3Drefine: consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.

Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

Genomic characterization of Influenza A (H1N1)pdm09 and SARS-CoV-2 from Influenza Like Illness (ILI) and Severe Acute Respiratory Illness (SARI) cases reported between July-December, 2022

Influenza Like Illness (ILI) and Severe Acute Respiratory Infection (SARI) cases are more prone to Influenza and SARS-CoV-2 infection. Accordingly, we genetically characterized Influenza and SARS-CoV-2 in 633 ILI and SARI cases by rRT-PCR and WGS. ILI and SARI cases showed H1N1pdm09 prevalence of 20.9% and 23.2% respectively. 135 (21.3%) H1N1pdm09 and 23 (3.6%) H3N2 and 5 coinfection (0.78%) of H1N1pdm09 and SARS-CoV-2 were detected. Phylogenetic analysis revealed H1N1pdm09 resemblance to clade 6B.1A.5a.2 and their genetic relatedness to InfA/Perth/34/2020, InfA/Victoria/88/2020 and InfA/Victoria/2570/2019. Pan 24 HA and 26 NA nonsynonymous mutations and novel HA (G6D, Y7F, Y78H, P212L, G339R, T508K and S523T) and NA (S229A) mutations were observed. S74R, N129D, N156K, S162N, K163Q and S164T alter HA Cb and Sa antibody recognizing site. Similarly, M19T, V13T substitution and multiple mutations in transmembrane and NA head domain drive antigenic drift. SARS-CoV-2 strains genetically characterized to Omicron BA.2.75 lineage containing thirty nonsynonymous spike mutations exhibited enhanced virulence and transmission rates. Coinfection although detected very minimal, the mutational changes in H1N1pdm09 and SARS-CoV-2 virus infected individuals could alter antibody receptor binding sites, allowing the viruses to escape immune response resulting in better adaptability and transmission. Thus continuous genomic surveillance is required to tackle any future outbreak.

Ensemble-Based Mutational Profiling and Network Analysis of the SARS-CoV-2 Spike Omicron XBB Lineages for Interactions with the ACE2 Receptor and Antibodies: Cooperation of Binding Hotspots in Mediating Epistatic Couplings Underlies Binding Mechanism and Immune Escape

In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.

Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles and binding mechanisms of convergent evolution for the SARS-CoV-2 Spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron Spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamic simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron Spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and molecular dynamics simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and molecular dynamics simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.

AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.

Structure based docking and biological evaluation towards exploring potential anti-cancerous and apoptotic activity of 6-Gingerol against human prostate carcinoma cells

BACKGROUND

6-Gingerol (6-G) is the primary active phytocomponent of ginger and has been shown to regulate multiple targets against cancer and its treatment. Androgen receptors (ARs) remain critical in the progression of prostate cancer (PCa). This study focuses on investigating 6-G as a promising anti-cancerous agent that inhibits AR activity significantly.

METHODS

In this study, molecular docking simulation was done to investigate the binding affinity of 6-G and control drug Bicalutamide (BT) against oncogenic AR and tumor suppressor estrogen receptor β (ERβ). The crystal structure of AR and ERβ was retrieved from Protein Data Bank (PDB) and docked with 3D Pubchem structures of 6-G using iGEMDOCK and AutoDock. Further in vitro study was done to evaluate the antioxidant, anti-cancerous, apoptotic, and wound healing potential of 6-G.

RESULTS

The result displays that 6-G shows good binding affinity with AR and ERβ. Condensation of the nucleus, change in mitochondrial membrane potential (MMP) and the ability to induce reactive oxygen species (ROS) were done in human PCa PC-3 cells. Results from the MTT assay demonstrated that 6-G and control drug BT showed significant (p < 0.01) dose and time dependent inhibition of human PCa PC-3 cells. 6-G increased the ROS generation intracellularly and decreased the MMP, and cell migration in treated PCa PC-3 cells. 6-G treated cells showed fragmented, condensed chromatin and nuclear apoptotic bodies.

CONCLUSIONS

Thus, this study validates 6-G as a potential drug candidate against human PCa. However, further study of the anticancer potency of 6-G has to be done before its use for PCa treatment.

AlphaFold2-Enabled Atomistic Modeling of Epistatic Binding Mechanisms for the SARS-CoV-2 Spike Omicron XBB.1.5, EG.5 and FLip Variants: Convergent Evolution Hotspots Cooperate to Control Stability and Conformational Adaptability in Balancing ACE2 Binding and Antibody Resistance

In this study, we combined AI-based atomistic structural modeling and microsecond molecular simulations of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for XBB.1.5+L455F, XBB.1.5+F456L(EG.5) and XBB.1.5+L455F/F456L (FLip) lineages to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and physics-based rigorous computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of Q493 hotspot in synchronization of epistatic couplings between L455F and F456L mutations providing a quantitative insight into the mechanism underlying differences between XBB lineages. Mutational profiling is combined with network-based model of epistatic couplings showing that the Q493, L455 and F456 sites mediate stable communities at the binding interface with ACE2 and can serve as stable mediators of non-additive couplings. Structure-based mutational analysis of Spike protein binding with the class 1 antibodies quantified the critical role of F456L and F486P mutations in eliciting strong immune evasion response. The results of this analysis support a mechanism in which the emergence of EG.5 and FLip variants may have been dictated by leveraging strong epistatic effects between several convergent revolutionary hotspots that provide synergy between the improved ACE2 binding and broad neutralization resistance. This interpretation is consistent with the notion that functionally balanced substitutions which simultaneously optimize immune evasion and high ACE2 affinity may continue to emerge through lineages with beneficial pair or triplet combinations of RBD mutations involving mediators of epistatic couplings and sites in highly adaptable RBD regions.

Characterization of novel CD19-specific VHHs isolated from a camelid immune library by phage display

BACKGROUND

Monoclonal antibody (mAb)-based immunotherapies have achieved promising outcomes in the treatment of immunological and oncological indications. CD19 is considered one of the most qualified antigens in the treatment of B-cell neoplasms. VHHs (nanobodies) are known for their physicochemical advantages over conventional mAbs rendering them suitable therapeutics and diagnostic tools. Herein, we aimed to isolate CD19-specific VHHs from a novel immune library using phage display.

METHODS

An immune VHH gene library was constructed. Using phage display and after five biopanning rounds, two monoclonal CD19-specific VHHs were isolated. The selected VHHs were expressed, purified, and characterized in terms of their affinity, specificity, sensitivity, and ability to target CD19-positive cell lines. Moreover, in silico analyses were employed for further characterization.

RESULTS

A VHH library was developed, and because the outputs of the 4th biopanning round exhibited the most favorable characteristics, a panel of random VHHs was selected from them. Ultimately, two of the most favorable VHHs were selected and DNA sequenced (designated as GR37 and GR41). Precise experiments indicated that GR37 and GR41 exhibited considerable specificity, sensitivity, and affinity (1.15 × 107 M-1 and 2.08 × 107 M-1, respectively) to CD19. Flow cytometric analyses revealed that GR37 and GR41 could bind CD19 on the surface of cell lines expressing the antigen. Moreover, in silico experiments predicted that both VHHs target epitopes that are distinct from that targeted by the CD19-specific single-chain variable fragment (scFv) FMC63.

CONCLUSION

The selected VHHs can be used as potential targeting tools for the development of CD19-based immunotherapeutics.

Tubulin as a potential molecular target for resveratrol in Giardia lamblia trophozoites, in vitro and in silico approaches

Giardia lamblia is a globally distributed protozoan parasite that causes intestinal disease. Recently, there is an increase in refractory cases of giardiasis to chemotherapeutic agents, and drugs available cause side effects that may limit its use or cause therapeutic non-compliance. Therefore, search for alternative and less harmful drugs to treat giardiasis is an important task. In this sense, resveratrol (RSV) is a polyphenol with a wide range of pharmacological effects such as antimicrobial, anticarcinogenic and antioxidant. The aim of this study was to evaluate the effects of RSV on Giardia lamblia trophozoites in vitro and in silico, focusing on tubulin affectation, a major protein of the Giardia cytoskeleton which participates in relevant processes for cell survival. In vitro determinations showed that RSV inhibits parasite growth and adherence, causes morphological changes, and induces apoptosis-like cell death through tubulin alterations demonstrated by immunolocalization and Western blot assays. Bioinformatic analysis by molecular docking suggested that RSV binds to Giardia tubulin interface heterodimer, sharing binding residues to those reported with depolymerization inhibitors. These findings suggest that RSV affects microtubular dynamics and make it an interesting compound to study for its safety and antigiardiasic potential.

Accurate Characterization of Conformational Ensembles and Binding Mechanisms of the SARS-CoV-2 Omicron BA.2 and BA.2.86 Spike Protein with the Host Receptor and Distinct Classes of Antibodies Using AlphaFold2-Augmented Integrative Computational Modeling

The latest wave SARS-CoV-2 Omicron variants displayed a growth advantage and the increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with all-atom MD simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that AlphaFold2-predicted conformational ensemble of the BA.2.86 spike protein complex can accurately capture the main dynamics signatures obtained from microscond molecular dynamics simulations. The ensemble-based dynamic mutational scanning of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 dissected the role of the BA.2 and BA.2.86 backgrounds in modulating binding free energy changes revealing a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 mutational sites R403K, F486P and R493Q. To examine immune evasion properties of BA.2.86 in atomistic detail, we performed large scale structure-based mutational profiling of the S protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against BA.2.86 variant. The results quantified specific function of the BA.2.86 mutations to ensure broad resistance against different classes of RBD antibodies. This study revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have primarily evolved to improve immune escape while modulating binding affinity with ACE2 through cooperative effect of R403K, F486P and R493Q mutations. The study supports a hypothesis that the impact of the increased ACE2 binding affinity on viral fitness is more universal and is mediated through cross-talk between convergent mutational hotspots, while the effect of immune evasion could be more variant-dependent.

Proteomic identification of metabolic changes in Paracoccidioides brasiliensis induced by a nitroheteroarylchalcone

Aim: To access the metabolic changes caused by a chalcone derivative (LabMol-75) through a proteomic approach. Methods: Proteomic analysis was performed after 9 h of Paracoccidioides brasiliensis yeast (Pb18) cell incubation with the LabMol-75 at MIC. The proteomic findings were validated through in vitro and in silico assays. Results: Exposure to the compound led to the downregulation of proteins associated with glycolysis and gluconeogenesis, β-oxidation, the citrate cycle and the electron transport chain. Conclusion: LabMol-75 caused an energetic imbalance in the fungus metabolism and deep oxidative stress. Additionally, the in silico molecular docking approach pointed to this molecule as a putative competitive inhibitor of DHPS.

In silico identification and analysis of potential inhibitors for acid phosphatase, HppA from Helicobacter pylori

Helicobacter pylori is the most common cause of gastric ulcers and is associated with gastric cancer. The enzyme HppA of class C nonspecific acid phosphohydrolases (NSAPs) of H. pylori plays a crucial role in the electron transport chain. Herein, we report an in silico homology model of HppA consisting of a monomeric α + β model. A high throughput structure-based virtual screening approach yielded potential inhibitors against HppA with higher binding energies. Further analyses of molecular interaction maps and protein-ligand fingerprints, followed by molecular mechanics-generalized Born surface area (MM-GBSA) end point binding energy calculations of docked complexes, resulted in the detection of top binders/ligands. Our investigations identified potential substrate-competitive small molecule inhibitors of HppA, with admissible pharmacokinetic properties. These molecules may provide a starting point for developing novel therapeutic agents against H. pylori.

In Silico Physicochemical Characterization of Fusion Proteins from Emerging Amazonian Arboviruses

Mayaro (MAYV), Saint Louis encephalitis (SLEV), and Oropouche (OROV) viruses are neglected members of the three main families of arboviruses with medical relevance that circulate in the Amazon region as etiological agents of outbreaks of febrile illnesses in humans. As enveloped viruses, MAYV, SLEV, and OROV largely depend on their class II fusion proteins (E1, E, and Gc, respectively) for entry into the host cell. Since many aspects of the structural biology of such proteins remain unclear, the present study aimed at physicochemically characterizing them by an in silico approach. The complete amino acid sequences of MAYV E1, SLEV E, and OROV Gc proteins derived by conceptual translation from annotated coding regions in the reference sequence genome of the respective viruses were obtained from the NCBI Protein database in the FASTA format and then submitted to the ClustalO, Protcalc, Pepstats, Predator, Proscan, PCprof, Phyre2, and 3Drefine web servers for the determination of sequence identities, the estimation of residual properties, the prediction of secondary structures, the identification of potential post-translational modifications, the recognition of antigenic propensities, and the modeling/refinement of three-dimensional structures. Sequence identities were 20.44%, 18.82%, and 13.70% between MAYV/SLEV, SLEV/OROV, and MAYV/OROV fusion proteins, respectively. As for the residual properties, MAYV E1 and SLEV E proteins showed a predominance of the non-polar profile (56% and 55% of the residues, respectively), whereas the OROV Gc protein showed a predominance of the polar profile (52% of the residues). Regarding predicted secondary structures, MAYV E1 and SLEV E proteins showed fewer alpha-helices (16.51% and 15.17%, respectively) than beta-sheets (21.79% and 25.15%, respectively), while the opposite was observed in the OROV Gc protein (20.39% alpha-helices and 12.14% beta-sheets). Regarding post-translational modifications, MAYV E1, SLEV E, and OROV Gc proteins showed greater relative potential for protein kinase C phosphorylation, N-myristoylation, and casein kinase II phosphorylation, respectively. Finally, antigenic propensities were higher in the N-terminus half than in the C-terminus half of these three proteins, whose three-dimensional structures revealed three distinctive domains. In conclusion, MAYV E1 and SLEV E proteins were found to share more physicochemical characteristics with each other than the OROV Gc protein, although they are all grouped under the same class of viral fusion proteins.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variants Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and the increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, extensive microsecond MD simulations and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant which is contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of conformational states. The results suggested that variant-specific changes of conformational mobility in the functional interfacial loops of the spike receptor binding domain can be fine-tuned through cross-talk between convergent mutations thereby providing an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulating conformational plasticity at the binding interface and regulating allosteric responses. This study also characterized the dynamics-induced evolution of allosteric pockets in the Omicron complexes that revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions. Through integrative computational approaches, this investigation provides a systematic analysis and comparison of the effects of Omicron subvariants on conformational dynamics and allosteric signaling in the complexes with the ACE2 receptor.

For Table of Contents Use Only

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer

MOTIVATION

The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph.

RESULTS

The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score-the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement.

AVAILABILITY AND IMPLEMENTATION

The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

Probing Conformational Landscapes of Binding and Allostery in the SARS-CoV-2 Omicron Variant Complexes Using Microsecond Atomistic Simulations and Perturbation-Based Profiling Approaches: Hidden Role of Omicron Mutations as Modulators of Allosteric Signaling and Epistatic Relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which is contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using ensemble-based mutational scanning of binding interactions, we identified binding affinity and structural stability hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron variants on allosteric communications. The results of this analysis revealed specific roles of Omicron mutations as "plastic and evolutionary adaptable" modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes, which is performed in the background of the original strain, we identified that the key Omicron binding affinity hotspots N501Y and Q498R could mediate allosteric interactions and epistatic couplings. Our results suggested that the synergistic role of these hotspots in controlling stability, binding and allostery can enable for compensatory balance of fitness tradeoffs with conformationally and evolutionary adaptable immune-escape Omicron mutations. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor. The findings support a mechanism in which Omicron mutations can evolve to balance thermodynamic stability and conformational adaptability in order to ensure proper tradeoff between stability, binding and immune escape.

In-silico drug design for the novel Karachi-NF001 strain of brain-eating amoeba: Naegleria fowleri

Naegleria fowleri (N. fowleri) is a free-living thermophilic amoeba of fresh water and soil. The amoeba primarily feeds on bacteria but can be transmitted to humans upon contact with freshwater sources. Furthermore, this brain-eating amoeba enters the human body through the nose and travels to the brain to cause primary amebic meningoencephalitis (PAM). N. fowleri has been reported globally since its discovery in 1961. Recently a new strain of N. fowleri named Karachi-NF001 was found in a patient who had traveled from Riyadh, Saudi Arabia to Karachi in 2019. There were 15 unique genes identified in the genome of the Karachi-NF001 strain compared to all the previously reported strains of N. fowleri worldwide. Six of these genes encode well-known proteins. In this study, we performed in-silico analysis on 5 of these 6 proteins, namely, Rab family small GTPase, NADH dehydrogenase subunit 11, two Glutamine-rich protein 2 proteins (locus tags: 12086 and 12110), and Tigger transposable element-derived protein 1. We conducted homology modeling of these 5 proteins followed by their active site identification. These proteins were subjected to molecular docking against 105 anti-bacterial ligand compounds as potential drugs. Subsequently, the 10 best-docked compounds were determined for each protein and ranked according to the number of interactions and their binding energies. The highest binding energy was recorded for the two Glutamine-rich protein 2 proteins with different locus tags, and results have shown that the protein-inhibitor complex was stable throughout the simulation run. Moreover, future in-vitro studies could validate the findings of our in-silico analysis and identify potential therapeutic drugs against N. fowleri infections.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy

The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.

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.

Characterization and Structural Prediction of Proteins in SARS-CoV-2 Bangladeshi Variant Through Bioinformatics

The renowned respiratory disease induced by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has become a global epidemic in just less than a year by the first half of 2020. The subsequent efficient human-to-human transmission of this virus eventually affected millions of people worldwide. The most devastating thing is that the infection rate is continuously uprising and resulting in significant mortality especially among the older age population and those with health co-morbidities. This enveloped, positive-sense RNA virus is chiefly responsible for the infection of the upper respiratory system. The virulence of the SARS-CoV-2 is mostly regulated by its proteins such as entry to the host cell through fusion mechanism, fusion of infected cells with neighboring uninfected cells to spread virus, inhibition of host gene expression, cellular differentiation, apoptosis, mitochondrial biogenesis, etc. But very little is known about the protein structures and functionalities. Therefore, the main purpose of this study is to learn more about these proteins through bioinformatics approaches. In this study, ORF10, ORF7b, ORF7a, ORF6, membrane glycoprotein, and envelope protein have been selected from a Bangladeshi Corona-virus strain G039392 and a number of bioinformatics tools (MEGA-X-V10.1.7, PONDR, ProtScale, ProtParam, SCRIBER, NetSurfP v2.0, IntFOLD, UCSF Chimera, and PyMol) and strategies were implemented for multiple sequence alignment and phylogeny analysis with 9 different variants, predicting hydropathicity, amino acid compositions, protein-binding propensity, protein disorders, and 2D and 3D protein modeling. Selected proteins were characterized as highly flexible, structurally and electrostatically extremely stable, ordered, biologically active, hydrophobic, and closely related to proteins of different variants. This detailed information regarding the characterization and structure of proteins of SARS-CoV-2 Bangladeshi variant was performed for the first time ever to unveil the deep mechanism behind the virulence features. And this robust appraisal also paves the future way for molecular docking, vaccine development targeting these characterized proteins.

A short framework-III (mini-M-2) conotoxin from the venom of a vermivorous species, Conus archon, inhibits human neuronal nicotinic acetylcholine receptors

The venoms of Conus snails contain neuroactive peptides named conotoxins (CTXs). Some CTXs are nicotinic acetylcholine receptor (nAChRs) antagonists. nAChRs modulate the release of neurotransmitters and are implicated in several pathophysiologies. One venom peptide from Conus archon, a vermivorous species from the Mexican Pacific, was purified by RP-HPLC and its activity on human α7, α3β2, and α7β2 nAChRs was assessed by the two-electrode voltage clamp technique. At 36.3 µM the purified peptide (F27-1, renamed tentatively ArchIIIA) slowly reversibly inhibited the ACh-induced response of the hα7 subtype by 44.52 ± 5.83%, while it had low or no significant effect on the response of the hα3β2 and hα7β2 subtypes; the EC₅₀ of the inhibiting effect was 45.7 µM on the hα7 subtype. This peptide has 15 amino acid residues and a monoisotopic mass of 1654.6 Da (CCSALCSRYHCLPCC), with three disulfide bridges and a free C-terminus. This sequence with a CC-C-C-CC arrangement (framework III) belongs to the M superfamily of conotoxins, corresponding to the mini-M´s (M-1-M-3) conotoxins; due to its size and inter-Cys spacings it is an M-2 conotoxin. This toxin is a novel mini-M conotoxin affecting ligand-gated ion channels, like the maxi-M CTX ψ-conotoxins and α-MIIIJ conotoxin (nAChRs blockers). This peptide seems to be homologous to the reg3b conotoxin (from Conus regius) with an identity of 93.3%, differing only in the third residue in the sequence, serine for threonine, both uncharged polar residues. We obtained, in silico, a probable 3D structure, which is consistent with its effect on neuronal subtypes.

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.

Molecular Modeling of Subtype-Specific Tat Protein Signatures to Predict Tat-TAR Interactions That May Be Involved in HIV-Associated Neurocognitive Disorders

HIV-1 is responsible for a spectrum of neurocognitive deficits defined as HIV-associated neurocognitive disorders (HAND). The HIV transactivator of transcription (Tat) protein plays a key role in the neuropathophysiology of HAND. The Tat protein functions by transactivation of viral genes through its interaction with the transactivation response (TAR) RNA element. Subtype-specific Tat protein signatures including C31S, R57S and Q63E present in Tat subtype C has previously been linked to a lowered neuropathophysiology compared to Tat subtype B. In this study, we attempted to understand the molecular mechanism by which Tat subtype-specific variation, particularly, C31S, R57S, and Q63E influence the Tat-TAR interaction. We performed molecular modeling to generate accurate three-dimensional protein structures of the HIV-1 Tat subtypes C and B using the Swiss model webserver. Thereafter, we performed a molecular docking of the TAR RNA element to each of the Tat subtypes B and C protein structures using the HDOCK webserver. Our findings indicate that Tat subtype B had a higher affinity for the TAR RNA element compared to Tat subtype C based on a higher docking score of −187.37, a higher binding free energy value of −9834.63 ± 216.17 kJ/mol, and a higher number of protein–nucleotide interactions of 26. Furthermore, Tat subtype B displayed more flexible regions when bound to the TAR element and this flexibility could account for the stronger affinity of Tat subtype B to TAR. From the Tat signatures linked to neuropathogenesis, only R57/R57S are involved in Tat-TAR interaction. Due to the lack of electrostatic interactions observed between Tat subtype C and TAR, weaker affinity is observed, and this may contribute to a lower level of neuropathophysiology observed in subtype C infection.

Commonly used surfactants sodium dodecyl sulphate, cetylpyridinium chloride and sodium laureth sulphate and their effects on antioxidant defence system and oxidative stress indices in Cyprinus carpio L.: an integrated in silico and in vivo approach

The present study evaluated the homology modelling, in silico prediction and characterization of Cyprinus carpio cytochrome P450, as well as molecular docking experiments between the modelled protein and the surfactants sodium dodecyl sulphate (SDS), sodium laureth sulphate (SLES) and cetylpyridinium chloride (CPC). Homology modelling of cytochrome P450 was performed using the best fit template structure. The structure was optimized with 3D refine, and the ultimate 3D structure was checked with PROCHEK and ERRATA. ExPASy's ProtParam was likewise used to analyse the modelled protein's physiochemical and stereochemical attributes. To establish the binding pattern of each ligand to the targeted protein and its effect on the overall protein conformation, molecular docking calculations and protein-ligand interactions were performed. Our in silico analysis revealed that hydrophobic interactions with the active site amino acid residues of cytochrome p450 were more prevalent than hydrogen bonds and salt bridges. The in vivo analysis exhibited that exposure of fish to sublethal concentrations (10% and 30% of 96 h LC₅₀) of SDS (0.34 and 1.02 mg/l), CPC (0.002 and 0.006 mg/l) and SLES (0.69 and 2.07 mg/l) at 15d, 30d and 45d adversely affected the oxidative stress and antioxidant enzymes (CAT, SOD, GST, GPx and MDA) in the liver of Cyprinus carpio. As a result, the study suggests that elicited oxidative stress, prompted by the induction of antioxidant enzymes activity, could be attributable to the stable binding of cytochrome P450 with SDS, CPC and SLES which ultimately leads to the evolution of antioxidant enzymes for its neutralization.

In vitro and in silico analysis reveals antifungal activity and potential targets of curcumin on Paracoccidioides spp

The search for new compounds with activity against Paracoccidioides, etiologic agents of Paracoccidioidomycosis (PCM), is extremely necessary due to the current scenario of the available therapeutic arsenal. Treatment is restricted to three classes of antifungals with side effects. Curcumin is a polyphenol with antifungal effects that is extracted from Curcuma longa. The present work aimed to evaluate the activity of curcumin in different species of Paracoccidioides and to evaluate the potential molecular targets of curcumin using computational strategies. In addition, interactions with classic antifungals used in the treatment of PCM were evaluated. Curcumin inhibits the growth of Paracoccidioides spp. exerting a fungicidal effect. The combination of curcumin with amphotericin B, co-trimoxazole, and itraconazole showed a synergistic or additive interaction. Molecular targets as superoxide dismutase, catalase, and isocitrate lyase were proposed based on in silico approaches. Curcumin affects the fungal plasma membrane and increases the production of reactive oxygen species. Therefore, curcumin is a good alternative for the treatment of PCM.

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.

Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11β-HSD1 Inhibitors

The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4-triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P21/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO2 and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO2/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO2 group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11β-HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11β-HSD1 inhibitors were able to accurately dock within the 11β-HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11β-HSD1 binding affinity scores as well as interactions to a known potent 11β-HSD1 inhibitor.

ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts

ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May-Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.

Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

Cervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

A novel piperazine derivative that targets hepatitis B surface antigen effectively inhibits tenofovir resistant hepatitis B virus

Chronic hepatitis B virus (HBV) infection is a global problem. The loss of hepatitis B surface antigen (HBsAg) in serum is a therapeutic end point. Prolonged therapy with nucleoside/nucleotide analogues targeting the HBV-polymerase may lead to resistance and rarely results in the loss of HBsAg. Therefore, inhibitors targeting HBsAg may have potential therapeutic applications. Here, we used computational virtual screening, docking, and molecular dynamics simulations to identify potential small molecule inhibitors against HBsAg. After screening a million molecules from ZINC database, we identified small molecules with potential anti-HBV activity. Subsequently, cytotoxicity profiles and anti-HBV activities of these small molecules were tested using a widely used cell culture model for HBV. We identified a small molecule (ZINC20451377) which binds to HBsAg with high affinity, with a KD of 65.3 nM, as determined by Surface Plasmon Resonance spectroscopy. Notably, the small molecule inhibited HBsAg production and hepatitis B virion secretion (10 μM) at low micromolar concentrations and was also efficacious against a HBV quadruple mutant (CYEI mutant) resistant to tenofovir. We conclude that this small molecule exhibits strong anti-HBV properties and merits further testing.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method. In this regard, structural proteins from SARS-COV-2, including the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, were selected as target antigens for epitope prediction. A total of five helper T lymphocytes (HTL) and five cytotoxic T lymphocytes (CTL) epitopes were selected after screening the predicted epitopes for antigenicity, allergenicity, and toxicity. Subsequently, the selected HTL and CTL epitopes were fused via flexible linkers. Next, the cholera toxin B-subunit (CTxB) as an adjuvant was linked to the N-terminal of the chimeric structure. The proposed vaccine was analyzed for the properties of physicochemical, antigenicity, and allergenicity. The 3D model of the vaccine construct was predicted and docked with the Toll-like receptor 4 (TLR4). The molecular dynamics (MD) simulation was performed to evaluate the stable interactions between the vaccine construct and TLR4. The immune simulation was also conducted to explore the immune responses induced by the vaccine. Finally, in silico cloning of the vaccine construct into the pET-28 (+) vector was conducted. The results obtained from all bioinformatics analysis stages were satisfactory; however, in vitro and in vivo tests are essential to validate these results.

Designing of Potential Polyvalent Vaccine Model for Respiratory Syncytial Virus by System Level Immunoinformatics Approaches

BACKGROUND

Respiratory syncytial virus (RSV) infection is a public health epidemic, leading to around 3 million hospitalization and about 66,000 deaths each year. It is a life-threatening condition exclusive to children with no effective treatment.

METHODS

In this study, we used system-level and vaccinomics approaches to design a polyvalent vaccine for RSV, which could stimulate the immune components of the host to manage this infection. Our framework involves data accession, antigenicity and subcellular localization analysis, T cell epitope prediction, proteasomal and conservancy evaluation, host-pathogen-protein interactions, pathway studies, and in silico binding affinity analysis.

RESULTS

We found glycoprotein (G), fusion protein (F), and small hydrophobic protein (SH) of RSV as potential vaccine candidates. Of these proteins (G, F, and SH), we found 9 epitopes for multiple alleles of MHC classes I and II bear significant binding affinity. These potential epitopes were linked to form a polyvalent construct using AAY, GPGPG linkers, and cholera toxin B adjuvant at N-terminal with a 23.9 kDa molecular weight of 224 amino acid residues. The final construct was a stable, immunogenic, and nonallergenic protein containing cleavage sites, TAP transport efficiency, posttranslation shifts, and CTL epitopes. The molecular docking indicated the optimum binding affinity of RSV polyvalent construct with MHC molecules (-12.49 and -10.48 kcal/mol for MHC classes I and II, respectively). This interaction showed that a polyvalent construct could manage and control this disease.

CONCLUSION

Our vaccinomics and system-level investigation could be appropriate to trigger the host immune system to prevent RSV infection.

Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Bioinformatics analysis and biochemical characterisation of ABC transporter-associated periplasmic substrate-binding proteins ModA and MetQ from Helicobacter pylori strain SS1

The human gastric pathogen Helicobacter pylori relies on the uptake of host-provided nutrients for its proliferation and pathogenicity. ABC transporters that mediate import of small molecules into the cytoplasm of H. pylori employ their cognate periplasmic substrate-binding proteins (SBPs) for ligand capture in the periplasm. The genome of the mouse-adapted strain SS1 of H. pylori encodes eight ABC transporter-associated SBPs, but little is known about their specificity or structure. In this study, we demonstrated that the SBP annotated as ModA binds molybdate (MoO₄^2-, K_D = 3.8 nM) and tungstate (WO₄^2-, K_D = 7.8 nM). In addition, we showed that MetQ binds D-methionine (K_D = 9.5 μM), but not L-methionine, which suggests the existence of as yet unknown pathway for L-methionine uptake. Homology modelling has led to identification of the ligand-binding residues.

Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.

Computational Methods for the Elucidation of Protein Structure and Interactions

Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.

Functional and structural characterization of a novel GH3 β-glucosidase from the gut metagenome of the Brazilian Cerrado termite Syntermes wheeleri

In this study, a GH3 family β-glucosidase (Bgl7226) from metagenomic sequences of the Syntermes wheeleri gut, a Brazilian Cerrado termite, was expressed, purified and characterized. The enzyme showed two optimum pHs (pH 7 and pH 10), and a maximum optimum temperature of about 40 °C using 4-Nitrophenyl β-D-glucopyranoside (pNPG) as substrate. Bgl7226 showed higher enzymatic activity at basic pH, but higher affinity (K_m) at neutral pH. However, at neutral pH the Bgl7226 enzyme showed higher catalytic efficiency (k_cat/K_m) for pNPG substrate. Predictive analysis about the enzyme structure-function relationship by sequence alignment suggested the presence of multi-domains and conserved catalytic sites. Circular dichroism results showed that the secondary structure composition of the enzyme is pH-dependent. Small conformational changes occurred close to the optimum temperature of 40 ^o C, and seem important for the highest activity of Bgl7226 observed at pH 7 and 10. In addition, the small transition in the unfolding curves close to 40 ^o C is typical of intermediates associated with proteins structured in several domains. Bgl7226 has significant β-glucosidase activity which could be attractive for biotechnological applications, such as plant roots detoxification; specifically, our group is interested in cassava roots (Manihot esculenta) detoxification.

Pleiotropic roles of cold shock proteins with special emphasis on unexplored cold shock protein member of Plasmodium falciparum

The cold shock domain (CSD) forms the hallmark of the cold shock protein family that provides the characteristic feature of binding with nucleic acids. While much of the information is available on bacterial, plants and human cold shock proteins, their existence and functions in the malaria parasite remains undefined. In the present review, the available information on functions of well-characterized cold shock protein members in different organisms has been collected and an attempt was made to identify the presence and role of cold shock proteins in malaria parasite. A single Plasmodium falciparum cold shock protein (PfCoSP) was found in P. falciparum which is reported to be essential for parasite survival. Essentiality of PfCoSP underscores its importance in malaria parasite life cycle. In silico tools were used to predict the features of PfCoSP and to identify its homologues in bacteria, plants, humans, and other Plasmodium species. Modelled structures of PfCoSP and its homologues in Plasmodium species were compared with human cold shock protein 'YBOX-1' (Y-box binding protein 1) that provide important insights into their functioning. PfCoSP model was subjected to docking with B-form DNA and RNA to reveal a number of residues crucial for their interaction. Transcriptome analysis and motifs identified in PfCoSP implicate its role in controlling gene expression at gametocyte, ookinete and asexual blood stages of malaria parasite. Overall, this review emphasizes the functional diversity of the cold shock protein family by discussing their known roles in gene expression regulation, cold acclimation, developmental processes like flowering transition, and flower and seed development, and probable function in gametocytogenesis in case of malaria parasite. This enables readers to view the cold shock protein family comprehensively.

Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors

Background

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.

Methodology

Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis.

Results

We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC₅₀ values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors.

Conclusion

Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.

Nitrogen Catabolite Repression in members of Paracoccidioides complex

Paracoccidioides complex is a genus that comprises pathogenic fungi which are responsible by systemic disease Paracoccidioidomycosis. In host tissues, pathogenic fungi need to acquire nutrients in order to survive, making the uptake of nitrogen essential for their establishment and dissemination. Nitrogen utilization is employed by the alleviation of Nitrogen Catabolite Repression (NCR) which ensures the use of non-preferential or alternative nitrogen sources when preferential sources are not available. NCR is controlled by GATA transcription factors which act through GATA binding sites on promoter regions in NCR-sensitive genes. This process is responsible for encoding proteins involved with the scavenge, uptake and catabolism of a wide variety of non-preferential nitrogen sources. In this work, we predict the existence of AreA GATA transcription factor and feature the zinc finger domain by three-dimensional structure in Paracoccidioides. Furthermore, we demonstrate the putative genes involved with NCR response by means of in silico analysis. The gene expression profile under NCR conditions was evaluated. Demonstrating that P. lutzii supported transcriptional regulation and alleviated NCR in non-preferential nitrogen-dependent medium. The elucidation of NCR in members of Paracoccidioides complex will provide new knowledge about survival, dissemination and virulence for these pathogens with regard to nitrogen-scavenging strategies in the interactions of host-pathogens.

refineD: improved protein structure refinement using machine learning based restrained relaxation

MOTIVATION

Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.

RESULTS

Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints.

AVAILABILITY AND IMPLEMENTATION

http://watson.cse.eng.auburn.edu/refineD/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.