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Microtubules (MTs) are widely recognized as targets for cancer therapies. They are directly related to unique mechanical properties, closely dependent on MT architecture and tubulin molecular features. Taxol is known to affect tubulin interactions resulting in the stabilization of the MT lattice, and thus the hierarchical organization stability, mechanics, and function. A deeper understanding of the molecular mechanisms through which taxol modulates intertubulin interactions in the MT lattice, and consequently, its stability and mechanical response is crucial to characterize how MT properties are regulated by environmental factors, such as interacting ligands. In this study, a computational analysis of the effect of taxol on the MT was performed at different scales, combining molecular dynamics simulation, dynamical network analysis, and elastic network modeling. The results show that the taxol-induced conformational differences at the M-loop region increase the stability of the lateral interactions and the amount of surface in contact between laterally coupled tubulins. Moreover, the conformational rearrangements in the taxane binding site result in a different structural communication pattern. Finally, the different conformation of the tubulin heterodimers and the stabilized lateral interactions resulted in a tendency toward higher deformation of the vibrating MT in the presence of taxol. Overall, this work provides additional insights into taxol-induced stabilization and relates the conformational changes at the tubulin level to the MT mechanics. Besides providing useful insights into taxol effect on MT mechanics, a methodological framework that could be used to characterize the effects of other MT stabilizing agents is presented.

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Antimicrobial peptides have gradually gained advantages over small molecule inhibitors for their multifunctional effects, synthesising accessibility and target specificity. The current study aims to determine an antimicrobial peptide to inhibit PknB, a serine/threonine protein kinase (STPK), by binding efficiently at the helically oriented hinge region. A library of 5626 antimicrobial peptides from publicly available repositories has been prepared and categorised based on the length. Molecular docking using ADCP helped to find the multiple conformations of the subjected peptides. For each peptide served as input the tool outputs 100 poses of the subjected peptide. To maintain an efficient binding for relatively a longer duration, only those peptides were chosen which were seen to bind constantly to the active site of the receptor protein over all the poses observed. Each peptide had different number of constituent amino acid residues; the peptides were classified based on the length into five groups. In each group the peptide length incremented upto four residues from the initial length form. Five peptides were selected for Molecular Dynamic simulation in Gromacs based on higher binding affinity. Post-dynamic analysis and the frame comparison inferred that neither the shorter nor the longer peptide but an intermediate length of 15 mer peptide bound well to the receptor. Residual substitution to the selected peptides was performed to enhance the targeted interaction. The new complexes considered were further analysed using the Elastic Network Model (ENM) for the functional site's intrinsic dynamic movement to estimate the new peptide's role. The study sheds light on prospects that besides the length of peptides, the combination of constituent residues equally plays a pivotal role in peptide-based inhibitor generation. The study envisages the challenges of fine-tuned peptide recovery and the scope of Machine Learning (ML) and Deep Learning (DL) algorithm development. As the study was primarily meant for generation of therapeutics for Tuberculosis (TB), the peptide proposed by this study demands meticulous invitro analysis prior to clinical applications.

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N-Methyl-D-aspartate (NMDA) receptors are glutamate-gated excitatory channels that play essential roles in brain functions. While high-resolution structures were solved for an allosterically inhibited form of functional NMDA receptor, other key functional states (particularly the active open-channel state) have not yet been resolved at atomic resolutions. To decrypt the molecular mechanism of the NMDA receptor activation, structural modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this chapter, we describe coarse-grained modeling of the NMDA receptor using an elastic network model and related modeling/analysis tools (e.g., normal mode analysis, flexibility and hotspot analysis, cryo-EM flexible fitting, and transition pathway modeling) based on available structures. Additionally, we show how to build an atomistic model of the active-state receptor with targeted molecular dynamics (MD) simulation and explore its energetics and dynamics with conventional MD simulation. Taken together, these modeling and simulation can offer rich structural and dynamic information which will guide experimental studies of the activation of this key receptor.

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Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of normal mode analysis (NMA) exist that perform NMA directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. Spring-based NMA (sbNMA) is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model (ENM) for NMA. The vast majority of ENMs are entropy models. One significance of having a free energy-based model for NMA is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV2 and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.

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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.

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Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

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SARS-CoV-2 virus continues to evolve and mutate causing most of the mutated variants resist to many of the therapeutic monoclonal antibodies (mAbs). Despite several mAbs retained neutralizing capability for Omicron BA.1 and BA.2, reduction in neutralization potency was reported. Hence, effort of searching for mAb that is broader in neutralization breadth without losing the neutralizing ability is continued. MW06 was reported with capability in neutralizing most of the variants of concern (VOC) and it binds to the conserved region (left flank) near epitope mAb sotrovimab (S309). In this study, binding affinity of mAb MW06 and its cocktail formulation with MW05 for receptor binding domain (RBD) SARS-CoV-2 virus was investigated under molecular dynamics simulations (MDs). Binding free energies computed by Molecular Mechanics Generalised Born Surface Area (MM-GBSA) algorithm predicted the binding affinity of MW06 for RBD BA.1 (-53 kcal/mol) as strong as RBD wildtype (-58 kcal/mol) while deterioration was observed for RBD BA.2 (-43 kcal/mol). Alike S309 and MW06, simulated cocktail mAb (MW05 and MW06)-RBD interactions suggested the neutralizing capability of the cocktail formulation for RBD BA.1 and BA.2 reduced. Meanwhile, residue pairs that favour the communication between the mAb and RBD have been identified by decomposing the free energy per pairwise residue basis. Apart from understanding the effects of mutation occurred in the RBD region on human angiotensin-converting enzyme 2 (hACE2) binding, impact of heavily mutated RBD on mAb-RBD interactions was investigated in this study as well. In addition to energetic profile obtained from MDs, plotting the dynamics cross-correlation map of the mAb-RBD complex under elastic network model (ENM) was aimed to understand the cross-correlations between residue fluctuations. It allows simple and rapid analysis on the motions or dynamics of the protein residues of mAbs and RBD in complex. Protein residues having correlated motions are normally part of the structural domains of the protein and their respective motions and protein function are related. Motion of mutated RBD residues and mAb residues was less correlated while their respective interactions energy computed to be higher. The combined techniques of MDs and ENM offered simplicity in understanding dynamics and energy contribution that explain binding affinity of mAb-RBD complexes.

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Ramachandran plots, which describe protein structures by plotting the dihedral angle pairs of the backbone on a two-dimensional plane, have played an important role in structural biology over the past few decades. However, despite continued discovery of new protein structures to date, the Ramachandran plot is still constructed by only a small number of data points, and further it cannot reflect the steric information of proteins. Here, we investigated the secondary structure of proteins in terms of static and dynamic characteristics. As for static feature, the Ramachandran plot was revisited for the dataset consisting of 9,148 non-redundant high-resolution protein structures released in the protein data bank until April 1, 2022. By calculating amino acid propensities, it was found that the proportion of secondary structures with respect to residue depth is directly related to their hydrophobicity. As for dynamic feature, normal mode analysis (NMA) based on an elastic network model (ENM) was carried out for the dataset using our KOSMOS web server (http://bioengineering.skku.ac.kr/kosmos/). All ENM-based NMA results were stored in the KOSMOS database, allowing researchers to use them in various ways. In this process, it was commonly found that high B-factors appeared at the edge of the alpha helix region, which was elucidated by introducing residue depth. In addition, by investigating the change in dihedral angle, it was possible to quantitatively survey the contribution of structural change of protein on the Ramachandran plot. In conclusion, our statistical analysis of protein characteristics will provide insight into a range of protein structural studies.

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Accurate development of allosteric modulators of GPCRs require a thorough assessment of their sequence, structure, and dynamics, toward gaining insights into their mechanisms of actions shared by family members, as well as dynamic features that distinguish subfamilies. Building on recent progress in the characterization of the signature dynamics of proteins, we analyzed here a dataset of 160 Class A GPCRs to determine their sequence similarities, structural landscape, and dynamic features across different species (human, bovine, mouse, squid, and rat), different activation states (active/inactive), and different subfamilies. The two dominant directions of variability across experimentally resolved structures, identified by principal component analysis of the dataset, shed light to cooperative mechanisms of activation, subfamily differentiation, and speciation of Class A GPCRs. The analysis reveals the functional significance of the conformational flexibilities of specific structural elements, including: the dominant role of the intracellular loop 3 (ICL3) together with the cytoplasmic ends of the adjoining helices TM5 and TM6 in enabling allosteric activation; the role of particular structural motifs at the extracellular loop 2 (ECL2) connecting TM4 and TM5 in binding ligands specific to different subfamilies; or even the differentiation of the N-terminal conformation across different species. Detailed analyses of the modes of motions accessible to the members of the dataset and their variations across members demonstrate how the active and inactive states of GPCRs obey distinct conformational dynamics. The collective fluctuations of the GPCRs are robustly defined in the active state, while the inactive conformers exhibit broad variance among members.

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Normal mode analysis (NMA) is a technique for describing the conformational states accessible to a protein in a minimum energy conformation. NMA gives results similar to those produced by principal components analysis of a molecular dynamics simulation, but with only a fraction of the computational effort. Here, we provide a brief overview of the theory and describe three methods for carrying out NMA, including the use of one of the on-line services, the use of off-line software for calculating the projection of the modes calculated from one conformation onto another, and an all-atom NMA calculated using GROMACS. For all three methods, we will use the E1·2Ca2+ form of the Ca2+-ATPase as a concrete example.

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Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

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The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.

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Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem of generating accurate protein conformers using normal coordinates into a better known one: the distance-geometry problem, which is herein solved with the help of the ROSETTA software. In the present study, this approach allowed to rebuild accurately six large amplitude conformational changes, using at most six low-frequency normal coordinates. As a consequence of the low-dimensionality of the corresponding subspace, random exploration also proved enough for generating low-energy conformers close to the known end-point of the conformational change of the LAO binding protein, lysozyme T4 and adenylate kinase.

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Protein dynamics has been investigated since almost half a century, as it is believed to constitute the fundamental connection between structure and function. Elastic network models (ENMs) have been widely used to predict protein dynamics, flexibility and the biological mechanism, from which remarkable results have been found regarding the prediction of protein conformational changes. Starting from the knowledge of the reference structure only, these conformational changes have been usually predicted either by looking at the individual mode shapes of vibrations (i.e., by considering the free vibrations of the ENM) or by applying static perturbations to the protein network (i.e., by considering a linear response theory). In this paper, we put together the two previous approaches and evaluate the complete protein response under the application of dynamic perturbations. Harmonic forces with random directions are applied to the protein ENM, which are meant to simulate the single frequency-dependent components of the collisions of the surrounding particles, and the protein response is computed by solving the dynamic equations in the underdamped regime, where mass, viscous damping and elastic stiffness contributions are explicitly taken into account. The obtained motion is investigated both in the coordinate space and in the sub-space of principal components (PCs). The results show that the application of perturbations in the low-frequency range is able to drive the protein conformational change, leading to remarkably high values of direction similarity. Eventually, this suggests that protein conformational change might be triggered by external collisions and favored by the inherent low-frequency dynamics of the protein structure.

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Allostery is usually considered to be a mechanism for transmission of signals associated with physical or dynamic changes in some part of a protein. Here, we investigate the changes in fluctuations across the protein upon ligand binding based on the fluctuations computed with elastic network models. These results suggest that binding reduces the fluctuations at the binding site but increases fluctuations at remote sites, but not to fully compensating extents. If there were complete conservation of entropy, then only the enthalpies of binding would matter and not the entropies; however this does not appear to be the case. Experimental evidence also suggests that energies and entropies of binding can compensate but that the extent of compensation varies widely from case to case. Our results do however always show transmission of an allosteric signal to distant locations where the fluctuations are increased. These fluctuations could be used to compute entropies to improve evaluations of the thermodynamics of binding. We also show the allosteric relationship between peptide binding in the GroEL trans-ring that leads directly to the release of GroES from the GroEL-GroES cis-ring. This finding provides an example of how calculating these changes to protein dynamics induced by the binding of an allosteric ligand can regulate protein function and mechanism.

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The coxsackievirus A16 (CVA16) is a highly contagious virus that causes the hand, foot, and mouth disease, which seriously threatens the health of children. At present, there are still no available antiviral drugs or effective treatments against the infection of CVA16, and thus it is of great significance to develop anti-CVA16 vaccines. However, the intrinsic uncoating property of the capsid may destroy the neutralizing epitopes and influence its immunogenicity, which hinders the vaccine developments. In the present work, the functional-quantity-based elastic network model analysis method developed by our group was extended to combine with group theory to investigate the uncoating motions of the CVA16 capsid, and then the functionally key residues controlling the uncoating motions were identified by our functional-quantity-based perturbation method. Several motion modes encoded in the topological structure of the capsid were revealed to be responsible for the uncoating of CVA16 particle. These modes predominantly contribute to the fluctuation of the gyration radius of the capsid. Then, by using the perturbation method, four clusters of key sites involved in the uncoating motions were identified, whose perturbations induce significant changes in the fluctuation of the gyration radius. These key residues are mainly located at the 2-fold channels, the quasi 3-fold channels, the bottom of the canyons, and the inter-subunit interfaces around the 3-fold axes. Our studies are helpful for better understanding the uncoating mechanism of the CVA16 capsid and provide potential target sites to prevent the uncoating motions, which is valuable for the vaccine design against CVA16.

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The crystallographic analysis of a lipase from Palaeococcus ferrophilus (PFL) previously annotated as a lysophospholipase revealed high structural conservation with other monoglyceride lipases, in particular in the lid domain and substrate binding pockets. In agreement with this observation, PFL was shown to be active on various monoacylglycerols. Molecular Dynamics (MD) studies performed in the absence and in the presence of ligands further allowed characterization of the dynamics of this system and led to a systematic closure of the lid compared to the crystal structure. However, the presence of ligands in the acyl-binding pocket stabilizes intermediate conformations compared to the crystal and totally closed structures. Several lid-stabilizing or closure elements were highlighted, i.e., hydrogen bonds between Ser117 and Ile204 or Asn142 and its facing amino acid lid residues, as well as Phe123. Thus, based on this complementary crystallographic and MD approach, we suggest that the crystal structure reported herein represents an open conformation, at least partially, of the PFL, which is likely stabilized by the ligand, and it brings to light several key structural features prone to participate in the closure of the lid.

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The 70 kDa and 90 kDa heat shock proteins Hsp70 and Hsp90 are two abundant and highly conserved ATP-dependent molecular chaperones that participate in the maintenance of cellular homeostasis. In Escherichia coli, Hsp90 (Hsp90Ec) and Hsp70 (DnaK) directly interact and collaborate in protein remodeling. Previous work has produced a model of the direct interaction of both chaperones. The locations of the residues involved have been confirmed and the model has been validated. In this study, we investigate the allosteric communication between Hsp90Ec and DnaK and how the chaperones couple their conformational cycles. Using elastic network models (ENM), normal mode analysis (NMA), and a structural perturbation method (SPM) of asymmetric and symmetric DnaK-Hsp90Ec, we extract biologically relevant vibrations and identify residues involved in allosteric signaling. When one DnaK is bound, the dominant normal modes favor biological motions that orient a substrate protein bound to DnaK within the substrate/client binding site of Hsp90Ec and release the substrate from the DnaK substrate binding domain. The presence of one DnaK molecule stabilizes the entire Hsp90Ec protomer to which it is bound. Conversely, the symmetric model of DnaK binding results in steric clashes of DnaK molecules and suggests that the Hsp90Ec and DnaK chaperone cycles operate independently. Together, this data supports an asymmetric binding of DnaK to Hsp90Ec.

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BACKGROUND: The ribose-binding protein (RBP) from Escherichia coli is one of the representative structures of periplasmic binding proteins. Binding of ribose at the cleft between two domains causes a conformational change corresponding to a closure of two domains around the ligand. The RBP has been crystallized in the open and closed conformations. OBJECTIVE: With the complex trajectory as a control, our goal was to study the conformation changes induced by the detachment of the ligand, and the results have been revealed from two computational tools, MD simulations and elastic network models. METHODS: Molecular dynamics (MD) simulations were performed to study the conformation changes of RBP starting from the open-apo, closed-holo and closed-apo conformations. RESULTS: The evolution of the domain opening angle θ clearly indicates large structural changes. The simulations indicate that the closed states in the absence of ribose are inclined to transition to the open states and that ribose-free RBP exists in a wide range of conformations. The first three dominant principal motions derived from the closed-apo trajectories, consisting of rotating, bending and twisting motions, account for the major rearrangement of the domains from the closed to the open conformation. CONCLUSIONS: The motions showed a strong one-to-one correspondence with the slowest modes from our previous study of RBP with the anisotropic network model (ANM). The results obtained for RBP contribute to the generalization of robustness for protein domain motion studies using either the ANM or PCA for trajectories obtained from MD.

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