Elastic Network Model Research Articles

Revisiting the Ramachandran plot based on statistical analysis of static and dynamic characteristics of protein structures

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.

Experimental and Bioinformatic Insights into the Effects of Epileptogenic Variants on the Function and Trafficking of the GABA Transporter GAT-1.

In this article, we identified a novel epileptogenic variant (G307R) of the gene SLC6A1, which encodes the GABA transporter GAT-1. Our main goal was to investigate the pathogenic mechanisms of this variant, located near the neurotransmitter permeation pathway, and compare it with other variants located either in the permeation pathway or close to the lipid bilayer. The mutants G307R and A334P, close to the gates of the transporter, could be glycosylated with variable efficiency and reached the membrane, albeit inactive. Mutants located in the center of the permeation pathway (G297R) or close to the lipid bilayer (A128V, G550R) were retained in the endoplasmic reticulum. Applying an Elastic Network Model, to these and to other previously characterized variants, we found that G307R and A334P significantly perturb the structure and dynamics of the intracellular gate, which can explain their reduced activity, while for A228V and G362R, the reduced translocation to the membrane quantitatively accounts for the reduced activity. The addition of a chemical chaperone (4-phenylbutyric acid, PBA), which improves protein folding, increased the activity of GAT-1WT, as well as most of the assayed variants, including G307R, suggesting that PBA might also assist the conformational changes occurring during the alternative access transport cycle.

Study on the allosteric activation mechanism of SHP2 via elastic network models and neural relational inference molecular dynamics simulation.

As a ubiquitous protein tyrosine phosphatase, SHP2 is involved in PD-1/PD-L1 mediated tumor immune escape and undergoes substantial conformational changes. Therefore, it is considered an ideal target for tumor intervention. However, the allosteric mechanisms of SHP2 binding PD-1 intracellular ITIM/ITSM phosphopeptides remain unclear, which greatly hinders the development of novel structure-based anticancer allosteric inhibitors. In this work, the open and closed structural models of SHP2 are first constructed based on this knowledge; next their motion modes are investigated via elastic network models such as the Gaussian network model (GNM), anisotropic network model (ANM) and adaptive anisotropic network model (aANM); and finally, a possible allosteric signaling pathway is proposed using a neural relational inference molecular dynamics (NRI-MD) simulation embedded with an artificial intelligence (AI) strategy. In GNM and ANM, the N-SH2, C-SH2 and PTP domains all exhibit distinct dynamics partitions, and the N-SH2/C-SH2 regions show a rigid rotation relative to PTP. According to a series of intermediate snapshots given by aANM, N-SH2 is first identified with pY223 specifically, inducing a D'E-loop to change from β-sheets to random coils, and then, C-SH2 serves as a fulcrum to drive N-SH2 to rotate 110° completely away from the original active sites of PTP. Finally, a possible allosteric signaling-transfer path for SHP2, namely R220-R138-T108-R32, is proposed based on NRI-MD sampling. This work provides a possible allosteric mechanism of SHP2, which is helpful for the following design of novel allosteric inhibitors and is expected to be used in clinical synergies with PD-1 monoclonal antibody.

Insight into free energy and dynamic cross-correlations of residue for binding affinity of antibody and receptor binding domain SARS-CoV-2

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.

New Methods of Evaluating Health Effects of Combined Exposures to Chemicals and Their Problems to Be Solved

There are several basic prerequisites for the risk assessment of combined exposures to pesticides and dioxins using human health effects as the endpoint. First, all the target chemical substances exert the same toxicity to humans through the same mechanisms. Second, there is a linear dose-response relationship between the toxicity and effects of individual chemicals. With these two prerequisites, the effects of combined exposures are estimated as the sum of the toxicities of individual chemicals. For example, the toxicities of dioxins are calculated using their toxic equivalent quantities (TEQ) by considering the assigned toxic equivalent factor (TEF) of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) set individually from their isomers and homologs. In conventional epidemiological studies, when the impact of each of multiple chemical substances is examined, methods such as multiple regression analysis or using a generalized linear model (GLM) have been used on the basis of the same prerequisites. However, in practice, some of the chemicals exhibit collinearity in their effects or do not show a linear dose-response relationship. In recent years, there have been several methods developed in the field of machine learning being applied to epidemiological research. Typical examples were methods using Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS), and the shrinkage method, i.e., using the least absolute shrinkage and selection operator (Lasso) and elastic network model (ENM). In the future, while taking into account the findings of experimental studies in biology, epidemiology, and other fields, it is expected that various methods will be applied and selected.

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.

Identification of the Intrinsic Motions and Related Key Residues Responsible for the Twofold Channel Opening of Poliovirus Capsid by Using an Elastic Network Model Combined with an Internal Coordinate.

Poliovirus (PV) is an infectious virus that causes poliomyelitis, which seriously threatens the health of children. The release of viral RNA is a key step of PV in host cell infection, and multiple lines of evidence have demonstrated that RNA release is initiated by the opening of the twofold channels of the PV capsid. However, the mechanism that controls the twofold channel opening is still not well understood. In addition, the channel opening motion of the recombinant PV capsid leads to the destruction of predominant neutralizing epitopes and thus hinders the capsid as a vaccine immunogen. Therefore, it is important to identify the intrinsic motions and the related key residues controlling the twofold channel opening for understanding the virus infection mechanism and developing capsid-based vaccines. In the present work, the width of the channel was selected as an internal coordinate directly related to the channel opening, and then the elastic network model (ENM) combined with the group theory were employed to extract the intrinsic motion modes that mostly contribute to the opening of the twofold channels. Our results show that the channel opening predominately induced by the breathing motion and the overall rotation of each protomer in the capsid. Then, an internal coordinate-based perturbation method was used to identify the key residues regulating the twofold channel opening of PV. The calculation results showed that the predicted key residues are mainly located at the twofold axes, the bottom of the canyons and the quasi threefold axes. Our study is helpful for better understanding the twofold channel opening mechanism and provides a potential target for preventing the opening of the channels, which is of great significance for PV vaccine design. The source code of this study is available at https://github.com/SJGLAB/CapsidKeyRes.git.

Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics.

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.

Landscape-Based View on the Stepping Movement of Myosin VI.

Myosin VI dimer walks toward the minus end of the actin filament with a large and variable step size of 25–36 nm. Two competing models have been put forward to explain this large step size. The Spudich model assumes that the myosin VI dimer associates at a distal tail near the cargo-binding domain, which makes two full-length single α-helix (SAH) domains serve as long legs. In contrast, the Houdusse–Sweeney model assumes that the association occurs in the middle (between residues 913 and 940) of the SAH domain and that the three-helix bundles unfold to ensure the large step size. Their consistency with the observation of stepping motion with a large and variable step size has not been examined in detail. To compare the two proposed models of myosin VI, we computationally characterized the free energy landscape experienced by the leading head during the stepping movement along the actin filament using the elastic network model of two heads and an implicit model of the SAH domains. Our results showed that the Spudich model is more consistent with the 25–36 nm step size than the Houdusse–Sweeney model. The unfolding of the three-helix bundles gives rise to the free energy bias toward a shorter distance between two heads. Besides, the stiffness of the SAH domain is a key factor for giving strong energetic bias toward the longer distance of stepping. Free energy analysis of the stepping motion complements the visual inspection of static structures and enables a deeper understanding of underlying mechanisms of molecular motors.

Local Normal Mode Analysis for Fast Loop Conformational Sampling.

We propose and validate a novel method to efficiently explore local protein loop conformations based on a new formalism for constrained normal mode analysis (NMA) in internal coordinates. The manifold of possible loop configurations imposed by the position and orientation of the fixed loop ends is reduced to an orthogonal set of motions (or modes) encoding concerted rotations of all the backbone dihedral angles. We validate the sampling power on a set of protein loops with highly variable experimental structures and demonstrate that our approach can efficiently explore the conformational space of closed loops. We also show an acceptable resemblance of the ensembles around equilibrium conformations generated by long molecular simulations and constrained NMA on a set of exposed and diverse loops. In comparison with other methods, the main advantage is the lack of restrictions on the number of dihedrals that can be altered simultaneously. Furthermore, the method is computationally efficient since it only requires the diagonalization of a tiny matrix, and the modes of motions are energetically contextualized by the elastic network model, which includes both the loop and the neighboring residues.

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method.

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.

Prediction of post-translational modification cross-talk and mutation within proteins via imbalanced learning

Post-translational modification (PTM) is crucial for various cell signaling pathways and biological processes. However, the interactions between PTMs (PTM cross-talk) or PTMs and other sites (PTM mutation) are still in there infancy, with many challenges remaining unexplored. Since PTMs play a fundamental role in cell biology, many mutations cause abnormalities in many PTM sites and affect many human diseases. In this research, we propose an algorithm to improve PTM sites and PTM sites (PTM cross-talk) prediction and to study the interaction between PTM sites and mutation sites (PTM mutation). In addition to the primary network features of three-dimensional protein structure and sequence features, the dynamic features based on ENMs (elastic network models) are also used to improve model performance. Through feature selection, we reserved 48 features and developed a predictor for predicting PTM cross-talk and mutation. According to the evaluation based on PTM cross-talk, the area under the curve of our best prediction model reaches 0.911, which exceeds the state-of-art-model PCTpred. Based on the evaluation of PTM mutation, our prediction model is highly reliable, with an AUC score of 0.935. Even with the removal of the distance, the performance of our model (which is the most excellent model of the nine models, Random Forest model) is relatively stable.

Hydroxylation Regiochemistry Is Robust to Active Site Mutations in Cytochrome P450cam (CYP101A1).

Cytochrome P450cam (CYP101A1) catalyzes the hydroxylation of d-camphor by molecular oxygen. The enzyme-catalyzed hydroxylation exhibits a high degree of regioselectivity and stereoselectivity, with a single major product, d-5-exo-hydroxycamphor, suggesting that the substrate is oriented to facilitate this specificity. In previous work, we used an elastic network model and perturbation response scanning to show that normal deformation modes of the enzyme structure are highly responsive not only to the presence of a substrate but also to the substrate orientation. This work examines the effects of mutations near the active site on substrate localization and orientation. The investigated mutations were designed to promote a change in substrate orientation and/or location that might give rise to different hydroxylation products, while maintaining the same carbon and oxygen atom balances as in the wild type (WT) enzyme. Computational experiments and parallel in vitro site-directed mutations of CYP101A1 were used to examine reaction products and enzyme activity. 1H-15N TROSY-HSQC correlation maps were used to compare the computational results with detectable perturbations in the enzyme structure and dynamics. We found that all of the mutant enzymes retained the same regio- and stereospecificity of hydroxylation as the WT enzyme, with varying degrees of efficiency, which suggests that large portions of the enzyme have been subjected to evolutionary pressure to arrive at the appropriate sequence-structure combination for efficient 5-exo hydroxylation of camphor.

Improving the energy efficiency of China: An analysis considering clean energy and fossil energy resources

Improving energy efficiency can simultaneously contribute to reducing energy shortages while achieving sustainable economic development. With the expansion of the renewable energy sector, improving energy efficiency requires further investigation. This study used the data envelopment analysis (DEA) game cross-efficiency model to calculate the clean energy efficiency (CEE) and fossil energy efficiency (FEE) of China at the provincial level; it identified the key factors affecting energy efficiency using an elastic network model, and clustered the 31 regions. The results show that: (1) the average level of CEE is 0.77, which is higher than the overall FEE; (2) in the geographical distribution, clean energy sources show a high trend in the middle and low trend in the east and west, while FEE shows a low trend in the west and high trend in the east; and (3) the main influencing factors influencing CEE are urbanization levels and secondary industry shares, while the main factors influencing FEE are electrification levels and urbanization level. Finally, suggestions on how to improve energy efficiency were proposed based on the clustering results.

Optimising Elastic Network Models for Protein Dynamics and Allostery: Spatial and Modal Cut-offs and Backbone Stiffness

The family of coarse-grained models for protein dynamics known as Elastic Network Models (ENMs) require careful choice of parameters to represent well experimental measurements or fully-atomistic simulations. The most basic ENM that represents each protein residue by a node at the position of its C-alpha atom, all connected by springs of equal stiffness, up to a cut-off in distance. Even at this level a choice is required of the optimum cut-off distance and the upper limit of elastic normal modes taken in any sum for physical properties, such as dynamic correlation or allosteric effects on binding. Additionally, backbone-enhanced ENM (BENM) may improve the model by allocating a higher stiffness to springs that connect along the protein backbone. This work reports on the effect of varying these three parameters (distance and mode cutoffs, backbone stiffness) on the dynamical structure of three proteins, Catabolite Activator Protein (CAP), Glutathione S-transferase (GST), and the SARS-CoV-2 Main Protease (M pro ). Our main results are: (1) balancing B-factor and dispersion-relation predictions, a near-universal optimal value of 8.5 Å is advisable for ENMs; (2) inhomogeneity in elasticity brings the first mode containing spatial structure not well-resolved by the ENM typically within the first 20; (3) the BENM only affects modes in the upper third of the distribution, and, additionally to the ENM, is only able to model the dispersion curve better in this vicinity; (4) BENM does not typically affect fluctuation-allostery, which also requires careful treatment of the effector binding to the host protein to capture.

Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach

• New advanced machine learning (ML) models applied for suspended sediment load prediction. • Results compared with traditional ML models as a benchmark. • Manually and feature selection methods applied to explore the best input scenario. • Advanced hybrid Elastic Network model outperform of all developed models. • The outcomes have practical implications in watershed management applications. The distribution and transportation of suspended sediment load (Q ssl ) in rivers have a significant effect on the design of hydraulic structures, river morphology, water quality, and aquatic ecosystems. As direct measurement of Q ssl can be costly and time-consuming, reliable estimates are vital for watershed management. In the present study, four standalone models including an Elastic Network (EN), Alternating Model Tree (AM Tree), Reduced Error Pruning Tree (REP Tree) and the Dual Perturb and Combine Tree (DPC Tree), including four hybridized models that combine a standalone model with the Rotation Forest (RF), were developed and evaluated for Q ssl prediction at Talar Watershed, in the northern Iran. Multiple scenarios comprised of antecedent flow discharge (Q w ), rainfall (R) and Q ssl , with all constructed manually and automatically using a Wrapper Feature Selection (WFS) to predict the Q ssl at Shirgah hydrometric station from January 1, 2004 to September 22, 2019. The optimal model fitted results were evaluated using multiple graphical and quantitative metrics with the results revealing that the flow discharge is perhaps the best predictor of Q ssl . Based on the Nash-Sutcliffe Efficiency (NSE) metric, the RF-EN (NSE = 0.85), EN (NSE = 0.83), AM Tree (NSE = 0.79), RF-AM Tree (NSE = 0.81) and the RF-REP Tree (NSE = 0.79) models seemed to perform very well, with the REP Tree (NSE = 0.65) and RF-DPC (NSE = 0.71) performed well, while the DPC Tree model (NSE = 0.35) were notably unsatisfactory. The hybridized models, however, captured extreme values more accurately compared with the standalone models. Finally, the model outputs were compared to the well-known optimized ANFIS models with a metaheuristic approach (imperialist competitive algorithm (ICA) and BAT algorithms), and all these results revealed that most the of newly developed models outperformed the ANFIS-ICA and ANFIS-BAT algorithms. The new modelling approaches developed and testing using advanced hybrid Rotation Forest based Elastic Networks in this study have important practical implications for suspended sediment load modeling and applications.

Human Diacylglycerol Kinase ε N-Terminal Segment Regulates the Phosphatidylinositol Cycle, Controlling the Rate but Not the Acyl Chain Composition of Its Lipid Intermediates.

Diacylglycerol kinase ε (DGKε), an enzyme of the phosphatidylinositol (PI) cycle, bears a highly conserved hydrophobic N-terminal segment, which was proposed to anchor the enzyme into the membrane. However, the importance of this segment to the DGKε function remains to be determined. To address this question, it is here reported an in silico and in vitro combined research strategy. Capitalizing on the AlphaFold 2.0 predicted structure of human DGKε, it is shown that its hydrophobic N-terminal segment anchors it into the membrane via a transmembrane α-helix. Coarse-grained based elastic network model studies showed that a conformational change in the hydrophobic N-terminal segment determines the proximity between the active site of DGKε and the membrane-water interface, likely regulating its kinase activity. In vitro studies with a purified DGKε construct lacking the hydrophobic N-terminal segment (His-SUMO*-Δ50-DGKε) corroborated the role of the N-terminus in regulating DGKε enzymatic properties. The comparison between the enzymatic properties of DGKε and His-SUMO*-Δ50-DGKε showed that the conserved N-terminal segment markedly inhibits the enzyme activity and its sensitivity to membrane intrinsic negative curvature, while also playing a role in the modulation of the enzyme by phosphatidylserine. On the other hand, this segment did not strongly affect its diacylglycerol acyl chain specificity, the modulation of the enzyme by membrane morphological changes, or the activation by phosphatidic acid-rich lipid domains. Hence, these results suggest that the conservation of the hydrophobic N-terminal segment of DGKε throughout evolution guaranteed not only membrane anchorage but also an efficient and elegant manner to regulate the rate of the PI cycle.

Edge weights in a protein elastic network reorganize collective motions and render long-range sensitivity responses.

The effects of inter-residue interactions on protein collective motions are analyzed by comparing two elastic network models (ENM)-structural contact ENM (SC-ENM) and molecular dynamics (MD)-ENM-with the edge weights computed from an all-atom MD trajectory by structure-mechanics statistical learning. A theoretical framework is devised to decompose the eigenvalues of ENM Hessian into contributions from individual springs and to compute the sensitivities of positional fluctuations and covariances to spring constant variation. Our linear perturbation approach quantifies the response mechanisms as softness modulation and orientation shift. All contacts of Cα positions in SC-ENM have an identical spring constant by fitting the profile of root-of-mean-squared-fluctuation calculated from an all-atom MD simulation, and the same trajectory data are also used to compute the specific spring constant of each contact as an MD-ENM edge weight. We illustrate that the soft-mode reorganization can be understood in terms of gaining weights along the structural contacts of low elastic strengths and loosing magnitude along those of high rigidities. With the diverse mechanical strengths encoded in protein dynamics, MD-ENM is found to have more pronounced long-range couplings and sensitivity responses with orientation shift identified as a key player in driving the specific residues to have high sensitivities. Furthermore, the responses of perturbing the springs of different residues are found to have asymmetry in the action-reaction relationship. In understanding the mutation effects on protein functional properties, such as long-range communications, our results point in the directions of collective motions as a major effector.

Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow.

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach.

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike–ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike–ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.