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

Phosphate uptake in PhoX: Molecular mechanisms

PhoX is a high-affinity phosphate binding protein, present in Xanthomonas citri, a phytopathogen responsible for the citrus canker disease. Performing molecular dynamics simulations and different types of computational analyses, we study the molecular mechanisms at play in relation to phosphate binding, revealing the global functioning of the protein: PhoX naturally oscillates along its global normal modes, which allow it to explore both bound and unbound conformations, eventually attracting a nearby negative phosphate ion to the highly positive electrostatic potential on its surface, particularly close to the binding pocket. There, several hydrogen bonds are formed with the two main domains of the structure. Phosphate creates, in this way, a strong bridge that connects the domains, keeping itself between them, in a tight closed conformation, explaining its high binding affinity.

CryoEM-sampling of metastable conformations appearing in cofactor-ligand association and catalysis of glutamate dehydrogenase

Kinetic aspects of enzymatic reactions are described by equations based on the Michaelis-Menten theory for the initial stage. However, the kinetic parameters provide little information on the atomic mechanism of the reaction. In this study, we analyzed structures of glutamate dehydrogenase in the initial and steady stages of the reaction using cryoEM at near-atomic resolution. In the initial stage, four metastable conformations displayed different domain motions and cofactor/ligand association modes. The most striking finding was that the enzyme-cofactor-substrate complex, treated as a single state in the enzyme kinetic theory, comprised at least three different metastable conformations. In the steady stage, seven conformations, including derivatives from the four conformations in the initial stage, made the reaction pathway complicated. Based on the visualized conformations, we discussed stage-dependent pathways to illustrate the dynamics of the enzyme in action.

Alternate conformations found in protein structures implies biological functions: A case study using cyclophilin A

Protein dynamics linked to numerous biomolecular functions, such as ligand binding, allosteric regulation, and catalysis, must be better understood at the atomic level. Reactive atoms of key residues drive a repertoire of biomolecular functions by flipping between alternate conformations or conformational substates, seldom found in protein structures. Probing such sparsely sampled alternate conformations would provide mechanistic insight into many biological functions. We are therefore interested in evaluating the instance of amino acids adopted alternate conformations, either in backbone or side-chain atoms or in both. Accordingly, over 70000 protein structures appear to contain alternate conformations only 'A' and 'B' for any atom, particularly the instance of amino acids that adopted alternate conformations are more for Arg, Cys, Met, and Ser than others. The resulting protein structure analysis depicts that amino acids with alternate conformations are mainly found in the helical and β-regions and are often seen in high-resolution X-ray crystal structures. Furthermore, a case study on human cyclophilin A (CypA) was performed to explain the pre-existing intrinsic dynamics of catalytically critical residues from the CypA and how such intrinsic dynamics perturbed upon Ser99Thr mutation using molecular dynamics simulations on the ns-μs timescale. Simulation results demonstrated that the Ser99Thr mutation had impaired the alternate conformations or the catalytically productive micro-environment of Phe113, mimicking the experimentally observed perturbation captured by X-ray crystallography. In brief, a deeper comprehension of alternate conformations adopted by the amino acids may shed light on the interplay between protein structure, dynamics, and function.

Allosteric communication and signal transduction in proteins

Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based framework, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.

Coenzyme-binding pathway on glutamate dehydrogenase suggested from multiple-binding sites visualized by cryo-electron microscopy

The structure of hexameric glutamate dehydrogenase (GDH) in the presence of the coenzyme nicotinamide adenine dinucleotide phosphate (NADP) was visualized using cryogenic transmission electron microscopy to investigate the ligand-binding pathways to the active site of the enzyme. Each subunit of GDH comprises one hexamer-forming core domain and one nucleotide-binding domain (NAD domain), which spontaneously opens and closes the active-site cleft situated between the two domains. In the presence of NADP, the potential map of GDH hexamer, assuming D3 symmetry, was determined at a resolution of 2.4 Å, but the NAD domain was blurred due to the conformational variety. After focused classification with respect to the NAD domain, the potential maps interpreted as NADP molecules appeared at five different sites in the active-site cleft. The subunits associated with NADP molecules were close to one of the four metastable conformations in the unliganded state. Three of the five binding sites suggested a pathway of NADP molecules to approach the active-site cleft for initiating the enzymatic reaction. The other two binding modes may rarely appear in the presence of glutamate, as demonstrated by the reaction kinetics. Based on the visualized structures and the results from the enzymatic kinetics, we discussed the binding modes of NADP to GDH in the absence and presence of glutamate.

Free Energy Surface and Molecular Mechanism of Slow Structural Transitions in Lipid Bilayers

Lipid membrane remodeling, crucial for many cellular processes, is governed by the coupling of membrane structure and shape fluctuations. Given the importance of the ∼ nm length scale, details of the transition intermediates for conformational change are not fully captured by a continuum-mechanical description. Slow dynamics and the lack of knowledge of reaction coordinates (RCs) for biasing methods pose a challenge for all-atom (AA) simulations. Here, we map system dynamics on Langevin dynamics in a normal mode space determined from an elastic network model representation for the lipid-water Hamiltonian. AA molecular dynamics (MD) simulations are used to determine model parameters, and Langevin dynamics predictions for bilayer structural, mechanical, and dynamic properties are validated against MD simulations and experiments. Transferability to describe the dynamics of a larger lipid bilayer and a heterogeneous membrane-protein system is assessed. A set of generic RCs for pore formation in two tensionless bilayers is obtained by coupling Langevin dynamics to the underlying energy landscape for membrane deformations. Structure evolution is carried out by AA MD, wherein the generic RCs are used in a path metadynamics or an umbrella sampling simulation to determine the thermodynamics of pore formation and its molecular determinants, such as the role of distinct bilayer motions, lipid solvation, and lipid packing.

Juno and CD9 protein network organization in oolemma of mouse oocyte

Juno and CD9 protein, expressed in oolemma, are known to be essential for sperm-oocyte binding and fusion. Although evidence exists that these two proteins cooperate, their interaction has not yet been demonstrated. Here in, we present Juno and CD9 mutual localization over the surface of mouse metaphase II oocytes captured using the 3D STED super-resolution technique. The precise localization of examined proteins was identified in different compartments of oolemma such as the microvillar membrane, planar membrane between individual microvilli, and the membrane of microvilli-free region. Observed variance in localization of Juno and CD9 was confirmed by analysis of transmission and scanning electron microscopy images, which showed a significant difference in the presence of proteins between selected membrane compartments. Colocalization analysis of super-resolution images based on Pearson's correlation coefficient supported evidence of Juno and CD9 mutual position in the oolemma, which was identified by proximity ligation assay. Importantly, the interaction between Juno and CD9 was detected by co-immunoprecipitation and mass spectrometry in HEK293T/17 transfected cell line. For better understanding of experimental data, mouse Juno and CD9 3D structure were prepared by comparative homology modelling and several protein-protein flexible sidechain dockings were performed using the ClusPro server. The dynamic state of the proteins was studied in real-time at atomic level by molecular dynamics (MD) simulation. Docking and MD simulation predicted Juno-CD9 interactions and stability also suggesting an interactive mechanism. Using the multiscale approach, we detected close proximity of Juno and CD9 within microvillar oolemma however, not in the planar membrane or microvilli-free region. Our findings show yet unidentified Juno and CD9 interaction within the mouse oolemma protein network prior to sperm attachment. These results suggest that a Juno and CD9 interactive network could assist in primary Juno binding to sperm Izumo1 as a prerequisite to subsequent gamete membrane fusion.

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions

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.

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Computational methods for the analysis of solution small-angle X-ray scattering of biomolecules: ATSAS

The ATSAS software suite provides a comprehensive set of programs for the processing, analysis and modeling of small-angle scattering data, tailored for but not limited to data acquired on biological macromolecules. In this review the major components and developments in the ATSAS package are described, with a focus on user driven application. Data reduction, analysis and modeling approaches and strategies will be introduced and discussed. At the time of writing the latest package, ATSAS 3.1, is freely available for academic users at: https://www.embl-hamburg.de/biosaxs/software.html.

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand-protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein-ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand-protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein-ligand systems, namely, adenylate kinase-di(adenosine-5')pentaphosphate, ribose binding protein-β-d-ribopyranose, DNA β-glucosyltransferase-uridine-5'-diphosphate, and G-protein α subunit-guanosine-5'-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

Response of Terahertz Protein Vibrations to Ligand Binding: Calmodulin-Peptide Complexes as a Case Study

Terahertz vibrations are sensitive reporters of the structure and interactions of proteins. Ligand binding alters the nature and distribution of these collective vibrations. The ligand-induced changes in the terahertz protein vibrations contribute to the binding entropy and to the overall thermodynamic stability of the resultant protein-ligand complexes. Here, we have examined the response of the low-frequency (below 6 terahertz) collective vibrations of the calcium-loaded calmodulin (CaM) to binding to five different ligands, both in the presence and absence of water, using normal-mode analysis and molecular dynamics simulations. A comparison of the vibrational spectra of hydrated and dry systems reveals that protein-solvent interactions stiffen the terahertz protein vibrations and that these solvent-coupled collective vibrations contribute significantly to the hydration-sensitive variation in the vibrational entropy of CaM. In the absence of water, the low-frequency vibrations of CaM are stiffened by ligand binding. On the contrary, the number and the cumulative vibrational entropy of low-frequency vibrational modes (ω < 200 cm^-1) of the hydrated CaM are increased noticeably after binding to the peptides, indicating binding-induced softening of collective vibrations of the protein. Although the calculated and experimental binding affinities of the chosen complexes correlated reasonably well, no systematic correlation was observed between the protein vibrational entropy and the binding affinity. The results underscored the importance of the interplay of protein-ligand and solvent interactions in modulating the low-frequency vibrations of proteins.

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both.

A stepwise docking molecular dynamics approach for simulating antibody recognition with substantial conformational changes

Conformational changes or rearrangements are common events during inter-biomolecular recognition. Tracking these changes are essential for exploring the allosteric mechanism and it is usually achieved by molecular dynamics simulation in silico. We previously identified a broad-neutralizing antibody against H5 influenza virus, 13D4, and solved the crystal structures of the free 13D4 Fab and its complex with hemagglutinin (HA). Structural comparison of the unbound and bound 13D4 Fabs showed that the heavy chain complementarity-determining region 3 (HCDR3) undergoes a substantial conformational rearrangement when it recognizes the receptor-binding site (RBS). Here, we used molecular dynamics (MD) to simulate the conformational changes that occur during antibody recognition. We showed that neither conventional MD nor steered MD could recapitulate the loop fitting of the RBS structure contour. Consequently, to simulate these challenging conformational changes, we engaged a stepwise docking MD method that allowed for the gradual docking of the ligand to receptor. This new method recapitulates the bound shape of the HCDR3 and provides the best approximation of the shape rendered by the co-crystal structure, with an RMSD of 0.926 Å. This strategy affords a flexible MD approach for simulating complicated conformational changes that occur during molecular recognition, and helps to provide an understanding of the involved allosteric mechanism.

The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.

Parameterizing elastic network models to capture the dynamics of proteins

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.

Fuzzy binding model of molecular interactions between tropoelastin and integrin alphaVbeta3

Tropoelastin is the highly flexible monomer subunit of elastin, required for the resilience of the extracellular matrix in elastic tissues. To elicit biological signaling, multiple sites on tropoelastin bind to cell surface integrins in a poorly understood multifactorial process. We constructed a full atomistic molecular model of the interactions between tropoelastin and integrin α_vβ₃ using ensemble-based computational methodologies. Conformational changes of integrin α_vβ₃ associated with outside-in signaling were more frequently facilitated in an ensemble in which tropoelastin bound the integrin's α1 helix rather than the upstream canonical binding site. Our findings support a model of fuzzy binding, whereby many tropoelastin conformations and defined sites cooperatively interact with multiple α_vβ₃ regions. This model explains prior experimental binding to distinct tropoelastin regions, domains 17 and 36, and points to the cooperative participation of domain 20. Our study highlights the utility of ensemble-based approaches in helping to understand the interactive mechanisms of functionally significant flexible proteins.

State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps

The eukaryotic chaperonin TRiC/CCT plays a major role in assisting the folding of many proteins through an ATP-driven allosteric cycle. Recent structures elucidated by cryo-electron microscopy provide a broad view of the conformations visited at various stages of the chaperonin cycle, including a sequential activation of its subunits in response to nucleotide binding. But we lack a thorough mechanistic understanding of the structure-based dynamics and communication properties that underlie the TRiC/CCT machinery. In this study, we present a computational methodology based on elastic network models adapted to cryo-EM density maps to gain a deeper understanding of the structure-encoded allosteric dynamics of this hexadecameric machine. We have analysed several structures of the chaperonin resolved in different states toward mapping its conformational landscape. Our study indicates that the overall architecture intrinsically favours cooperative movements that comply with the structural variabilities observed in experiments. Furthermore, the individual subunits CCT1-CCT8 exhibit state-dependent sequential events at different states of the allosteric cycle. For example, in the ATP-bound state, subunits CCT5 and CCT4 selectively initiate the lid closure motions favoured by the overall architecture; whereas in the apo form of the heteromer, the subunit CCT7 exhibits the highest predisposition to structural change. The changes then propagate through parallel fluxes of allosteric signals to neighbours on both rings. The predicted state-dependent mechanisms of sequential activation provide new insights into TRiC/CCT intra- and inter-ring signal transduction events.

Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2

Since the worldwide outbreak of the infectious disease COVID-19, several studies have been published to understand the structural mechanism of the novel coronavirus SARS-CoV-2. During the infection process, the SARS-CoV-2 spike (S) protein plays a crucial role in the receptor recognition and cell membrane fusion process by interacting with the human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants of these spike proteins emerge as the virus passes through the disease reservoir. This poses a major challenge for designing a potent antigen for an effective immune response against the spike protein. Through a normal mode analysis (NMA) we identified the highly flexible region in the receptor binding domain (RBD) of SARS-CoV-2, starting from residue 475 up to residue 485. Structurally, the position S477 shows the highest flexibility among them. At the same time, S477 is hitherto the most frequently exchanged amino acid residue in the RBDs of SARS-CoV-2 mutants. Therefore, using MD simulations, we have investigated the role of S477 and its two frequent mutations (S477G and S477N) at the RBD during the binding to hACE2. We found that the amino acid exchanges S477G and S477N strengthen the binding of the SARS-COV-2 spike with the hACE2 receptor.

ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis

The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus-based concentration-independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi-atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy - a PyMOL plugin to run a subset of ATSAS tools - to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html.

Structural Insight on Functional Regulation of Human MINERVA Protein

MINERVA (melanoma invasion by ERK), also known as FAM129B, is a member of the FAM129 protein family, which is only present in vertebrates. MINERVA is involved in key signaling pathways regulating cell survival, proliferation and apoptosis and found upregulated in many types of cancer promoting invasion. However, the exact function of the protein remains elusive. X-ray crystallographic methods were implemented to determine the crystal structure of MINERVA^ΔC, lacking C-terminal flexible region. Trypsin digestion was required before crystallization to obtain diffraction-quality crystals. While the N-terminal pleckstrin homology (PH) domain exhibits the typical fold of PH domains, lipid binding assay indicates specific affinity towards phosphatidic acid and inositol 3-phosphate. A helix-rich domain that constitutes the rest of the molecule demonstrates a novel L-shaped fold that encompasses the PH domain. The overall structure of MINERVA^ΔC with binding assays and cell-based experiments suggest plasma membrane association of MINERVA and its function seem to be tightly regulated by various motifs within the C-terminal flexible region. Elucidation of MINERVA^ΔC structure presents a novel fold for an α-helix bundle domain that would provide a binding platform for interacting partners.

Physical Constraints on Epistasis

Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.

Motifs and interface amino acid-mediated regulation of amyloid biogenesis in microbes to humans: potential targets for intervention

Amyloids are linked to many debilitating diseases in mammals. Some organisms produce amyloids that have a functional role in the maintenance of their biological processes. Microbes utilize functional bacterial amyloids (FuBA) for pathogenicity and infections. Amyloid biogenesis is regulated differentially in various systems to avoid its toxic accumulation. A familiar feature in the process of amyloid biogenesis from humans to microbes is its regulation by protein-protein interactions (PPI). The spatial arrangement of amino acid residues in proteins generates topologies like flat interface and linear motif, which participate in protein interactions. Motifs and interface residue-mediated interactions have a direct or an indirect impact on amyloid secretion and assembly. Some motifs undergo post-translational modifications (PTM), which effects interactions and dynamics of the amyloid biogenesis cascade. Interaction-induced local changes stimulate global conformational transitions in the PPI complex, which indirectly affects amyloid formation. Perturbation of such motifs and interface residues results in amyloid abolishment. Interface residues, motifs and their respective interactive protein partners could serve as potential targets for intervention to inhibit amyloid biogenesis.

ODiNPred: comprehensive prediction of protein order and disorder

Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

Predicted dynamical couplings of protein residues characterize catalysis, transport and allostery

MOTIVATION

Protein function is intrinsically linked to native dynamics, but the systematic characterization of functionally relevant dynamics remains elusive besides specific examples. Here we exhaustively characterize three types of dynamical couplings between protein residues: co-directionality (moving along collinear directions), coordination (small fluctuations of the interatomic distance) and deformation (the extent by which perturbations applied at one residue modify the local structure of the other one), which we analytically compute through the torsional network model.

RESULTS

We find that ligand binding sites are characterized by large within-site coordination and co-directionality, much larger than expected for generic sets of residues with equivalent sequence distances. In addition, catalytic sites are characterized by high coordination couplings with other residues in the protein, supporting the view that the overall protein structure facilitates the catalytic dynamics. The binding sites of allosteric effectors are characterized by comparably smaller coordination and higher within-site deformation than other ligands, which supports their dynamic nature. Allosteric inhibitors are coupled to the active site more frequently through deformation than through coordination, while the contrary holds for activators. We characterize the dynamical couplings of the sodium-dependent Leucine transporter protein (LeuT). The couplings between and within sites progress consistently along the transport cycle, providing a mechanistic description of the coupling between the uptake and release of ions and substrate, and they highlight qualitative differences between the wild-type and a mutant for which chloride is necessary for transport.

AVAILABILITY AND IMPLEMENTATION

The program tnm is freely available at https://github.com/ugobas/tnm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Toward Simple, Predictive Understanding of Protein-Ligand Interactions: Electronic Structure Calculations on Torpedo Californica Acetylcholinesterase Join Forces with the Chemist's Intuition

Contemporary efforts for empirically-unbiased modeling of protein-ligand interactions entail a painful tradeoff – as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. We demonstrate that information drawn exclusively from static molecular structures can be used for reproducing and predicting experimentally-measured binding affinities for protein-ligand complexes. In particular, inhibition constants (K_i) were calculated for seven different competitive inhibitors of Torpedo californica acetylcholinesterase using a multiple-linear-regression-based model. The latter, incorporating five independent variables – drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the seven complexes, each containing active amino-acid residues found within interacting distance (3.5 Å) from the corresponding ligand – is shown to recover 99.9% of the sum of squares for measured K_i values, while having no statistically-significant residual errors. Despite being fitted to a small number of data points, leave-one-out cross-validation statistics suggest that it possesses surprising predictive value (Q²_LOO=0.78, or 0.91 upon removal of a single outlier). This thus challenges ligand-invariant definitions of active sites, such as implied in the lock-key binding theory, as well as in alternatives highlighting shape-complementarity without taking electronic effects into account. Broader implications of the current work are discussed in dedicated appendices.

Identification of Alternative Allosteric Sites in Glycolytic Enzymes for Potential Use as Species-Specific Drug Targets

Three allosteric glycolytic enzymes, phosphofructokinase, glyceraldehyde-3 phosphate dehydrogenase and pyruvate kinase, associated with bacterial, parasitic and human species, were explored to identify potential allosteric sites that would be used as prime targets for species-specific drug design purposes using a newly developed approach which incorporates solvent mapping, elastic network modeling, sequence and structural alignments. The majority of binding sites detected by solvent mapping overlapped with the interface regions connecting the subunits, thus appeared as promising target sites for allosteric regulation. Each binding site was then evaluated by its ability to alter the global dynamics of the receptor defined by the percentage change in the frequencies of the lowest-frequency modes most significantly and as anticipated, the most effective ones were detected in the vicinity of the well-reported catalytic and allosteric sites. Furthermore, some of our proposed regions intersected with experimentally resolved sites which are known to be critical for activity regulation, which further validated our approach. Despite the high degree of structural conservation encountered between bacterial/parasitic and human glycolytic enzymes, the majority of the newly presented allosteric sites exhibited a low degree of sequence conservation which further increased their likelihood to be used as species-specific target regions for drug design studies.

The Pierced Lasso Topology Leptin has a Bolt on Dynamic Domain Composed by the Disordered Loops I and III

Leptin is an important signaling hormone, mostly known for its role in energy expenditure and satiety. Furthermore, leptin plays a major role in other proteinopathies, such as cancer, marked hyperphagia, impaired immune function, and inflammation. In spite of its biological relevance in human health, there are no NMR resonance assignments of the human protein available, obscuring high-resolution characterization of the soluble protein and/or its conformational dynamics, suggested as being important for receptor interaction and biological activity. Here, we report the nearly complete backbone resonance assignments of human leptin. Chemical shift-based secondary structure prediction confirms that in solution leptin forms a four-helix bundle including a pierced lasso topology. The conformational dynamics, determined on several timescales, show that leptin is monomeric, has a rigid four-helix scaffold, and a dynamic domain, including a transiently formed helix. The dynamic domain is anchored to the helical scaffold by a secondary hydrophobic core, pinning down the long loops of leptin to the protein body, inducing motional restriction without a well-defined secondary or tertiary hydrogen bond stabilized structure. This dynamic region is well suited for and may be involved in functional allosteric dynamics upon receptor binding.

Conformational gating in ammonia lyases

BACKGROUND

Ammonia lyases are enzymes of industrial and biomedical interest. Knowledge of structure-dynamics-function relationship in ammonia lyases is instrumental for exploiting the potential of these enzymes in industrial or biomedical applications.

METHODS

We investigated the conformational changes in the proximity of the catalytic pocket of a 3-methylaspartate ammonia lyase (MAL) as a model system. At this scope, we used microsecond all-atom molecular dynamics simulations, analyzed with dimensionality reduction techniques, as well as in terms of contact networks and correlated motions.

RESULTS

We identify two regulatory elements in the MAL structure, i.e., the β5-α2 loop and the helix-hairpin-loop subdomain. These regulatory elements undergo conformational changes switching from 'occluded' to 'open' states. The rearrangements are coupled to changes in the accessibility of the active site. The β5-α2 loop and the helix-hairpin-loop subdomain modulate the formation of tunnels from the protein surface to the catalytic site, making the active site more accessible to the substrate when they are in an open state.

CONCLUSIONS

Our work pinpoints a sequential mechanism, in which the helix-hairpin-loop subdomain of MAL needs to break a subset of intramolecular interactions first to favor the displacement of the β5-α2 loop. The coupled conformational changes of these two elements contribute to modulate the accessibility of the catalytic site.

GENERAL SIGNIFICANCE

Similar molecular mechanisms can have broad relevance in other ammonia lyases with similar regulatory loops. Our results also imply that it is important to account for protein dynamics in the design of variants of ammonia lyases for industrial and biomedical applications.

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions

Despite the wealth of methods developed for exploring the molecular basis of allostery in biomolecular systems, there is still a need for structure-based predictive tools that can efficiently detect susceptible sites for triggering allosteric responses. Toward this goal, we introduce here an elastic network model (ENM)-based method, Essential Site Scanning Analysis (ESSA). Essential sites are here defined as residues that would significantly alter the protein's global dynamics if bound to a ligand. To mimic the crowding induced upon substrate binding, the heavy atoms of each residue are incorporated as additional network nodes into the α-carbon-based ENM, and the resulting shifts in soft mode frequencies are used as a metric for evaluating the essentiality of each residue. Results on a dataset of monomeric proteins indicate the enrichment of allosteric and orthosteric binding sites, as well as global hinge regions among essential residues, highlighting the significant role of these sites in controlling the overall structural dynamics. Further integration of ESSA with information on predicted pockets and their local hydrophobicity density enables successful predictions of allosteric pockets for both ligand-bound and -unbound structures. ESSA can be efficiently applied to large multimeric systems. Three case studies, namely (i) G-protein binding to a GPCR, (ii) heterotrimeric assembly of the Ser/Thr protein phosphatase PP2A, and (iii) allo-targeting of AMPA receptor, demonstrate the utility of ESSA for identifying essential sites and narrowing down target allosteric sites identified by druggability simulations.

Comparative Structural Analysis of Different Mycobacteriophage-Derived Mycolylarabinogalactan Esterases (Lysin B)

Mycobacteriophage endolysins have emerged as a potential alternative to the current antimycobacterial agents. This study focuses on mycolylarabinogalactan hydrolase (LysB) enzymes of the α/β-hydrolase family, which disrupt the unique mycolic acid layer of mycobacterium cell wall. Multiple sequence alignment and structural analysis studies showed LysB-D29, the only enzyme with a solved three-dimensional structure, to share several common features with esterases (lacking lid domain) and lipases (acting on long chain lipids). Sequence and structural comparisons of 30 LysB homology models showed great variation in domain organizations and total protein length with major differences in the loop-5 motif harboring the catalytic histidine residue. Docking of different p-nitrophenyl ligands (C4-C18) to LysB-3D models revealed that the differences in length and residues of loop-5 contributed towards wide diversity of active site conformations (long tunnels, deep and superficial funnels, shallow bowls, and a narrow buried cave) resembling that of lipases, cutinases, and esterases. A set of seven LysB enzymes were recombinantly produced; their activity against p-nitrophenyl esters could be related to their active site conformation and acyl binding site. LysB-D29 (long tunnel) showed the highest activity with long chain p-nitrophenyl palmitate followed by LysB-Omega (shallow bowl) and LysB-Saal (deep funnel).

Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach

The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.

Normal Mode Analysis as a Routine Part of a Structural Investigation

Normal mode analysis (NMA) is a technique that can be used to describe the flexible states accessible to a protein about an equilibrium position. These states have been shown repeatedly to have functional significance. NMA is probably the least computationally expensive method for studying the dynamics of macromolecules, and advances in computer technology and algorithms for calculating normal modes over the last 20 years have made it nearly trivial for all but the largest systems. Despite this, it is still uncommon for NMA to be used as a component of the analysis of a structural study. In this review, we will describe NMA, outline its advantages and limitations, explain what can and cannot be learned from it, and address some criticisms and concerns that have been voiced about it. We will then review the most commonly used techniques for reducing the computational cost of this method and identify the web services making use of these methods. We will illustrate several of their possible uses with recent examples from the literature. We conclude by recommending that NMA become one of the standard tools employed in any structural study.

Structural Consideration of the Working Mechanism of Fold Type I Transaminases From Eubacteria: Overt and Covert Movement

Transaminases (TAs) reversibly catalyze the transfer reaction of an amino group between an amino group donor and an amino group acceptor, using pyridoxal 5′-phosphate (PLP) as a cofactor. TAs are categorized according to the amino group position of the donor substrate and respective TAs recognize their own specific substrates. Over the past decade, a number of TA structures have been determined by X-ray crystallography. On the basis of the structural information, the detailed mechanism of substrate recognition by TAs has also been elucidated. In this review, fold type I TAs are addressed intensively. Comparative studies on structural differences between the apo and holo forms of fold type I TAs have demonstrated that regions containing the active site exhibit structural plasticity in the apo form, facilitating PLP insertion into the active site. In addition, given that TAs recognize two different kinds of substrates, they possess dual substrate specificity. It is known that spatial rearrangements of active site residues occur upon binding of the substrates. Intriguingly, positively charged residues are predominantly distributed at the active site cavity. The electric field generated by such charge distributions may attract negatively charged molecules, such as PLP and amino group acceptors, into the active site. Indeed, TAs show remarkable dynamics in diverse aspects. In this review, we describe the comprehensive working mechanism of fold type I TAs, with a focus on conformational changes.

Intramolecular Domain Movements of Free and Bound pMHC and TCR Proteins: A Molecular Dynamics Simulation Study

The interaction of antigenic peptides (p) and major histocompatibility complexes (pMHC) with T-cell receptors (TCR) is one of the most important steps during the immune response. Here we present a molecular dynamics simulation study of bound and unbound TCR and pMHC proteins of the LC13-HLA-B*44:05-pEEYLQAFTY complex to monitor differences in relative orientations and movements of domains between bound and unbound states of TCR-pMHC. We generated local coordinate systems for MHC α1- and MHC α2-helices and the variable T-cell receptor regions TCR V_α and TCR V_β and monitored changes in the distances and mutual orientations of these domains. In comparison to unbound states, we found decreased inter-domain movements in the simulations of bound states. Moreover, increased conformational flexibility was observed for the MHC α2-helix, the peptide, and for the complementary determining regions of the TCR in TCR-unbound states as compared to TCR-bound states.

On identifying low energy conformational excited states with differential ruggedness in human γS-crystallin promoting severe infantile cataracts

Transient excited states in proteins can be accurately probed from temperature dependence of amide proton (¹H^N) chemical shifts displaying significant curvatures. Characterizing these near-native alternative states is of high therapeutic relevance in conformational diseases wherein missense mutations promote structural instability that leads to conformational heterogeneity. Extending the structure-function paradigm from physiology to pathology, we recently reported the solution NMR structure and dynamics of a severe congenital cataract variant, G57W of human γS-crystallin (abbreviated as γS-G57W) which is resistant towards crystallization. In an endeavour to explore the functional consequences of this mutation, here we report for the first time, native state conformational ruggedness in human γS-G57W as compared to its wild-type counterpart from residue resolved nonlinear temperature dependence of backbone ¹H^N chemical shifts using solution NMR spectroscopy. Our calculations suggest that the simulated chemical shift curvatures are indicative of low energy excited states within 2-4 kcal mol^-1 from the native state. Residues accessing alternative conformations populate the N-terminal domain of γS-G57W more than its C-terminal counterpart. Collectively, curvatures retaining native state ensemble on mild denaturation suggest that the free energy landscape in human γS-G57W at the bottom of the folding funnel is sufficiently robust and malleable against such perturbations. Overall, this critical study highlights the functional aspects of such structural malleability promoting infantile cataracts as a global health risk marker.

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

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

Interplay of self-association and conformational flexibility in regulating protein function

Many functional roles have been attributed to homodimers, the most common mode of protein self-association, notably in the regulation of enzymes, ion channels, transporters and transcription factors. Here we review findings that offer new insights into the different roles conformational flexibility plays in regulating homodimer function. Intertwined homodimers of two-domain proteins and their related family members display significant conformational flexibility, which translates into concerted motion between structural domains. This flexibility enables the corresponding proteins to regulate function across family members by modulating the spatial positions of key recognition surfaces of individual domains, to either maintain subunit interfaces, alter them or break them altogether, leading to a variety of functional consequences. Many proteins may exist as monomers but carry out their biological function as homodimers or higher-order oligomers. We present early evidence that in such systems homodimer formation primes the protein for its functional role. It does so by inducing elevated mobility in protein regions corresponding to the binding epitopes of functionally important ligands. In some systems this process acts as an allosteric response elicited by the self-association reaction itself. Our analysis furthermore suggests that the induced extra mobility likely facilitates ligand binding through the mechanism of conformational selection.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Allostery in Its Many Disguises: From Theory to Applications

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.

Coarse-Grained Simulation of Full-Length Integrin Activation

Integrin conformational dynamics are critical to their receptor and signaling functions in many cellular processes, including spreading, adhesion, and migration. However, assessing integrin conformations is both experimentally and computationally challenging because of limitations in resolution and dynamic sampling. Thus, structural changes that underlie transitions between conformations are largely unknown. Here, focusing on integrin αvβ₃, we developed a modified form of the coarse-grained heterogeneous elastic network model (hENM), which allows sampling conformations at the onset of activation by formally separating local fluctuations from global motions. Both local fluctuations and global motions are extracted from all-atom molecular dynamics simulations of the full-length αvβ₃ bent integrin conformer, but whereas the former are incorporated in the hENM as effective harmonic interactions between groups of residues, the latter emerge by systematically identifying and treating weak interactions between long-distance domains with flexible and anharmonic connections. The new hENM model allows integrins and single-point mutant integrins to explore various conformational states, including the initiation of separation between α- and β-subunit cytoplasmic regions, headpiece extension, and legs opening.

Molecular, Biological and Structural Features of VL CDR-1 Rb44 Peptide, Which Targets the Microtubule Network in Melanoma Cells

Microtubules are important drug targets in tumor cells, owing to their role in supporting and determining the cell shape, organelle movement and cell division. The complementarity-determining regions (CDRs) of immunoglobulins have been reported to be a source of anti-tumor peptide sequences, independently of the original antibody specificity for a given antigen. We found that, the anti-Lewis B mAb light-chain CDR1 synthetic peptide Rb44, interacted with microtubules and induced depolymerization, with subsequent degradation of actin filaments, leading to depolarization of mitochondrial membrane-potential, increase of ROS, cell cycle arrest at G2/M, cleavage of caspase-9, caspase-3 and PARP, upregulation of Bax and downregulation of Bcl-2, altogether resulting in intrinsic apoptosis of melanoma cells. The in vitro inhibition of angiogenesis was also an Rb44 effect. Peritumoral injection of Rb44L1 delayed growth of subcutaneously grafted melanoma cells in a syngeneic mouse model. L1-CDRs from immunoglobulins and their interactions with tubulin-dimers were explored to interpret effects on microtubule stability. The opening motion of tubulin monomers allowed for efficient L1-CDR docking, impairment of dimer formation and microtubule dissociation. We conclude that Rb44 V_L-CDR1 is a novel peptide that acts on melanoma microtubule network causing cell apoptosis in vitro and melanoma growth inhibition in vivo.

Why the Energy Landscape of Barnase Is Hierarchical

We have used NMR and computational methods to characterize the dynamics of the ribonuclease barnase over a wide range of timescales in free and inhibitor-bound states. Using temperature- and denaturant-dependent measurements of chemical shift, we show that barnase undergoes frequent and highly populated hinge bending. Using relaxation dispersion, we characterize a slower and less populated motion with a rate of 750 ± 200 s⁻¹, involving residues around the lip of the active site, which occurs in both free and bound states and therefore suggests conformational selection. Normal mode calculations characterize correlated hinge bending motions on a very rapid timescale. These three measurements are combined with previous measurements and molecular dynamics calculations on barnase to characterize its dynamic landscape on timescales from picoseconds to milliseconds and length scales from 0.1 to 2.5 nm. We show that barnase has two different large-scale fluctuations: one on a timescale of 10⁻⁹−10⁻⁶ s that has no free energy barrier and is a hinge bending that is determined by the architecture of the protein; and one on a timescale of milliseconds (i.e., 750 s⁻¹) that has a significant free energy barrier and starts from a partially hinge-bent conformation. These two motions can be described as hierarchical, in that the more highly populated faster motion provides a platform for the slower (less probable) motion. The implications are discussed. The use of temperature and denaturant is suggested as a simple and general way to characterize motions on the intermediate ns-μs timescale.

Dynamics based clustering of globin family members

A methodology to cluster proteins based on their dynamics’ similarity is presented. For each pair of proteins from a dataset, the structures are superimposed, and the Anisotropic Network Model modes of motions are calculated. The twelve slowest modes from each protein are matched using a local mode alignment algorithm based on the local sequence alignment algorithm of Smith–Waterman. The dynamical similarity distance matrix is calculated based on the top scoring matches of each pair and the proteins are clustered using a hierarchical clustering algorithm. The utility of this method is exemplified on a dataset of protein chains from the globin family and a dataset of tetrameric hemoglobins. The results demonstrate the effect of the quaternary structure of globin members on their intrinsic dynamics and show good ability to distinguish between different states of hemoglobin, revealing the dynamical relations between them.