CABS-flex standalone: a simulation environment for fast modeling of protein flexibility

Efficient overexpression and purification of severe acute respiratory syndrome coronavirus 2 nucleocapsid proteins in Escherichia coli

The fundamental biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (Ncap), its use in diagnostic assays and its potential application as a vaccine component have received considerable attention since the outbreak of the Covid19 pandemic in late 2019. Here we report the scalable expression and purification of soluble, immunologically active, SARS-CoV-2 Ncap in Escherichia coli. Codon-optimised synthetic genes encoding the original Ncap sequence and four common variants with an N-terminal 6His affinity tag (sequence MHHHHHHG) were cloned into an inducible expression vector carrying a regulated bacteriophage T5 synthetic promoter controlled by lac operator binding sites. The constructs were used to express Ncap proteins and protocols developed which allow efficient production of purified Ncap with yields of over 200 mg per litre of culture media. These proteins were deployed in ELISA assays to allow comparison of their responses to human sera. Our results suggest that there was no detectable difference between the 6His-tagged and untagged original Ncap proteins but there may be a slight loss of sensitivity of sera to other Ncap isolates.

Structure prediction of linear and cyclic peptides using CABS-flex

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

Unveiling the Anthelminthic Potential of Merremia vitifolia Stem through in Vitro and in Silico Approach

This study aimed to assess the anthelmintic activity of methanol extracts from Merremia vitifolia stems using a combination approach encompassing experimental, in vitro, and in silico evaluations. Despite the well-recognized pharmacological properties of M. vitifolia, its potential as an anthelmintic agent remained unexplored. This plant's anthelmintic potential was assessed on adult earthworms (Pheretima posthuma), revealing a dose-dependent reduction in spontaneous motility leading to paralysis and eventual mortality. The most effective dose of M. vitifolia (200 mg/ml) for anthelmintic effects on Pheretima posthuma was identified. Complementary in silico investigations were also conducted, employing Autodock PyRx 0.8 for docking studies of reported M. vitifolia compounds. Notably, quercetin emerged as a promising candidate with superior binding energies against β-tubulin (-8.3 Kcal/mol). Moreover, this comprehensive research underlines the anthelmintic potential of Merremia vitifolia stem extract and highlights quercetin as a noteworthy compound for further investigation in the quest for novel anthelmintic agents.

The role of tandem repeats in bacterial functional amyloids

Repetitivity and modularity of proteins are two related notions incorporated into multiple evolutionary concepts. We discuss whether they may also be essential for functional amyloids. Amyloids are proteins that create very regular and usually highly insoluble fibrils, which are often associated with neurodegeneration. However, recent discoveries showed that amyloid structure of a protein could also be beneficial and desired, e.g., to promote cell adhesion. Functional amyloids are proteins which differ in their characteristics from pathological amyloids, so that the fibril formation could be more under control of an organism. We propose that repeats in the sequence could regulate the aggregation propensity of these proteins. The inclusion of multiple symmetric interactions, due to the presence of the repeats, could be supporting and strengthening the desirable structural properties of functional amyloids. Our results show that tandem repeats in bacterial functional amyloids have a distinct characteristic. The pattern of repeats supports the appropriate level of fibril formation and better controllability of fibril stability. The repeats tend to be more imperfect, which attenuates excessive aggregation propensity. Their desired structure and function are also reinforced by their amino acid profile. Although in the study we focused on bacterial functional amyloids, due to their importance in biofilm formation, we propose that similar mechanisms could be employed in other functional amyloids which are designed by evolution to aggregate in a desirable manner, but not necessarily in pathological amyloids.

Attenuation of fluoride-induced hepatorenal oxidative stress by ferulic acid in vivo: An approach with in-silico analysis and interaction informatics of ferulic acid

BACKGROUND

Chronic fluoride toxicity induces oxidative strain and lipid peroxidation and imparts deleterious effects on human metabolic organs.

AIM

The present study aimed to expose the defensive impact of ferulic acid against sodium fluoride (NaF) induced hepatorenal dysfunction at the biochemical and antioxidative systems.

METHODS

In-vivo. Rats were arbitrarily separated into five groups as control, sodium fluoride-treated (200 ppm kg ^-1), vitamin C -as a positive control, and FA co-administered groups with 10 mg kg ^-1 and 20 mg kg ^-1 body weight for 56 days. In the present investigation, we measured antioxidant enzymes, superoxide dismutase, catalase, and lactate dehydrogenase by electrozymographic and spectrophotometric methods. Biochemical assessment of TBARS, conjugated diene, and different serum biomarkers was done for liver and kidney functionality tests. In-silico. An in-silico study was conducted through a molecular docking experiment to evaluate the binding potentiality of FA by employing AutoDock Vina [version 1.5.6] to overcome the abnormality in the activities of catalase, and superoxide dismutase in NaF promoted toxicity of hepatorenal system. In-vitro. An in vitro biochemical experiment was conducted to support the in-silico study.

RESULTS

Superoxide dismutase and catalase were decreased in the intoxicated rat. Ferulic acid (FA) as an antioxidant remarkably defended the NaF-mediated deterioration of the antioxidative status in the hepatorenal system, lowering lipid peroxidation products, malondialdehyde, and conjugated diene. Serum biomarkers, ALT, AST, ALP, urea, and creatinine increased in the intoxicated group than in control. Ferulic acid significantly neutralized the ill effects of NaF on serum lipid profile. In-silico analysis hypothesized the strong interaction of FA with the active side of catalase and superoxide dismutase that prevented the binding of NaF at the active site of these mentioned enzymes and this was further validated by in-vitro assay.

CONCLUSION

However, FA modulates free radical generation and protected these metabolic organs against sodium fluoride-induced injury.

Molecular insights into MYO3A kinase domain variants explain variability in both severity and progression of DFNB30 hearing impairment

Hereditary hearing impairment (HI) is a common disease with the highest incidence among sensory defects. Several genes have been identified to affect stereocilia structure causing HI, including the unconventional myosin3A. Interestingly, we noticed that variants in MYO3A gene have been previously found to cause variable HI onset and severity. Using clinical exome sequencing, we identified a novel pathogenic variant p.(Lys50Arg) in the MYO3A kinase domain (MYO3A-KD). Previous in vitro studies supported its damaging effect as a 'kinase-dead' mutant. We further analyzed this variation through molecular dynamics which predicts that changes in flexibility of MYO3A structure would influence the protein-ATP binding properties. This Lys50Arg mutation segregated with congenital profound non-syndromic HI. To better investigate this variability, we collected previously identified MYO3A-KDs variants, p.(Tyr129Cys), p.(His142Gln) and p.(Pro189Thr), and built both wild type and mutant 3 D MYO3A-KD models to assess their impact on the protein structure and function. Our results suggest that KD mutations could either cause a congenital profound form of HI, when particularly affecting the kinase activity and preventing the auto-phosphorylation of the motor, or a late onset and progressive form, when partially or completely inactivating the MYO3A protein. In conclusion, we report a novel pathogenic variant affecting the ATP-binding site within the MYO3A-KD causing congenital profound HI. Through computational approaches we provide a deeper understanding on the correlation between the effects of MYO3A-KD mutations and the variable hearing phenotypes. To the best of our knowledge this is the first study to correlate mutations' genotypes with the variable phenotypes of DFNB30.Communicated by Ramaswamy H. Sarma.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.Communicated by Ramaswamy H. Sarma.

Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein

Haemophilus influenzae is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of Haemophilus influenzae. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein FtsN that specifically combat all the Haemophilus influenzae strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein-protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the Toll-like receptor 4 protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.

Molecular Docking and Molecular Dynamics Studies Reveal Secretory Proteins as Novel Targets of Temozolomide in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is a tumor of glial origin and is the most malignant, aggressive and prevalent type, with the highest mortality rate in adult brain cancer. Surgical resection of the tumor followed by Temozolomide (TMZ) therapy is currently available, but the development of resistance to TMZ is a common limiting factor in effective treatment. The present study investigated the potential interactions of TMZ with several secretory proteins involved in various molecular and cellular processes in GBM. Automated docking studies were performed using AutoDock 4.2, which showed an encouraging binding affinity of TMZ towards all targeted proteins, with the strongest interaction and binding affinity with GDF1 and SLIT1, followed by NPTX1, CREG2 and SERPINI, among the selected proteins. Molecular dynamics (MD) simulations of protein-ligand complexes were performed via CABS-flex V2.0 and the iMOD server to evaluate the root-mean-square fluctuations (RMSFs) and measure protein stability, respectively. The results showed that docked models were more flexible and stable with TMZ, suggesting that it may be able to target putative proteins implicated in gliomagenesis that may impact radioresistance. However, additional in vitro and in vivo investigations can ascertain the potential of the selected proteins to serve as novel targets for TMZ for GBM treatment.

Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.

Conformational Dynamics and Mechanisms of Client Protein Integration into the Hsp90 Chaperone Controlled by Allosteric Interactions of Regulatory Switches: Perturbation-Based Network Approach for Mutational Profiling of the Hsp90 Binding and Allostery

Understanding the allosteric mechanisms of the Hsp90 chaperone interactions with cochaperones and client protein clientele is fundamental to dissect activation and regulation of many proteins. In this work, atomistic simulations are combined with perturbation-based approaches and dynamic network modeling for a comparative mutational profiling of the Hsp90 binding and allosteric interaction networks in the three Hsp90 maturation complexes with FKBP51 and P23 cochaperones and the glucocorticoid receptor (GR) client. The conformational dynamics signatures of the Hsp90 complexes and dynamics fluctuation analysis revealed how the intrinsic plasticity of the Hsp90 dimer can be modulated by cochaperones and client proteins to stabilize the closed dimer state required at the maturation stage of the ATPase cycle. In silico deep mutational scanning of the protein residues characterized the hot spots of protein stability and binding affinity in the Hsp90 complexes, showing that binding hot spots may often coincide with the regulatory centers that modulate dynamic allostery in the Hsp90 dimer. We introduce a perturbation-based network approach for mutational scanning of allosteric residue potentials and characterize allosteric switch clusters that control mechanism of cochaperone-dependent client recognition and remodeling by the Hsp90 chaperone. The results revealed a conserved network of allosteric switches in the Hsp90 complexes that allow cochaperones and GR protein to become integrated into the Hsp90 system by anchoring to the conformational switch points in the functional Hsp90 regions. This study suggests that the Hsp90 binding and allostery may operate under a regulatory mechanism in which activation or repression of the Hsp90 activity can be pre-encoded in the allosterically regulated Hsp90 dimer motions. By binding directly to the conformational switch centers on the Hsp90, cochaperones and interacting proteins can efficiently modulate the allosteric interactions and long-range communications required for client remodeling and activation.

An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD)

Conventional drug development strategies typically use pocket in protein structures as drug-target sites. They overlook the plausible effects of protein evolvability and resistant mutations on protein structure which in turn may impair protein-drug interaction. In this study, we used an integrated evolution and structure guided strategy to develop potential evolutionary-escape resistant therapeutics using receptor binding domain (RBD) of SARS-CoV-2 spike-protein/S-protein as a model. Deploying an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence-space. Guided by molecular simulation and structure network analysis we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility. Using fuzzy C-means clustering we combined evolutionary and structural features of RBD and identified a critical region. Subsequently, we used computational drug screening using a library of 1615 small molecules and identified one lead molecule, which is expected to target the identified region, critical for evolvability and structural stability of RBD. This integrated evolution-structure guided strategy to develop evolutionary-escape resistant lead molecules have potential general applications beyond SARS-CoV-2.

Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach

Streptomyces is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently. Streptomyces derived compounds namely streptenols A, C, and F-I and streptazolin were subjected for ADMET property assessment. Our computational studies based on molecular docking effectively displayed the synergistic effect of streptomyces-derived compounds on the gynecological cancer target PIK3CA. These compounds were observed with the highest docking scores as well as promising intermolecular interaction stability throughout the molecular dynamic simulation. Molecular docking and molecular dynamic modeling techniques were utilized to investigate the binding mode stability of drugs using a pharmacophore scaffold, as well as physicochemical and pharmacokinetic aspects linked to alpelisib. With a root mean square fluctuation of the protein backbone of less than 0.7 nm, they demonstrated a steady binding mode in the target binding pocket. They have also prompted hydrogen bonding throughout the simulations, implying that the chemicals have firmly occupied the active site. A comprehensive study showed that streptenol D, streptenol E, streptenol C, streptenol G, streptenol F, and streptenol B can be considered as lead compounds for PIK3CA-based inhibitor design. To warrant the treatment efficacy against cancer, comprehensive computational research based on proposed chemicals must be assessed through in vitro studies.

Application of Phytochemicals To Combat Fungal Pathogens of Pulses: An Approach toward Inhibition of Fungal Propagation and Invasin Activity

The present study represented an innovative strategy for inactivating the secreted invasins (lignocellulolytic enzymes) of fungal phytopathogens using natural phytochemicals to combat fungal infection to the pulses. A fungal pathogen (Aspergillus niger SKP1) was isolated from the white lentil (Vigna mungo), which has the ability to synthesize different lignocellulolytic enzymes. An in silico docking study elucidated that quercetin, naringin, epigallocatechin gallate, curcumin, and cinnamic acid were the prime efficient phytochemicals to inhibit the activity of fungal invasive enzymes like endoglucanase, endo-1,4-β-xylanase, and glucoamylase. Considering this observation, extracted phytochemicals in different mixtures were applied to prevent growth of the isolated pathogen under in situ experimental studies. The minimal inhibitory concentrations (MIC₅₀) and minimal fungicidal concentration (MFC₅₀) values of the first mixture (naringenin, epicatechin gallate, and cinnamic acid) and second mixture (quercetin and curcumin) were 170 and 220 mg/L and 320 and 380 mg/L, respectively. The studied phytochemicals were established to be cytosafe when compared to the commercial fungicides. The seeds of the white lentil were subjected to 1 year of long-term storage with the two aforementioned combinatorial phytochemicals. Subsequent morphological and physiological analyses revealed the complete protection of the stored seeds from the fungal infection. The present work has enough potentiality for the storage of pulses using natural preservatives that circumvent the adverse effect of the chemical preservatives on the ecosystem.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Eugenol-Mediated Inhibition of Biofilm Formed by S. aureus: a Potent Organism for Pediatric Digestive System Diseases

Ocimum tenuiflorum (KT) is a common ethnobotanical plant of Southeast Asia. The ethnic communities of these regions use the various parts of the plants, especially the leaves, for the treatment of various ailments like cold and flu, chronic infections, and surface ailments. The leaves of these plants are consumed to act as immune boosters in the body. With this ethnical background, we performed the antimicrobial and antibiofilm potential of the methanolic extract of Ocimum tenuiflorum against biofilm formed by S. aureus biofilm. The biofilm formed by S. aureus is a potent cause for the development of gastrointestinal (GI)-associated chronic infection. The extract from the KT leaf was analyzed using UV spectroscopy and HPLC to confirm the presence of the active ingredients present within the extract. The HPLC and GC-MS studies revealed the presence of eugenol and linalool in a greater proportion having the maximum drug-like properties. It was observed that KT showed maximum inhibition of biofilms, proteins, and carbohydrates being present with the extracellular polymeric substance (EPS). Interestingly, the maximum inhibition to the quorum sensing (QS) and the genomic DNA, RNA content was reduced by eugenol and linalool in comparison to the plant extract. The studies were supported by in silico interaction between eugenol and linalool with the QS proteins of S. aureus. The studies were further confirmed with microscopic studies SEM and FCM. The IR studies also confirmed much reduction in biofilm when treated with eugenol, linalool, and KT with respect to the untreated sample.

Allosteric Determinants of the SARS-CoV-2 Spike Protein Binding with Nanobodies: Examining Mechanisms of Mutational Escape and Sensitivity of the Omicron Variant

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric determinants of the SARS-CoV-2 spike protein binding with nanobodies, we also examined nanobody-induced modulation of escaping mutations and the effect of the Omicron variant on nanobody binding. The mutational scanning analysis supported the notion that E484A mutation can have a significant detrimental effect on nanobody binding and result in Omicron-induced escape from nanobody neutralization. Our findings showed that SARS-CoV-2 spike protein might exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter response to binding without compromising activity. The network analysis supported these findings showing that VHH E/VHH V nanobody binding can induce long-range couplings between the cryptic binding epitope and ACE2-binding site through a broader ensemble of communication paths that is less dependent on specific mediating centers and therefore may be less sensitive to mutational perturbations of functional residues. The results suggest that binding affinity and long-range communications of the SARS-CoV-2 complexes with nanobodies can be determined by structurally stable regulatory centers and conformationally adaptable hotspots that are allosterically coupled and collectively control resilience to mutational escape.

Conformational Flexibility and Local Frustration in the Functional States of the SARS-CoV-2 Spike B.1.1.7 and B.1.351 Variants: Mutation-Induced Allosteric Modulation Mechanism of Functional Dynamics and Protein Stability

Structural and functional studies of the SARS-CoV-2 spike proteins have recently determined distinct functional states of the B.1.1.7 and B.1.351 spike variants, providing a molecular framework for understanding the mechanisms that link the effect of mutations with the enhanced virus infectivity and transmissibility. A detailed dynamic and energetic analysis of these variants was undertaken in the present work to quantify the effects of different mutations on functional conformational changes and stability of the SARS-CoV-2 spike protein. We employed the efficient and accurate coarse-grained (CG) simulations of multiple functional states of the D614G mutant, B.1.1.7 and B.1.351 spike variants to characterize conformational dynamics of the SARS-CoV-2 spike proteins and identify dynamic signatures of the functional regions that regulate transitions between the closed and open forms. By combining molecular simulations with full atomistic reconstruction of the trajectories and the ensemble-based mutational frustration analysis, we characterized how the intrinsic flexibility of specific spike regions can control functional conformational changes required for binding with the host-cell receptor. Using the residue-based mutational scanning of protein stability, we determined protein stability hotspots and identified potential energetic drivers favoring the receptor-accessible open spike states for the B.1.1.7 and B.1.351 spike variants. The results suggested that modulation of the energetic frustration at the inter-protomer interfaces can serve as a mechanism for allosteric couplings between mutational sites and the inter-protomer hinges of functional motions. The proposed mechanism of mutation-induced energetic frustration may result in greater adaptability and the emergence of multiple conformational states in the open form. This study suggested that SARS-CoV-2 B.1.1.7 and B.1.351 variants may leverage the intrinsic plasticity of functional regions in the spike protein for mutation-induced modulation of protein dynamics and allosteric regulation to control binding with the host cell receptor.

Protocols for Rational Design of Protein Solubility and Aggregation Properties Using Aggrescan3D Standalone

Protein aggregation is a major hurdle in the development and manufacturing of protein-based therapeutics. Development of aggregation-resistant and stable protein variants can be guided by rational redesign using computational tools. Here, we describe the architecture and functionalities of the Aggrescan3D (A3D) standalone package for the rational design of protein solubility and aggregation properties based on three-dimensional protein structures. We present the case studies of the three therapeutic proteins, including antibodies, exploring the practical use of the A3D standalone tool. The case studies demonstrate that protein solubility can be easily improved by the A3D prediction of non-destabilizing amino acid mutations at the protein surfaces.

A3D 2.0 Update for the Prediction and Optimization of Protein Solubility

Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this chapter is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.

An integrated in silico and in vivo approach to determine the effects of three commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate on growth rate and hematology in Cyprinus carpio L

The purpose of this work is to evaluate the homology modeling, in silico prediction, and characterization of somatotropin and erythropoietin from Cyprinus carpio as well as molecular docking and simulation experiments between the modeled proteins and surfactants sodium dodecyl sulfate (SDS), sodium laureth sulfate (SLES) and cetylpyridinium chloride (CPC). Using the best fit template structure, homology modeling of somatotropin and erythropoietin of Cyprinus carpio respectively was conducted. The model structures were improved further with 3Drefine, and the final 3D structures were verified with PROCHEK, ERRATA and ProQ. The physiochemical, as well as the stereochemical parameters of the modeled proteins, were evaluated using ExPASy's ProtParam. Molecular docking calculations, protein-ligand interactions, and protein flexibility analysis were carried out to determine the binding pattern of each ligand to the targeted proteins and their effect on the overall proteins' conformation. Our in silico analysis showed that hydrophobic interactions with the active site amino acid residues of the modeled proteins (somatotropin and erythropoietin) were more prevalent than hydrogen bonds and salt bridges that affect the flexibility and stability of the somatotropin and erythropoietin as revealed from our protein flexibility analysis. The in vivo analysis showed that sublethal concentrations of SDS, SLES, and CPC negatively affected the growth and hematological parameters of Cyprinus carpio. Hence, it may be inferred from the study that the alterations in the flexibility of somatotropin and erythropoietin of Cyprinus carpio upon addition of SDS, CPC and SLES might be attributable to the reduction in growth and hematological parameters.

Flavonoid compounds of buah merah (Pandanus conoideus Lamk) as a potent SARS-CoV-2 main protease inhibitor: in silico approach

Background

COVID19 is a global pandemic that threatens all nations. As there is no effective antiviral drug for COVID19, we examined the potency of natural ingredients against the SARS-CoV-2 main protease (PDB ID 6YNQ). Buah merah is a typical fruit from Papua, Indonesia, which is known to contain high levels of carotenoids and flavonoids. The contents have been proven to be effective as antiparasitic and anti-HIV. An in silico approach to 16 metabolites of buah merah (Pandanus conoideus Lamk) was carried out using AutoDock Vina. Furthermore, the study of the dynamics of ligand–protein interactions was carried out using CABS Flex 2.0 server to determine the test ligand and receptor complexes' stability. ADMET prediction was also carried out to study the pharmacokinetic profile of potential antiviral candidates.

Result

The docking results showed that 3 of the 16 buah merah metabolites were potent inhibitors against the SARS-CoV-2 main protease. The flavonoid compounds are quercetin 3′-glucoside, quercetin 3-O-glucose, and taxifolin 3-O-α-arabinopyranose with a binding affinity of − 9.7, − 9.3, and − 8.8, respectively, with stable ligand–protein complex. ADMET study shows that the three compounds are easily dissolved, easily absorbed orally and topically, have a high unbound fraction, low toxicity, and non-irritant.

Conclusion

We conclude that quercetin 3′-glucoside, quercetin 3-O-glucose, and taxifolin 3-O-α-arabinopyranose can be used and improved as potential anti-SARS-CoV-2 agents in further study.

Chitosan derivatives: A suggestive evaluation for novel inhibitor discovery against wild type and variants of SARS-CoV-2 virus

With increasing global cases and mortality rates due to COVID-19 infection, finding effective therapeutic interventions has become a top priority. Marine resources are not explored much and to be taken into consideration for exploring antiviral potential. Chitosan (carbohydrate polymer) is one such bioactive glycan found ubiquitously in marine organisms. The presence of reactive amine/hydroxyl groups, with low toxicity/allergenicity, compels us to explore it against SARS-CoV-2. We have screened a library of chitosan derivatives by site-specific docking at not only spike protein Receptor Binding Domain (RBD) of wild type SARS-CoV-2 but also on RBD of B.1.1.7 (UK) and P.1 (Brazil) SARS-CoV-2 variants. The obtained result was very interesting and ranks N-benzyl-O-acetyl-chitosan, Imino-chitosan, Sulfated-chitosan oligosaccharides derivatives as a potent antiviral candidate due to its high binding affinity of the ligands (-6.0 to -6.6 kcal/mol) with SARS-CoV-2 spike protein RBD and they critically interacting with amino acid residues Tyr 449, Asn 501, Tyr 501, Gln 493, Gln 498 and some other site-specific residues associated with higher transmissibility and severe infection. Further ADMET analysis was done and found significant for exploration of the future therapeutic potential of these three ligands. The obtained results are highly encouraging in support for consideration and exploration in further clinical studies of these chitosan derivatives as anti-SARS-CoV-2 therapeutics.

Antithrombin Resistance Rescues Clotting Defect of Homozygous Prothrombin-Y510N Dysprothrombinemia

A patient with hematuria in our clinic was diagnosed with urolithiasis. Analysis of the patient's plasma clotting time indicated that both activated partial thromboplastin time (52.6 seconds) and prothrombin time (19.4 seconds) are prolonged and prothrombin activity is reduced to 12.4% of normal, though the patient exhibited no abnormal bleeding phenotype and a prothrombin antigen level of 87.9%. Genetic analysis revealed the patient is homozygous for prothrombin Y510N mutation. We expressed and characterized the prothrombin-Y510N variant in appropriate coagulation assays and found that the specificity constant for activation of the mutant zymogen by factor Xa is impaired approximately fivefold. Thrombin generation assay using patient's plasma and prothrombin-deficient plasma supplemented with either wild-type or prothrombin-Y510N revealed that both peak height and time to peak for the prothrombin mutant are decreased; however, the endogenous thrombin generation potential is increased. Further analysis indicated that the thrombin mutant exhibits resistance to antithrombin and is inhibited by the serpin with approximately 12-fold slower rate constant. Protein C activation by thrombin-Y510N was also decreased by approximately 10-fold; however, thrombomodulin overcame the catalytic defect. The Na⁺-concentration-dependence of the amidolytic activities revealed that the dissociation constant for the interaction of Na⁺ with the mutant has been elevated approximately 20-fold. These results suggest that Y510 (Y184a in chymotrypsin numbering) belongs to network of residues involved in binding Na⁺. A normal protein C activation by thrombin-Y510N suggests that thrombomodulin modulates the conformation of the Na⁺-binding loop of thrombin. The clotting defect of thrombin-Y510N appears to be compensated by its markedly lower reactivity with antithrombin, explaining patient's normal hemostatic phenotype.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies: Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations

In this study, we used an integrative computational approach to examine molecular mechanisms and determine functional signatures underlying the role of functional residues in the SARS-CoV-2 spike protein that are targeted by novel mutational variants and antibody-escaping mutations. Atomistic simulations and functional dynamics analysis are combined with alanine scanning and mutational sensitivity profiling of the SARS-CoV-2 spike protein complexes with the ACE2 host receptor and the REGN-COV2 antibody cocktail(REG10987+REG10933). Using alanine scanning and mutational sensitivity analysis, we have shown that K417, E484, and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 spike protein complexes with ACE2, we demonstrate that E406, N439, K417, and N501 residues serve as effector centers of allosteric interactions and anchor major intermolecular communities that mediate long-range communication in the complexes. The results provide support to a model according to which mutational variants and antibody-escaping mutations constrained by the requirements for host receptor binding and preservation of stability may preferentially select structurally plastic and energetically adaptable allosteric centers to differentially modulate collective motions and allosteric interactions in the complexes with the ACE2 enzyme and REGN-COV2 antibody combination. This study suggests that the SARS-CoV-2 spike protein may function as a versatile and functionally adaptable allosteric machine that exploits the plasticity of allosteric regulatory centers to fine-tune response to antibody binding without compromising the activity of the spike protein.

In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors

COVID-19, a new strain of coronavirus family, was identified at the end of 2019 in China. The COVID-19 virus spread rapidly all over the world. Scientists strive to find virus-specific antivirals for the treatment of COVID-19. The present study reports a molecular docking study of the stilbenolignans and SARS-CoV-2 main protease (SARS-CoV-2 Mpro) inhibitors. The detailed interactions between the stilbenolignan analogues and SARS-CoV-2 Mpro inhibitors were determined as hydrophobic bonds, hydrogen bonds and electronic bonds, inhibition activity, ligand efficiency, bonding type and distance and etc. The binding energies of the stilbenolignan analogues were obtained from the molecular docking of SARS-CoV-2 Mpro. Lehmbachol D, Maackolin, Gnetucleistol, Gnetifolin F, Gnetofuran A and Aiphanol were found to be -7.7, -8.2, -7.3, -8.5, -8.0 and -7.3 kcal/mol, respectively. Osirus, Molinspiration and SwissADME chemoinformatic tools were used to examine ADMET properties, pharmacokinetic parameters and toxicological characteristics of the stilbenolignan analogues. All analogues obey the Lipinski's rule of five. Furthermore, stilbenolignan analogues were studied to predict their binding affinities against SARS-CoV-2 Mpro using molecular modeling and simulation techniques, and the binding free energy calculations of all complexes were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) method. With the data presented here it has been observed that these analogues may be a good candidate for SARS-CoV-2 Mpro in vivo studies, so more research can be done on stilbenolignan analogues.

Integrated Biophysical Modeling of the SARS-CoV-2 Spike Protein Binding and Allosteric Interactions with Antibodies

Structural and biochemical studies of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoproteins and complexes with highly potent antibodies have revealed multiple conformation-dependent epitopes highlighting conformational plasticity of spike proteins and capacity for eliciting specific binding and broad neutralization responses. In this study, we used coevolutionary analysis, molecular simulations, and perturbation-based hierarchical network modeling of the SARS-CoV-2 spike protein complexes with a panel of antibodies targeting distinct epitopes to explore molecular mechanisms underlying binding-induced modulation of dynamics and allosteric signaling in the spike proteins. Through coevolutionary analysis of the SARS-CoV-2 spike proteins, we identified highly coevolving hotspots and functional clusters that enable a functional cross-talk between distant allosteric regions in the SARS-CoV-2 spike complexes with antibodies. Coarse-grained and all-atom molecular dynamics simulations combined with mutational sensitivity mapping and perturbation-based profiling of the SARS-CoV-2 receptor-binding domain (RBD) complexes with CR3022 and CB6 antibodies enabled a detailed validation of the proposed approach and an extensive quantitative comparison with the experimental structural and deep mutagenesis scanning data. By combining in silico mutational scanning, perturbation-based modeling, and network analysis of the SARS-CoV-2 spike trimer complexes with H014, S309, S2M11, and S2E12 antibodies, we demonstrated that antibodies can incur specific and functionally relevant changes by modulating allosteric propensities and collective dynamics of the SARS-CoV-2 spike proteins. The results provide a novel insight into regulatory mechanisms of SARS-CoV-2 S proteins showing that antibody-escaping mutations can preferentially target structurally adaptable energy hotspots and allosteric effector centers that control functional movements and allosteric communication in the complexes.

Protocols for Fast Simulations of Protein Structure Flexibility Using CABS-Flex and SURPASS

Conformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods since experimental characterization can be difficult. Depending on protein system size, computational tools may require large computational resources or significant simplifications in the modeled systems to speed up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling, and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2-Spike RBD Interface

SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually in part due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor, several in silico methods and numerous experimental data reported recently to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both the UK and South African strains) should be favorable for the interaction with ACE2, while the K417N and E484K substitutions (South African strain) would seem neutral or even unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the K417N and E484K Spike replacements with regard to ACE2 binding. Our finding suggests that the UK strain should have higher affinity toward ACE2 and therefore likely increased transmissibility and possibly pathogenicity. If indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions located outside the direct Spike-ACE2 interface but not so much to the K417N and E484K replacements. Yet, it should be noted that amino acid changes at Spike position 484 can lead to viral escape from neutralizing antibodies. Further, these amino acid substitutions do not seem to induce major structural changes in this region of the Spike protein. This structure-function study allows us to rationalize some observations made for the UK strain but raises questions for the South African strain.

Exploring the active constituents of Oroxylum indicum in intervention of novel coronavirus (COVID-19) based on molecular docking method

The severe acute respiratory syndrome COVID-19 declared a global pandemic by WHO has become the present wellbeing worry to the whole world. There is an emergent need to search for possible medications. We report in this study a molecular docking study of eighteen Oroxylum indicum molecules with the main protease (Mpro) responsible for the replication of SARS-CoV-2 virus. The outcome of their molecular simulation and ADMET properties reveal four potential inhibitors of the enzyme (Baicalein-7-O-diglucoside, Chrysin-7-O-glucuronide, Oroxindin and Scutellarein) with preference of ligand Chrysin-7-O-glucuronide that has the second highest binding energy (- 8.6 kcal/mol) and fully obeys the Lipinski's rule of five.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13721-020-00279-y.

Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches

The rapidly growing body of structural and biochemical studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examined molecular mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic analysis of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained molecular simulations, protein stability analysis, and perturbation-based modeling of residue interaction networks, we examined how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the experimentally confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and determine topography of signal communication pathways operating through state-specific cascades of control switch points. This analysis provides a plausible strategy for allosteric probing of the conformational equilibrium and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Identification of Potential Flavonoid Inhibitors of the SARS-CoV-2 Main Protease 6YNQ: A Molecular Docking Study

Objective

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent for coronavirus disease 2019 (COVID-19), is responsible for the recent global pandemic. As there are no effective drugs or vaccines available for SARS-CoV-2, we investigated the potential of flavonoids against SARS-CoV-2 main protease 6YNQ.

Methods

In silico molecular simulation study against SARS-CoV-2 main protease 6YNQ.

Results

Among the 21 selected flavonoids, rutin demonstrated the highest binding energy (− 8.7 kcal/mol) and displayed perfect binding with the catalytic sites.

Conclusions

Our study demonstrates the inhibitory potential of flavonoids against SARS-CoV-2 main protease 6YNQ. These computational simulation studies support the hypothesis that flavonoids might be helpful for the treatment of COVID-19.

Molecular Simulations and Network Modeling Reveal an Allosteric Signaling in the SARS-CoV-2 Spike Proteins

The development of computational strategies for the quantitative characterization of the functional mechanisms of SARS-CoV-2 spike proteins is of paramount importance in efforts to accelerate the discovery of novel therapeutic agents and vaccines combating the COVID-19 pandemic. Structural and biophysical studies have recently characterized the conformational landscapes of the SARS-CoV-2 spike glycoproteins in the prefusion form, revealing a spectrum of stable and more dynamic states. By employing molecular simulations and network modeling approaches, this study systematically examined functional dynamics and identified the regulatory centers of allosteric interactions for distinct functional states of the wild-type and mutant variants of the SARS-CoV-2 prefusion spike trimer. This study presents evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. Perturbation-based modeling of the interaction networks revealed a key role of the cross-talk between the effector hotspots in the receptor binding domain and the fusion peptide proximal region of the SARS-CoV-2 spike protein. The results have shown that the allosteric hotspots of the interaction networks in the SARS-CoV-2 spike protein can control the dynamic switching between functional conformational states that are associated with virus entry to the host receptor. This study offers a useful and novel perspective on the underlying mechanisms of the SARS-CoV-2 spike protein through the lens of allosteric signaling as a regulatory apparatus of virus transmission that could open up opportunities for targeted allosteric drug discovery against SARS-CoV-2 proteins and contribute to the rapid response to the current and potential future pandemic scenarios.

Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of the SARS-CoV and SARS-CoV-2 spike protein receptor binding domains with the ACE2 host receptor. Different from other computational studies, we systematically examine the molecular and energetic determinants of the binding mechanisms between SARS-CoV-2 and ACE2 proteins through the lens of coevolution, conformational dynamics, and allosteric interactions that conspire to drive binding interactions and signal transmission. Conformational dynamics analysis revealed the important differences in mobility of the binding interfaces for the SARS-CoV-2 spike protein that are not confined to several binding hotspots, but instead are broadly distributed across many interface residues. Through coevolutionary network analysis and dynamics-based alanine scanning, we established linkages between the binding energy hotspots and potential regulators and carriers of signal communication in the virus-host receptor complexes. The results of this study detailed a binding mechanism in which the energetics of the SARS-CoV-2 association with ACE2 may be determined by cumulative changes of a number of residues distributed across the entire binding interface. The central findings of this study are consistent with structural and biochemical data and highlight drug discovery challenges of inhibiting large and adaptive protein-protein interfaces responsible for virus entry and infection transmission.

Impact of Early Pandemic Stage Mutations on Molecular Dynamics of SARS-CoV-2 Mpro

A new coronavirus (SARS-CoV-2) is a global threat to world health and economy. Its dimeric main protease (Mpro), which is required for the proteolytic cleavage of viral precursor proteins, is a good candidate for drug development owing to its conservation and the absence of a human homolog. Improving our understanding of Mpro behavior can accelerate the discovery of effective therapies to reduce mortality. All-atom molecular dynamics (MD) simulations (100 ns) of 50 mutant Mpro dimers obtained from filtered sequences from the GISAID database were analyzed using root-mean-square deviation, root-mean-square fluctuation, Rg, averaged betweenness centrality, and geometry calculations. The results showed that SARS-CoV-2 Mpro essentially behaves in a similar manner to its SAR-CoV homolog. However, we report the following new findings from the variants: (1) Residues GLY15, VAL157, and PRO184 have mutated more than once in SARS CoV-2; (2) the D48E variant has lead to a novel "TSEEMLN"" loop at the binding pocket; (3) inactive apo Mpro does not show signs of dissociation in 100 ns MD; (4) a non-canonical pose for PHE140 widens the substrate binding surface; (5) dual allosteric pockets coinciding with various stabilizing and functional components of the substrate binding pocket were found to display correlated compaction dynamics; (6) high betweenness centrality values for residues 17 and 128 in all Mpro samples suggest their high importance in dimer stability-one such consequence has been observed for the M17I mutation whereby one of the N-fingers was highly unstable. (7) Independent coarse-grained Monte Carlo simulations suggest a relationship between the rigidity/mutability and enzymatic function. Our entire approach combining database preparation, variant retrieval, homology modeling, dynamic residue network (DRN), relevant conformation retrieval from 1-D kernel density estimates from reaction coordinates to other existing approaches of structural analysis, and data visualization within the coronaviral Mpro is also novel and is applicable to other coronaviral proteins.

Impact of protein dynamics on secondary structure prediction

Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.

Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: Similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

The spread of COVID-19 caused by the SARS-CoV-2 outbreak has been growing since its first identification in December 2019. The publishing of the first SARS-CoV-2 genome made a valuable source of data to study the details about its phylogeny, evolution, and interaction with the host. Protein-protein binding assays have confirmed that Angiotensin-converting enzyme 2 (ACE2) is more likely to be the cell receptor through which the virus invades the host cell. In the present work, we provide an insight into the interaction of the viral spike Receptor Binding Domain (RBD) from different coronavirus isolates with host ACE2 protein. By calculating the binding energy score between RBD and ACE2, we highlighted the putative jump in the affinity from a progenitor form of SARS-CoV-2 to the current virus responsible for COVID-19 outbreak. Our result was consistent with previously reported phylogenetic analysis and corroborates the opinion that the interface segment of the spike protein RBD might be acquired by SARS-CoV-2 via a complex evolutionary process rather than a progressive accumulation of mutations. We also highlighted the relevance of Q493 and P499 amino acid residues of SARS-CoV-2 RBD for binding to human ACE2 and maintaining the stability of the interface. Moreover, we show from the structural analysis that it is unlikely for the interface residues to be the result of genetic engineering. Finally, we studied the impact of eight different variants located at the interaction surface of ACE2, on the complex formation with SARS-CoV-2 RBD. We found that none of them is likely to disrupt the interaction with the viral RBD of SARS-CoV-2.

CABS-dock standalone: a toolbox for flexible protein-peptide docking

Summary

CABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.

Availability and implementation

CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

Exploring Mechanisms of Communication Switching in the Hsp90-Cdc37 Regulatory Complexes with Client Kinases through Allosteric Coupling of Phosphorylation Sites: Perturbation-Based Modeling and Hierarchical Community Analysis of Residue Interaction Networks

Understanding molecular principles underlying chaperone-based modulation of kinase client activity is critically important to dissect functions and activation mechanisms of many oncogenic proteins. The recent experimental studies have suggested that phosphorylation sites in the Hsp90 and Cdc37 proteins can serve as conformational communication switches of chaperone regulation and kinase interactions. However, a mechanism of allosteric coupling between phosphorylation sites in the Hsp90 and Cdc37 during client binding is poorly understood, and the molecular signatures underpinning specific roles of phosphorylation sites in the Hsp90 regulation remain unknown. In this work, we employed a combination of evolutionary analysis, coarse-grained molecular simulations together with perturbation-based network modeling and scanning of the unbound and bound Hsp90 and Cdc37 structures to quantify allosteric effects of phosphorylation sites and identify unique signatures that are characteristic for communication switches of kinase-specific client binding. By using network-based metrics of the dynamic intercommunity bridgeness and community centrality, we characterize specific signatures of phosphorylation switches involved in allosteric regulation. Through perturbation-based analysis of the dynamic residue interaction networks, we show that mutations of kinase-specific phosphorylation switches can induce long-range effects and lead to a global rewiring of the allosteric network and signal transmission in the Hsp90-Cdc37-kinase complex. We determine a specific group of phosphorylation sites in the Hsp90 where mutations may have a strong detrimental effect on allosteric interaction network, providing insight into the mechanism of phosphorylation-induced communication switching. The results demonstrate that kinase-specific phosphorylation switches of communications in the Hsp90 may be partly predisposed for their regulatory role based on preexisting allosteric propensities.

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties

SUMMARY

Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools.

AVAILABILITY AND IMPLEMENTATION

A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

Multiepitope-Based Subunit Vaccine Design and Evaluation against Respiratory Syncytial Virus Using Reverse Vaccinology Approach

Respiratory syncytial virus (RSV) is primarily associated with respiratory disorders globally. Despite the availability of information, there is still no competitive vaccine available for RSV. Therefore, the present study has been designed to develop a multiepitope-based subunit vaccine (MEV) using a reverse vaccinology approach to curb RSV infections. Briefly, two highly antigenic and conserved proteins of RSV (glycoprotein and fusion protein) were selected and potential epitopes of different categories (B-cell and T-cell) were identified from them. Eminently antigenic and overlapping epitopes, which demonstrated strong associations with their respective human leukocyte antigen (HLA) alleles and depicted collective ~70% coverage of the world's populace, were shortlisted. Finally, 282 amino acids long MEV construct was established by connecting 13 major histocompatibility complex (MHC) class-I with two MHC class-II epitopes with appropriate adjuvant and linkers. Adjuvant and linkers were added to increase the immunogenic stimulation of the MEV. Developed MEV was stable, soluble, non-allergenic, non-toxic, flexible and highly antigenic. Furthermore, molecular docking and molecular dynamics (MD) simulations analyses were carried out. Results have shown a firm and robust binding affinity of MEV with human pathogenic toll-like receptor three (TLR3). The computationally mediated immune response of MEV demonstrated increased interferon-γ production, a significant abundance of immunoglobulin and activation of macrophages which are essential for immune-response against RSV. Moreover, MEV codons were optimized and in silico cloning was performed, to ensure its increased expression. These outcomes proposed that the MEV developed in this study will be a significant candidate against RSV to control and prevent RSV-related disorders if further investigated experimentally.

Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility

Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/

Structural Characterization of Open Reading Frame-Encoded Functional Genes from Tilapia Lake Virus (TiLV)

In recent years, large-scale mortalities are observed in tilapia due to infection with a novel orthomyxo-like virus named, tilapia lake virus (TiLV) which is marked to be a severe threat to universal tilapia industry. Currently, there are knowledge gaps relating to the antiviral peptide as well as there are no affordable vaccines or drugs available against TiLV yet. To understand the spreading of infection of TiLV in different organs of Oreochromis niloticus, RT-PCR analysis has been carried out. The gene segments of TiLV were retrieved from the NCBI database for computational biology analysis. The 14 functional genes were predicted from the 10 gene segments of TiLV. Phylogenetic analysis was employed to find out a better understanding for the evolution of tilapia lake virus genes. Out of 14 proteins, only six proteins show transmembrane helix region. Moreover, molecular modeling and molecular dynamics simulations of the predicted proteins revealed structural stability of the protein stabilized after 10-ns simulation. Overall, our study provided a basic bioinformatics on functional proteome of TiLV. Further, this study could be useful for development of novel peptide-based therapeutics to control TiLV infection.

Using protein engineering to understand and modulate aggregation

Protein aggregation occurs through a variety of mechanisms, initiated by the unfolded, non-native, or even the native state itself. Understanding the molecular mechanisms of protein aggregation is challenging, given the array of competing interactions that control solubility, stability, cooperativity and aggregation propensity. An array of methods have been developed to interrogate protein aggregation, spanning computational algorithms able to identify aggregation-prone regions, to deep mutational scanning to define the entire mutational landscape of a protein's sequence. Here, we review recent advances in this exciting and emerging field, focussing on protein engineering approaches that, together with improved computational methods, hold promise to predict and control protein aggregation linked to human disease, as well as facilitating the manufacture of protein-based therapeutics.

Flexible docking of peptides to proteins using CABS-dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.