Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP

The highly pathogenic Marburg virus (MARV) is a member of the Filoviridae family, a non-segmented negative-strand RNA virus. This article represents the computer-aided drug design (CADD) approach for identifying drug-like compounds that prevent the MARV virus disease by inhibiting nucleoprotein, which is responsible for their replication. This study used a wide range of in silico drug design techniques to identify potential drugs. Out of 368 natural compounds, 202 compounds passed ADMET, and molecular docking identified the top two molecules (CID: 1804018 and 5280520) with a high binding affinity of -6.77 and -6.672 kcal/mol, respectively. Both compounds showed interactions with the common amino acid residues SER_216, ARG_215, TYR_135, CYS_195, and ILE_108, which indicates that lead compounds and control ligands interact in the common active site/catalytic site of the protein. The negative binding free energies of CID: 1804018 and 5280520 were -66.01 and -31.29 kcal/mol, respectively. Two lead compounds were re-evaluated using MD modeling techniques, which confirmed CID: 1804018 as the most stable when complexed with the target protein. PC3 of the (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) was 8.74 %, whereas PC3 of the 2'-Hydroxydaidzein (CID: 5280520) was 11.25 %. In this study, (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) unveiled the significant stability of the proteins' binding site in ADMET, Molecular docking, MM-GBSA and MD simulation analysis studies, which also showed a high negative binding free energy value, confirming as the best drug candidate which is found in Angelica archangelica which may potentially inhibit the replication of MARV nucleoprotein.

Dynamics-based protein network features accurately discriminate neutral and rheostat positions

In some proteins, a unique class of nonconserved positions is characterized by their ability to generate diverse functional outcomes through single amino acid substitutions. Due to their ability to tune protein function, accurately identifying such "rheostat" positions is crucial for protein design, for understanding the impact of mutations observed in humans, and for predicting the evolution of pathogen drug resistance. However, identifying rheostat positions has been challenging, due-in part-to the absence of a clear structural relationship with binding sites. In this study, experimental data from our previous study of the Escherichia coli lactose repressor protein (LacI) was used to identify rheostat positions for which mutations tune in vivo EC50 for the allosteric ligand "IPTG." We next used the rheostat assignments to test the hypothesis that rheostat positions have unique dynamic features that will enable their identification. To that end, we integrated all-atom molecular dynamics simulations with perturbation residue response analysis. Results first revealed distinct dynamic behavior in IPTG-bound LacI compared with apo LacI, which was consistent with IPTG's role as an allosteric inducer. Next, we used a variety of dynamic features to build a classification model that discriminates experimentally characterized rheostat positions in LacI from positions with other types of substitution outcomes. In parallel, we built a second classifier model based on the 3D structural "static" network features of LacI. In comparative studies, the dynamic model better identified rheostat positions that were >8 Å from the binding site. In summary, our study provides insights into the dynamic characteristics of rheostat positions and suggests that models built on dynamic features may be useful for predicting the locations of rheostat positions in a wide range of proteins.

Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation

Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.

Reused Protein Segments Linked to Functional Dynamics

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

Investigating Pyruvate Dehydrogenase Kinase 3 Inhibitory Potential of Myricetin Using Integrated Computational and Spectroscopic Approaches

Protein kinases are involved in various diseases and currently represent potential targets for drug discovery. These kinases play major roles in regulating the cellular machinery and control growth, homeostasis, and cell signaling. Dysregulation of kinase expression is associated with various disorders such as cancer and neurodegeneration. Pyruvate dehydrogenase kinase 3 (PDK3) is implicated in cancer therapeutics as a potential drug target. In this current study, a molecular docking exhibited a strong binding affinity of myricetin to PDK3. Further, a 100 ns all-atom molecular dynamics (MD) simulation study provided insights into the structural dynamics and stability of the PDK3-myricetin complex, revealing the formation of a stable complex with minimal structural alterations upon ligand binding. Additionally, the actual affinity was ascertained by fluorescence binding studies, and myricetin showed appreciable binding affinity to PDK3. Further, the kinase inhibition assay suggested significant inhibition of PDK3 by myricetin, revealing an excellent inhibitory potential with an IC50 value of 3.3 μM. In conclusion, this study establishes myricetin as a potent PDK3 inhibitor that can be implicated in therapeutic targeting cancer and PDK3-associated diseases. In addition, this study underscores the efficacy of myricetin as a potential lead to drug discovery and provides valuable insights into the inhibition mechanism, enabling advancements in cancer therapeutics.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Investigating the structural basis of piperine targeting AKT1 against prostate cancer through in vitro and molecular dynamics simulations

AkT1, significantly impacts many tumours cell functions, like transcription, apoptosis, glucose metabolism, cell proliferation, and cell migration. For tumours to develop and spread, aberrant activation of AKT1 is essential. Therefore, a major focus of molecularly targeted PCa treatment is AKT1. The present study investigates the effect of piperine compared to SDF using in-vitro studies, viz colony formation assay, comet assay and AKT1 gene expression studies using human PCa cell line PC3. A cluster of approximately at least 50 cells constitutes a colony. The clonogenic assay showed that the number and size of colonies significantly decreased when treated with compounds (SDF and piperine) in comparison to the untreated cells which effectively proliferated to form more colonies. Piperine treatment showed significant inhibition of colony formation than SDF. Effective genotoxicity was observed in piperine-treated PC3 cells with an increased Tail length of 120 µm and it was moderately observed in SDF with a Tail length of 30 µm treated on PC3 cells. The control group did not show any considerable genotoxicity with a Tail length of 6 µm. Our data, both in vitro and in silico, suggested that piperine would be a good starting point for developing novel drugs for the treatment of PCa. The downstream functions of Akt1 may be inhibited by these effects, which could impede the proliferation of PCa cells. High stability of the piperine-AKT1 complex was found by the MD simulation. Higher hydrophilic residues like Lys268 and Ser205 at the active pocket may be the cause of the binding stability. Overall, the observed results confirmed the anti-PCa effect of piperine by causing effective DNA damage and proved to be genotoxic in nature against the human PCa. These effects may impede the downstream activities of Akt1 and result in PCa cell growth regression.Communicated by Ramaswamy H. Sarma.

Identification and mechanistic exploration of structural and conformational dynamics of NF-kB inhibitors: rationale insights from in silico and in vitro studies

Increased expression of target genes that code for proinflammatory chemical mediators results from a series of intracellular cascades triggered by activation of dysregulated NF-κB signaling pathway. Dysfunctional NF-kB signaling amplifies and perpetuates autoimmune responses in inflammatory diseases, including psoriasis. This study aimed to identify therapeutically relevant NF-kB inhibitors and elucidate the mechanistic aspects behind NF-kB inhibition. After virtual screening and molecular docking, five hit NF-kB inhibitors opted, and their therapeutic efficacy was examined using cell-based assays in TNF-α stimulated human keratinocyte cells. To investigate the conformational changes of target protein and inhibitor-protein interaction mechanisms, molecular dynamics (MD) simulations, binding free energy calculations together with principal component (PC) analysis, dynamics cross-correlation matrix analysis (DCCM), free energy landscape (FEL) analysis and quantum mechanical calculations were carried out. Among identified NF-kB inhibitors, myricetin and hesperidin significantly scavenged intracellular ROS and inhibited NF-kB activation. Analysis of the MD simulation trajectories of ligand-protein complexes revealed that myricetin and hesperidin formed energetically stabilized complexes with the target protein and were able to lock NF-kB in a closed conformation. Myricetin and hesperidin binding to the target protein significantly impacted conformational changes and internal dynamics of amino acid residues in protein domains. Tyr57, Glu60, Lys144 and Asp239 residues majorly contributed to locking the NF-kB in a closed conformation. The combinatorial approach employing in silico tools integrated with cell-based approaches substantiated the binding mechanism and NF-kB active site inhibition by the lead molecule myricetin, which can be explored as a viable antipsoriatic drug candidate associated with dysregulated NF-kB.Communicated by Ramaswamy H. Sarma.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

Insight into the Nucleotide Based Modulation of the Grp94 Molecular Chaperone Using Multiscale Dynamics

Grp94, an ER-localized molecular chaperone, is required for the folding and activation of many membrane and secretory proteins. Client activation by Grp94 is mediated by nucleotide and conformational changes. In this work, we aim to understand how microscopic changes from nucleotide hydrolysis can potentiate large-scale conformational changes of Grp94. We performed all-atom molecular dynamics simulations on the ATP-hydrolysis competent state of the Grp94 dimer in four different nucleotide bound states. We found that Grp94 was the most rigid when ATP was bound. ATP hydrolysis or nucleotide removal enhanced mobility of the N-terminal domain and ATP lid, resulting in suppression of interdomain communication. In an asymmetric conformation with one hydrolyzed nucleotide, we identified a more compact state, similar to experimental observations. We also identified a potential regulatory role of the flexible linker, as it formed electrostatic interactions with the Grp94 M-domain helix near the region where BiP is known to bind. These studies were complemented with normal-mode analysis of an elastic network model to investigate Grp94's large-scale conformational changes. SPM analysis identified residues that are important in signaling conformational change, many of which have known functional relevance in ATP coordination and catalysis, client binding, and BiP binding. Our findings suggest that ATP hydrolysis in Grp94 alters allosteric wiring and facilitates conformational changes.

Predicting allosteric pockets in protein biological assemblages

MOTIVATION

Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.

RESULTS

Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics.

AVAILABILITY AND IMPLEMENTATION

APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

Correlated Evolution of Low-Frequency Vibrations and Function in Enzymes

Previous studies of the flexibility of ancestral proteins suggest that proteins evolve their function by altering their native state ensemble. Here, we propose a more direct method to analyze such changes during protein evolution by comparing thermally activated vibrations at frequencies below 6 THz, which report on the dynamics of collective protein modes. We analyzed the backbone vibrational density of states of ancestral and extant β-lactamases and thioredoxins and observed marked changes in the vibrational spectrum in response to evolution. Coupled with previously observed changes in protein flexibility, the observed shifts of vibrational mode densities suggest that protein dynamics and dynamical allostery are critical factors for the evolution of enzymes with specialized catalytic and biophysical properties.

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.

Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein

Human stimulator of interferon genes (hSTING) is a signaling adaptor protein that triggers innate immune system by response to cytosolic DNA and second messenger cyclic dinucleotides (CDNs). Natural CDNs contain purine nucleobase with different phosphodiester linkage types (3'-3', 2'-2' or mixed 2'-3'-linkages) and exhibit different binding affinity towards hSTING, ranging from micromolar to nanomolar. High-affinity CDNs are considered as suitable candidates for treatment of chronic hepatitis B and cancer. We have used molecular dynamics simulations to investigate dynamical aspects of binding of natural CDNs (specifically, 2'-2'-cGAMP, 2'-3'-cGAMP, 3'-3'-cGAMP, 3'-3'-c-di-AMP, and 3'-3'-c-di-GMP) with hSTING^wt protein. Our results revealed that CDN/hSTING^wt interactions are controlled by the balance between fluctuations (conformational changes) in the CDN ligand and the protein dynamics. Binding of different CDNs induces different degrees of conformational/dynamics changes in hSTING^wt ligand binding cavity, especially in α₁-helices, the so-called lid region and α₂-tails. The ligand residence time in hSTING^wt protein pocket depends on different contribution of R232 and R238 residues interacting with oxygen atoms of phosphodiester groups in ligand, water distribution around interacting charged centers (in protein residues and ligand) and structural stability of closed conformation state of hSTING^wt protein. These findings may perhaps guide design of new compounds modulating hSTING activity.Communicated by Ramaswamy H. Sarma.

Mechanistic insight into impact of phosphorylation on the enzymatic steps of farnesyltransferase

Farnesyltransferase (FTase) is a heterodimeric enzyme, which catalyzes covalent attachment of the farnesyl group to target proteins, thus coordinating their trafficking in the cell. FTase has been demonstrated to be highly expressed in cancer and neurological diseases; hence considered as a hot target for therapeutic purposes. However, due to the nonspecific inhibition, there has been only one inhibitor that could be translated into the clinic. Importantly, it has been shown that phosphorylation of the α-subunit of FTase increases the activity of the enzyme in certain diseases. As such, understanding the impact of phosphorylation on dynamics of FTase provides a basis for targeting a specific state of the enzyme that emerges under pathological conditions. To this end, we performed 18 μs molecular dynamics (MD) simulations using complexes of (non)-phosphorylated FTase that are representatives of the farnesylation reaction. We demonstrated that phosphorylation modulated the catalytic site by rearranging interactions between farnesyl pyrophosphate (FPP)/peptide substrate, catalytic Zn2+ ion/coordinating residues and hot-spot residues at the interface of the subunits, all of which led to the stabilization of the substrate and facilitation of the release of the product, thus collectively expediting the reaction rate. Importantly, we also identified a likely allosteric pocket on the phosphorylated FTase, which might be used for specific targeting of the enzyme. To the best of our knowledge, this is the first study that systematically examines the impact of phosphorylation on the enzymatic reaction steps, hence opens up new avenues for drug discovery studies that focus on targeting phosphorylated FTase.

PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model

Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids, leading to increased or decreased stability of the encoded proteins. In this study, we describe a novel approach—Protein Stability Prediction with a Gaussian Network Model (PSP-GNM)—to measure the unfolding Gibbs free energy change (ΔΔG) and evaluate the effects of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa–Jernigan statistical potential. We used PSP-GNM to simulate partial unfolding of the wildtype and mutant protein structures, and then used the difference in the energies and entropies of the unfolded wildtype and mutant proteins to calculate ΔΔG. The extent of the agreement between the ΔΔG calculated by PSP-GNM and the experimental ΔΔG was evaluated on three benchmark datasets: 350 forward mutations (S350 dataset), 669 forward and reverse mutations (S669 dataset) and 611 forward and reverse mutations (S611 dataset). We observed a Pearson correlation coefficient as high as 0.61, which is comparable to many of the existing state-of-the-art methods. The agreement with experimental ΔΔG further increased when we considered only those measurements made close to 25 °C and neutral pH, suggesting dependence on experimental conditions. We also assessed for the antisymmetry (ΔΔG_reverse = −ΔΔG_forward) between the forward and reverse mutations on the Ssym+ dataset, which has 352 forward and reverse mutations. While most available methods do not display significant antisymmetry, PSP-GNM demonstrated near-perfect antisymmetry, with a Pearson correlation of −0.97. PSP-GNM is written in Python and can be downloaded as a stand-alone code.

Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier

SUMMARY

A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

Identification of Phytoconstituents as Potent Inhibitors of Casein Kinase-1 Alpha Using Virtual Screening and Molecular Dynamics Simulations

Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell cycle progression, apoptosis, autophagy, etc. It has been known to involve in the progression of many diseases, including cancer, neurodegeneration, obesity, and behavioral disorders. The elevated expression of CK1α in diseased conditions facilitates its selective targeting for therapeutic management. Here, we have performed virtual screening of phytoconstituents from the IMPPAT database seeking potential inhibitors of CK1α. First, a cluster of compounds was retrieved based on physicochemical parameters following Lipinski's rules and PAINS filter. Further, high-affinity hits against CK1α were obtained based on their binding affinity score. Furthermore, the ADMET, PAINS, and PASS evaluation was carried out to select more potent hits. Finally, following the interaction analysis, we elucidated three phytoconstituents, Semiglabrinol, Curcusone_A, and Liriodenine, posturing considerable affinity and specificity towards the CK1α binding pocket. The result was further evaluated by molecular dynamics (MD) simulations, dynamical cross-correlation matrix (DCCM), and principal components analysis (PCA), which revealed that binding of the selected compounds, especially Semiglabrinol, stabilizes CK1α and leads to fewer conformational fluctuations. The MM-PBSA analysis suggested an appreciable binding affinity of all three compounds toward CK1α.

Activation Mechanism of Corticotrophin Releasing Factor Receptor Type 1 Elucidated Using Molecular Dynamics Simulations

The corticotropin-releasing factor receptor type 1 (CRF1R), a member of class B G-protein-coupled receptors (GPCRs), is a good drug target for treating depression, anxiety, and other stress-related neurodisorders. However, there is no approved drug targeting the CRF1R to date, partly due to inadequate structural information and its elusive activation mechanism. Here, by use of the crystal structures of its transmembrane domain (TMD) and the N-terminal extracellular domain (ECD) as a template, a full-length homology model of CRF1R was built and its complexes with peptide agonist urocortin 1 or small molecule antagonist CP-376395 were subjected to all-atom molecular dynamics simulations. We observed well preserved helical contents in the TMD through simulations, while the transmembrane (TM) helices showed clear rearrangements. The TM rearrangement is especially pronounced for the TM6 in the agonist-bound CRF1R system. The observed conformational changes are likely due to breakage of interhelical/inter-regional hydrogen bonds in the TMD. Dynamical network analysis identifies communities with high connections to TM6. Simulations reveal three key residues, Y356^6.53, Q384^7.49, and L395^7.60, which corroborate experimental mutagenesis data, implying the important roles in the receptor activation. The observed large-scale conformational changes are related to CRF1R activation by agonist binding, providing guidance for ligand design.

Coarse-grained nucleic acid-protein model for hybrid nanotechnology

The emerging field of hybrid DNA-protein nanotechnology brings with it the potential for many novel materials which combine the addressability of DNA nanotechnology with the versatility of protein interactions. However, the design and computational study of these hybrid structures is difficult due to the system sizes involved. To aid in the design and in silico analysis process, we introduce here a coarse-grained DNA/RNA-protein model that extends the oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on an anisotropic network model representation. Fully equipped with analysis scripts and visualization, our model aims to facilitate hybrid nanomaterial design towards eventual experimental realization, as well as enabling study of biological complexes. We further demonstrate its usage by simulating DNA-protein nanocage, DNA wrapped around histones, and a nascent RNA in polymerase.

Structural Determination, Biological Function, and Molecular Modelling Studies of Sulfoaildenafil Adulterated in Herbal Dietary Supplement

Unapproved ingredients included in herbal medicines and dietary supplements have been detected as adulterated synthetic drugs used for erectile dysfunction. Extraction from a dietary supplement was performed to isolate the compounds by HPLC analysis. The structural characterization was confirmed using mass spectrometry (ESI-TOF/MS and LC-MS/MS), 1H NMR, and 13C NMR spectroscopy techniques. Results identified the thus-obtained compound to be sulfoaildenafil, a thioketone analogue of sildenafil. The biological activities of this active compound have been focused for the first time by the experimental point of view performance in vitro. The results revealed that sulfoaildenafil can affect the therapeutic level of nitric oxide through the upregulation of nitric oxide synthase and phosphodiesterase type 5 (PDE5) gene expressions. This bulk material, which displays structural similarity to sildenafil, was analyzed for the presence of a PDE5 inhibitor using a theoretical calculation. These unique features of the potential activity of PDE5 protein and its inhibitors, sildenafil and sulfoaildenafil, may play a key consideration for understanding the mode of actions and predicting the biological activities of PDE5 inhibitors.

Modal Analysis of the Lysozyme Protein Considering All-Atom and Coarse-Grained Finite Element Models

Proteins are the fundamental entities of several organic activities. They are essential for a broad range of tasks in a way that their shapes and folding processes are crucial to achieving proper biological functions. Low-frequency modes, generally associated with collective movements at terahertz (THz) and sub-terahertz frequencies, have been appointed as critical for the conformational processes of many proteins. Dynamic simulations, such as molecular dynamics, are vastly applied by biochemical researchers in this field. However, in the last years, proposals that define the protein as a simplified elastic macrostructure have shown appealing results when dealing with this type of problem. In this context, modal analysis based on different modelization techniques, i.e., considering both an all-atom (AA) and coarse-grained (CG) representation, is proposed to analyze the hen egg-white lysozyme. This work presents new considerations and conclusions compared to previous analyses. Experimental values for the B-factor, considering all the heavy atoms or only one representative point per amino acid, are used to evaluate the validity of the numerical solutions. In general terms, this comparison allows the assessment of the regional flexibility of the protein. Besides, the low computational requirements make this approach a quick method to extract the protein’s dynamic properties under scrutiny.

Adaptability and specificity: how do proteins balance opposing needs to achieve function?

Many proteins select from a small repertoire of 3-dimensional folds retained over evolutional timescales and recruited for different functions, with changes in local structure and sequence to enable specificity. Recent studies have revealed the evolutionary constraints on protein dynamics to achieve function. The significance of protein dynamics in simultaneously satisfying conformational flexibility/malleability and stability/precision requirements becomes clear upon dissecting the spectrum of equilibrium motions accessible to fold families. Accessibility to highly conserved global modes of motions shared by family members, to low-to-intermediate-frequency modes that distinguish subfamilies and confer specificity, and to conserved high-frequency modes ensuring chemical precision and core stability underlies functional specialization while exploiting highly versatile folds. These design principles are illustrated for the family of PDZ domains.

Structural compliance: A new metric for protein flexibility

Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic fluctuations have often been used to represent their dynamics and then compared to the experimental B-factors. However, proteins do not move in a vacuum and their motions are modulated by solvent that can impose forces on the structure. In this paper, we introduce a new structural concept, which has been called the structural compliance, for the evaluation of the global and local deformability of the protein structure in response to intramolecular and solvent forces. Based on the application of pairwise pulling forces to a protein elastic network, this structural quantity has been computed and sometimes is even found to yield an improved correlation with the experimental B-factors, meaning that it may serve as a better metric for protein flexibility. The inverse of structural compliance, namely the structural stiffness, has also been defined, which shows a clear anticorrelation with the experimental data. Although the present applications are made to proteins, this approach can also be applied to other biomolecular structures such as RNA. This present study considers only elastic network models, but the approach could be applied further to conventional atomic molecular dynamics. Compliance is found to have a slightly better agreement with the experimental B-factors, perhaps reflecting its bias toward the effects of local perturbations, in contrast to mean square fluctuations. The code for calculating protein compliance and stiffness is freely accessible at https://jerniganlab.github.io/Software/PACKMAN/Tutorials/compliance.

In Silico Analysis of a Highly Mutated Gene in Cancer Provides Insight into Abnormal mRNA Splicing: Splicing Factor 3B Subunit 1K700E Mutant

Cancer is the second leading cause of death worldwide. The etiology of the disease has remained elusive, but mutations causing aberrant RNA splicing have been considered one of the significant factors in various cancer types. The association of aberrant RNA splicing with drug/therapy resistance further increases the importance of these mutations. In this work, the impact of the splicing factor 3B subunit 1 (SF3B1) K700E mutation, a highly prevalent mutation in various cancer types, is investigated through molecular dynamics simulations. Based on our results, K700E mutation increases flexibility of the mutant SF3B1. Consequently, this mutation leads to i) disruption of interaction of pre-mRNA with SF3B1 and p14, thus preventing proper alignment of mRNA and causing usage of abnormal 3' splice site, and ii) disruption of communication in critical regions participating in interactions with other proteins in pre-mRNA splicing machinery. We anticipate that this study enhances our understanding of the mechanism of functional abnormalities associated with splicing machinery, thereby, increasing possibility for designing effective therapies to combat cancer at an earlier stage.

Binding Interactions of Ergotamine and Dihydroergotamine to 5-Hydroxytryptamine Receptor 1B (5-HT1b) Using Molecular Dynamics Simulations and Dynamic Network Analysis

Ergotamine (ERG) and dihydroergotamine (DHE), common migraine drugs, have small structural differences but lead to clinically important distinctions in their pharmacological profiles. For example, DHE is less potent than ERG by about 10-fold at the 5-hydroxytrptamine receptor 1B (5-HT_1B). Although the high-resolution crystal structures of the 5-HT_1B receptor with both ligands have been solved, the high similarity between these two complex structures does not sufficiently explain their activity differences and the activation mechanism of the receptor. Hence, an examination of the dynamic motion of both drugs with the receptor is required. In this study, we ran a total of 6.0 μs molecular dynamics simulations on each system. Our simulation data show the subtle variations between the two systems in terms of the ligand-receptor interactions and receptor secondary structures. More importantly, the ligand and protein root-mean-square fluctuations (RMSFs) for the two systems were distinct, with ERG having a trend of lower RMSF values, indicating it to be bound tighter to 5-HT_1B with less fluctuations. The molecular mechanism-general born surface area (MM-GBSA) binding energies illustrate this further, proving ERG has an overall stronger MM-GBSA binding energy. Analysis of several different microswitches has shown that the 5-HT_1B-ERG complex is in a more active conformation state than 5-HT_1B-DHE, which is further supported by the dynamic network model, with reference to mutagenesis data with the critical nodes and the first three low-energy modes from the normal mode analysis. We also identify Trp327^6.48 and Phe331^6.52 as key residues involved in the active state 5-HT_1B for both ligands. Using the detailed dynamic information from our analysis, we made predictions for possible modifications to DHE and ERG that yielded five derivatives that might have more favorable binding energies and reduced structural fluctuations.

The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution

Advances in sequencing techniques and statistical methods have made it possible not only to predict sequences of ancestral proteins but also to identify thousands of mutations in the human exome, some of which are disease associated. These developments have motivated numerous theories and raised many questions regarding the fundamental principles behind protein evolution, which have been traditionally investigated horizontally using the tip of the phylogenetic tree through comparative studies of extant proteins within a family. In this article, we review a vertical comparison of the modern and resurrected ancestral proteins. We focus mainly on the dynamical properties responsible for a protein's ability to adapt new functions in response to environmental changes. Using the Dynamic Flexibility Index and the Dynamic Coupling Index to quantify the relative flexibility and dynamic coupling at a site-specific, single-amino-acid level, we provide evidence that the migration of hinges, which are often functionally critical rigid sites, is a mechanism through which proteins can rapidly evolve. Additionally, we show that disease-associated mutations in proteins often result in flexibility changes even at positions distal from mutational sites, particularly in the modulation of active site dynamics.

Conformational Flexibility of the Protein-Protein Interfaces of the Ebola Virus VP40 Structural Matrix Filament

The Ebola virus (EBOV) is a virulent pathogen that causes severe hemorrhagic fever with a high fatality rate in humans. The EBOV transformer protein VP40 plays crucial roles in viral assembly and budding at the plasma membrane of infected cells. One of VP40's roles is to form the long, flexible, pleomorphic filamentous structural matrix for the virus. Each filament contains three unique interfaces: monomer NTD-NTD to form a dimer, dimer-to-dimer NTD-NTD oligomerization to form a hexamer, and end-to-end hexamer CTD-CTD to build the filament. However, the atomic-level details of conformational flexibility of the VP40 filament are still elusive. In this study, we have performed explicit-solvent, all-atom molecular dynamic simulations to explore the conformational flexibility of the three different interface structures of the filament. Using dynamic network analysis and other calculational methods, we find that the CTD-CTD hexamer interface with weak interdomain amino acid communities is the most flexible, and the NTD-NTD oligomer interface with strong interdomain communities is the least flexible. Our study suggests that the high flexibility of the CTD-CTD interface may be essential for the supple bending of the Ebola filovirus, and such flexibility may present a target for molecular interventions to disrupt the Ebola virus functioning.

In silico analysis of nonsynonymous single-nucleotide polymorphisms (nsSNPs) of the SMPX gene

Mutations in the SMPX gene can disrupt the regular activity of the SMPX protein, which is involved in the hearing process. Recent reports showing a link between nonsynonymous single-nucleotide polymorphisms (nsSNPs) in SMPX and hearing loss, thus classifying deleterious SNPs in SMPX will be an uphill task before designing a more extensive population study. In this study, damaging nsSNPs of SMPX from the dbSNP database were identified by using 13 bioinformatics tools. Initially, the impact of nsSNPs in the SMPX gene were evaluated through different in silico predictors; and the deleterious convergent changes were analyzed by energy-minimization-guided residual network analysis. In addition, the pathogenic effects of mutations in SMPX-mediated protein-protein interactions were also characterized by structural modeling and binding energy calculations. A total of four mutations (N19D, A29T, K54N, and S71L) were found to be highly deleterious by all the tools, which are located at highly conserved regions. Furthermore, all four mutants showed structural alterations, and the communities of amino acids for mutant proteins were readily changed, compared to the wild-type. Among them, A29T (rs772775896) was revealed as the most damaging nsSNP, which caused significant structural deviation of the SMPX protein, as a result reducing the binding affinity to other functional partners. These findings reflect the computational insights into the deleterious role of nsSNPs in SMPX, which might be helpful for subjecting wet-lab confirmatory analysis.

A mechanism for the activation of the mechanosensitive Piezo1 channel by the small molecule Yoda1

Mechanosensitive Piezo1 and Piezo2 channels transduce various forms of mechanical forces into cellular signals that play vital roles in many important biological processes in vertebrate organisms. Besides mechanical forces, Piezo1 is selectively activated by micromolar concentrations of the small molecule Yoda1 through an unknown mechanism. Here, using a combination of all-atom molecular dynamics simulations, calcium imaging and electrophysiology, we identify an allosteric Yoda1 binding pocket located in the putative mechanosensory domain, approximately 40 Å away from the central pore. Our simulations further indicate that the presence of the agonist correlates with increased tension-induced motions of the Yoda1-bound subunit. Our results suggest a model wherein Yoda1 acts as a molecular wedge, facilitating force-induced conformational changes, effectively lowering the channel's mechanical threshold for activation. The identification of an allosteric agonist binding site in Piezo1 channels will pave the way for the rational design of future Piezo modulators with clinical value.

Ligand-Binding-Site Structure Shapes Allosteric Signal Transduction and the Evolution of Allostery in Protein Complexes

The structure of ligand-binding sites has been shown to profoundly influence the evolution of function in homomeric protein complexes. Complexes with multichain binding sites (MBSs) have more conserved quaternary structure, more similar binding sites and ligands between homologs, and evolve new functions slower than homomers with single-chain binding sites (SBSs). Here, using in silico analyses of protein dynamics, we investigate whether ligand-binding-site structure shapes allosteric signal transduction pathways, and whether the structural similarity of binding sites influences the evolution of allostery. Our analyses show that: 1) allostery is more frequent among MBS complexes than in SBS complexes, particularly in homomers; 2) in MBS homomers, semirigid communities and critical residues frequently connect interfaces and thus they are characterized by signal transduction pathways that cross protein-protein interfaces, whereas SBS homomers usually not; 3) ligand binding alters community structure differently in MBS and SBS homomers; and 4) except MBS homomers, allosteric proteins are more likely to have homologs with similar binding site than nonallosteric proteins, suggesting that binding site similarity is an important factor driving the evolution of allostery.

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

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

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.