into the dynamics?function relationship can be gained by comparing protein motions between protein homologs and functional classifications. This can be achieved by comparing normal modes obtained from sets of evolutionary related proteins. Results We have developed an automated tool for comparative NMA of a set of pre-aligned protein structures. The user can submit a sequence alignment in the FASTA format and the corresponding coordinate files in the Protein Data Bank (PDB) format. The computed normalised squared atomic fluctuations and atomic deformation energies of the submitted structures can be easily compared on graphs provided by the web user interface. The web server provides pairwise comparison of the dynamics of all proteins included in the submitted set using two measures: the Root Mean Squared Inner Product and the Bhattacharyya Coefficient. The Comparative Analysis has been implemented on our web server for NMA, WEBnm@, which also provides recently upgraded functionality for NMA of single protein structures. This includes new visualisations of protein motion, visualisation of inter-residue correlations and the analysis of conformational change using the overlap analysis. In addition, programmatic access to WEBnm@ is now available through a SOAP-based web service. Webnm@ is available at http://apps.cbu.uib.no/webnma. Conclusion WEBnm@ v2.0 is an online tool offering unique capability for comparative NMA on multiple protein structures. Along with a convenient web interface, powerful computing resources, and several methods for mode analyses, WEBnm@ facilitates the assessment of protein flexibility within protein families and superfamilies. These analyses can give a good view of how the structures move and how the flexibility is conserved over the different structures. [ABSTRACT FROM AUTHOR]

ABSTRACT Normal mode analysis (NMA) has been a powerful tool for studying protein dynamics. Elastic network models (ENM), through their simplicity, have made normal mode computations accessible to a much broader research community and for many more biomolecular systems. The drawback of ENMs, however, is that they are less accurate than NMA. In this work, through steps of simplification that starts with NMA and ends with ENMs we build a tight connection between NMA and ENMs. In the process of bridging between the two, we have also discovered several high-quality simplified models. Our best simplified model has a mean correlation with the original NMA that is as high as 0.88. In addition, the model is force-field independent and does not require energy minimization, and thus can be applied directly to experimental structures. Another benefit of drawing the connection is a clearer understanding why ENMs work well and how it can be further improved. We discovered that [ABSTRACT FROM AUTHOR]