CABS-flex: Server for fast simulation of protein structure fluctuations

Rational Design of a Novel DNA Polymerase From Clostridium thermocellum to Improve LAMP Detection Performance

Loop-mediated isothermal amplification (LAMP) is a detection method widely used in pathogen detection and clinical diagnosis. Nevertheless, it is highly constrained by thermal stability, catalytic activity, and resistance to inhibitors of Bst DNA polymerase. In this study, a novel DNA polymerase was characterized from Clostridium thermocellum, exhibiting potential in LAMP detection. Through bioinformatics analysis, the enzyme and the DNA-binding domain (DBD) from Pyrococcus abyssi were mutated for enhanced interaction between proteins and DNA. A chimeric mutant DBDE146K-S738R reaches the detection threshold 13 min earlier than wild-type Cth DNA polymerase in real-time LAMP detection with a template concentration of 1.58 × 105 fg/µL. It also showed the highest enzymatic activity at pH 9.0 and 65°C. The chimeric enzyme DBDE146K-S738R exhibits good thermal stability, capable of performing LAMP reactions after treatment at 73°C or 70°C for 8 h. Moreover, it maintains high activity even under the inhibitory conditions of 50 U/mL heparin, 1.6 mM EDTA, 200 mM NaCl, 10% ethanol, 1.2 M urea, or 0.8% phenol. Notably, it was able to detect 1.58 × 102 ag/µL of the genome and 1.03 CFU/mL of the colony in Salmonella typhimurium detection. The enzyme's performance is superior to commercial Bst 2.0 and comparable to commercial Bst 3.0. The results suggest that DBDE146K-S738R in LAMP exhibits great potential for molecular biological studies and clinical diagnostic analysis.

Importance of Secondary Structure Data in Large Scale Protein Modeling Using Low-Resolution SURPASS Method

Secondary structure elements, such as alpha helices and beta strands, play a fundamental role in defining the overall fold of a protein. Leveraging secondary structure information is essential for encoding the structural features in coarse-grained protein models. Such models simplify the representation of amino acid residues, thereby reducing computational complexity. By incorporating accurate (even if only partial) secondary structure data, the models can efficiently search for the native conformation of proteins and preserve the core structural motifs across extended time frames. Here, the pivotal role of (predicted) secondary structure data in the coarse-grained modeling of protein tertiary and quaternary structures, along with their long-time dynamics, is investigated. Computational simulations of large protein systems using a low-resolution SURPASS model were performed. These case studies demonstrate the sufficiency of predicted secondary structure data in an accurate fold assembly. It leads to a realistic depiction of long-time dynamics in the recorded pseudo-trajectories by employing the Monte Carlo dynamics sampling schema, based on a long random sequence of local conformational modifications. This approach may provide a powerful tool for investigating the critical stages of protein folding. Future combination with knowledge-based potentials derived using machine learning techniques offers exciting opportunities to unravel the underlying mechanisms of biological processes in a variety of molecular complexes.

Treatment of oxidative damage caused by valproic acid in tongue tissue with ethanolic Moringa oleifera leaves extract and prediction of potential bioactive molecules with molecular docking

Moringa oleifera (M. oleifera) is a popular medicinal plant that has become a wide research area in recent years due to its detected biological effects and its bioactive compounds. Valproic acid (VPA) is a medication used in the treatment of epilepsy and bipolar disorder and high doses or prolonged use of VPA can result in oxidative stress in cells. Since M. oleifera has high biological activities and contains many bioactive compounds, it is necessary to understand whether it plays a role in reducing oxidative damage, especially that caused VPA. The relationship between VPA and tongue tissue needs to be investigated, since VPA has negative effects on oral health and it is known that tongue tissue plays an important role in the continuity of oral health. In the present study, 3.0-3.5 month-old female Sprague Dawley rats (160-250 g) were divided into four groups (Control, Moringa, VPA, VPA + M), and VPA was administered via gavage. The aim was to understand the protective/preventive effects of ethanolic M. oleifera leaves extract against oxidative stress through biochemical parameters. Additionally, molecular docking studies were conducted on niazicin-A, niazimin-A, and niazimin-B found in M. oleifera leaves based on in vivo results. The results indicate that M. oleifera extract treats oxidative damage to the tongue tissue, and niazimin-A and niazimin-B particularly show high binding affinities to myeloperoxidase (MPO) and lactate dehydrogenase (LDH) enzymes. Further studies may suggest that the use of M. oleifera leaves extract with VPA could prevent potential negative effects on tongue tissue.

Multiscale modeling of protofilament structures: A case study on insulin amyloid aggregates

Under certain conditions, proteins may undergo misfolding and form long insoluble aggregates called amyloid fibrils. The presence of these aggregates is often associated with various diseases. The molecular mechanisms governing the aggregation process are yet to be fully understood. The self-assembly of amyloid protofilaments occurs over extended time frames, making the simulation of such events problematic. In this work, we describe a pipeline for multiscale modeling protofilament structures. In the first stage, the self-assembly of short fibrillar oligomers occurs during coarse-grained docking simulations of multiple copies of aggregating peptides. Subsequently, symmetry criteria are used to select the highest-ranked oligomer structures. Selected models are then reconstructed to an all-atom representation and used for the assembly of longer protofilaments. Models are optimized using molecular dynamics. Final structures are selected using various scoring protocols. We evaluated this modeling procedure through the test prediction of insulin amyloid protofilaments whose experimental structures have been published recently. The resulting insulin protofilament models closely resemble the experimental structures. This work provides a proof of concept for the proposed modeling procedure aiming to predict amyloid protofilament structures that exhibit in-register and parallel arrangement of β-sheets based solely on the amino acid sequence of aggregating peptides.

Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations

CABS-flex is a well-established method for fast protein flexibility simulations, offering an effective balance between computational efficiency and accuracy in modeling protein dynamics. To further enhance its predictive capabilities, we propose incorporating AlphaFold's predicted Local Distance Difference Test (pLDDT) scores into CABS-flex simulations. The pLDDT scores, which reflect the confidence of AlphaFold's structural predictions, were integrated with secondary structure information to refine the restraint schemes used in the simulations. We tested this approach on the ATLAS database, which includes molecular dynamics (MD) simulations of nearly 1400 proteins. The results showed improved alignment of flexibility predictions with the MD data compared to previous restraint schemes. The integration of pLDDT scores also offers a new perspective on protein flexibility by incorporating structural confidence into the analysis. This development enhances the utility of CABS-flex for investigating protein dynamics and motion.

Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate

Escherichia coli (E. coli), a gram-negative bacterium, quickly colonizes in the human gastrointestinal tract after birth and typically sustains a long-term, symbiotic relationship with the host. However, certain virulent strains of E. coli can cause diseases such as urinary tract infections, meningitis, and enteric disorders. The rising antibiotic resistance among these strains has heightened the urgency for an effective vaccine. This study employs immunoinformatics and a reverse vaccinology technique to identify prospective antigens and create an efficient vaccine construct. In this study, we reported the "Attaching and Effacing Protein" a novel outer-membrane protein conserved in all pathogenic E. coli strains, based on proteome screening. We developed an in silico multi-epitope vaccine that includes helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL), B cell lymphocyte (BCL), and pan HLA DR-binding reactive epitope (PADRE) sequences, along with appropriate linkers and adjuvants. Machine Learning algorithms were used to evaluate antigenicity, solubility, stability, and non-allergenicity of the vaccine construct. Additionally, molecular docking analysis revealed that vaccine construct has a strong predicted binding affinity for human toll-like receptors on the cell surface. In this context, laboratory validations are necessary to demonstrate the effectiveness of the possible vaccine design that showed encouraging findings through computational validation.

New oxomethacrylate and acetamide: synthesis, characterization, and their computational approaches: molecular docking, molecular dynamics, and ADME analyses

The compounds 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) and 2-(3-methoxyphenylamino)-2-oxoethyl methacrylate (3MPAEMA) were synthesized in this study for the first time in the literature. FTIR, 1H, and 13C NMR spectroscopic techniques were used to characterize it. Subsequently, computational techniques were used to assess various ADME factors, such as drug-likeness properties, bioavailability score, and adherence to Lipinski's rule. Finally, molecular docking experiments were conducted with the human topoisomerase α2 (TOP2A) protein to verify and validate the reliability and stability of the docking procedure. The results of the docking scores, which quantify binding affinity, indicated that these derivatives exhibited a stronger affinity for TOP2A.

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning

Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separated multi polypeptide chains within PDB data into single polypeptide chains, removing redundancy. This effort resulted in a dataset comprising 302 032 unique PDB entries. Subsequently, we compared sequence similarity and obtained 22 774 Homo sapiens data from AlphaFold2.0. Using this expanded and refined dataset, the trained GCN model for PA prediction achieves a remarkable coefficient of determination (R 2) score of 0.9849 and a low mean absolute error (MAE) of 0.0381. Furthermore, the efficacy of the active learning process was demonstrated through its rapid identification of proteins with high PA propensity. Consequently, the active learning approach achieved an MAE of 0.0291 in expected improvement, surpassing other methods. It identified 99% of the target proteins by exploring merely 29% of the entire search space. This improved GCN model demonstrates promise in selecting proteins susceptible to PA, advancing protein science. This work contributes to the development of efficient computational tools for PA prediction, with potential applications in disease diagnosis and therapy.

The protective effects of Myrtus communis subsp. on ovariectomized diabetic rats' renal and intestinal tissues: in vivo and in silico approaches

INTRODUCTION

Postmenopausal diabetes is a condition that affects millions of women and their quality of life. Also, kidney and small intestine tissues are damaged due to diabetes. The present study aimed to examine the protective effects of an extract prepared from Myrtus communis leaves on kidney and small intestine tissues against experimentally created postmenopausal diabetes.

METHODS

For this purpose, experimental rats were randomly divided into six groups (Control; ovariectomy:OVX, diabetic:D, ovariectomy + diabetic:OVX + D, ovariectomy + diabetic + oestrogen:OVX + D+E2, ovariectomy + diabetic + MC: OVX + D+MC) and kidney and small intestine tissues were taken after the experimental procedure.

RESULTS

Evaluations of biochemical parameters (glutathione and glutathione-related enzymes, antioxidant enzymes, etc.) showed that MC had a protective effect on kidney and small intestine tissues in diabetes and ovariectomy groups.

CONCLUSION

It can be suggested that MC extract has a protective effect on small intestine and kidney tissues in postmenopausal diabetes and may be a good herbal source for this purpose.

Assessment of Four Theoretical Approaches to Predict Protein Flexibility in the Crystal Phase and Solution

In this paper, we evaluated the ability of four coarse-grained methods to predict protein flexible regions with potential biological importance, UNRES-flex, UNRES-DSSP-flex (based on the united residue model of polypeptide chains without and with secondary structure restraints, respectively), CABS-flex (based on the C-α, C-β, and side chain model), and nonlinear rigid block normal mode analysis (NOLB) with a set of 100 protein structures determined by NMR spectroscopy or X-ray crystallography, with all secondary structure types. End regions with high fluctuations were excluded from analysis. The Pearson and Spearman correlation coefficients were used to quantify the conformity between the calculated and experimental fluctuation profiles, the latter determined from NMR ensembles and X-ray B-factors, respectively. For X-ray structures (corresponding to proteins in a crowded environment), NOLB resulted in the best agreement between the predicted and experimental fluctuation profiles, while for NMR structures (corresponding to proteins in solution), the ranking of performance is CABS-flex > UNRES-DSSP-flex > UNRES-flex > NOLB; however, CABS-flex sometimes exaggerated the extent of small fluctuations, as opposed to UNRES-DSSP-flex.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines

Human cytomegalovirus (HCMV) is accountable for high morbidity in neonates and immunosuppressed individuals. Due to the high genetic variability of HCMV, current prophylactic measures are insufficient. In this study, we employed a pan-genome and reverse vaccinology approach to screen the target for efficient vaccine candidates. Four proteins, envelope glycoprotein M, UL41A, US23, and US28, were shortlisted based on cellular localization, high solubility, antigenicity, and immunogenicity. A total of 29 B-cell and 44 T-cell highly immunogenic and antigenic epitopes with high global population coverage were finalized using immunoinformatics tools and algorithms. Further, the epitopes that were overlapping among the finalized B-cell and T-cell epitopes were linked with suitable linkers to form various combinations of multi-epitopic vaccine constructs. Among 16 vaccine constructs, Vc12 was selected based on physicochemical and structural properties. The docking and molecular simulations of VC12 were performed, which showed its high binding affinity (-23.35 kcal/mol) towards TLR4 due to intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions, and there were only minimal fluctuations. Furthermore, Vc12 eliciting a good response was checked for its expression in Escherichia coli through in silico cloning and codon optimization, suggesting it to be a potent vaccine candidate.

Cefiderocol susceptibility endows hope in treating carbapenem-resistant Pseudomonas aeruginosa: insights from in vitro and in silico evidence

'High-risk' hypermutable clones of Pseudomonas aeruginosa disseminating extensive drug-resistance (XDR) have raised global health concerns with escalating mortality rates in immunocompromised patients. Mutations in conventional drug-targets under antibiotic stress necessitate structural understanding to formulate sustainable therapeutics. In the present study, the major β-lactam antibiotic target, penicillin-binding protein-3 (PBP3) with mutations F533L and T91A, were identified in carbapenemase-positive P. aeruginosa isolates (n = 6) using whole genome sequencing. Antibiotic susceptibility tests showed susceptibility to cefiderocol (MIC ≤ 4 μg ml-1) despite pan-β-lactam resistance in the isolates. Both the mutations reduced local intra-chain interactions in PBP3 that marginally increased the local flexibility (∼1%) in the structures to affect antibiotic-interactions. Molecular dynamics simulations confirmed the overall stability of the PBP3 mutants through root-mean square deviations, radius of gyration, solvent-accessibility and density curves, which favored their selection. Docking studies unveiled that the mutations in PBP3 elicited unfavorable stereochemical clashes with the conventional antibiotics thereby increasing their inhibition constants (IC) up to ∼50 fold. It was deciphered that cefiderocol retained its susceptibility despite mutations in PBP3, due to its higher average binding affinity (ΔG: -8.2 ± 0.4 kcal mol-1) towards multiple PBP-targets and lower average binding affinity (ΔG: -6.7 ± 0.7 kcal mol-1) to β-lactamases than the other β-lactam antibiotics. The molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area calculations further indicated energetically favorable binding for cefiderocol with PBP3 proteins. The study gave structural insight into emerging non-polar amino acid substitutions in PBP3 causing XDR and recommends prioritizing available antibiotics based on multi-target affinities to overcome challenges imposed by target-protein mutations.

Hotspot Wizard-informed engineering of a hyperthermophilic β-glucosidase for enhanced enzyme activity at low temperatures

Hyperthermophilic enzymes serve as an important source of industrial enzymes due to their high thermostability. Unfortunately, most hyperthermophilic enzymes suffer from reduced activity at low temperatures (e.g., ambient temperature), limiting their applicability. In addition, evolving hyperthermophilic enzymes to increase low temperature activity without compromising other desired properties is generally difficult. In the current study, a variant of β-glucosidase from Pyrococcus furiosus (PfBGL) was engineered to enhance enzyme activity at low temperatures through the construction of a saturation mutagenesis library guided by the HotSpot Wizard analysis, followed by its screening for activity and thermostability. From this library construction and screening, one PfBGL mutant, PfBGL-A4 containing Q214S/A264S/F344I mutations, showed an over twofold increase in β-glucosidase activity at 25 and 50°C compared to the wild type, without compromising high-temperature activity, thermostability and substrate specificity. Our experimental and computational characterizations suggest that the findings with PfBGL-A4 may be due to the elevation of local conformational flexibility around the active site, while slightly compacting the global protein structure. This study showcases the potential of HotSpot Wizard-informed engineering of hyperthermophilic enzymes and underscores the interplays among temperature, enzyme activity, and conformational flexibility in these enzymes.

Novel Antimicrobial Peptide SAAP Mutant as a Better Adjuvant to Sulbactam-Based Treatments Against Clinical Strains of XDR Acinetobacter baumannii

The production of extended spectrum β-lactamases (ESBLs) in extensively drug-resistant (XDR) strains of Acinetobacter baumannii has created havoc amongst clinicians making the treatment procedure challenging. Carbapenem-resistant strains have displayed total ineffectiveness towards newer combinations of β-lactam-β-lactamase inhibitors (βL-βLI) in tertiary healthcare settings. Therefore, the present study was aimed to design potential β-lactamase antimicrobial peptide (AMP) inhibitors against ESBLs produced by the strains. We have constructed an AMP mutant library with higher antimicrobial efficacy (range: ~ 15 to 27%) than their parent peptides. The mutants were thoroughly screened based on different physicochemical and immunogenic properties revealing three peptides, namely SAAP-148, HFIAP-1, myticalin-C6 and their mutants with safe pharmacokinetics profile. Molecular docking highlighted SAAP-148_M15 displaying maximum inhibitory potential with lowest binding energies against NDM1 (- 1148.7 kcal/mol), followed by OXA23 (- 1032.5 kcal/mol) and OXA58 (- 925.3 kcal/mol). The intermolecular interaction profiles displayed SAAP-148_M15 exhibiting hydrogen bonds and van der Waals hydrophobic interactions with the crucial residues of metallo β-lactamase [IPR001279] and penicillin-binding transpeptidase [IPR001460] domains. Coarse-grained clustering and molecular dynamics simulations (MDS) further validated the stable backbone profile and minimal residue-level fluctuations of the protein-peptide complex that were maintained throughout the simulation timeframe. The present study hypothesised that the combination of sulbactam (βL) with SAAP-148_M15 (βLI) holds immense potential in inhibiting the ESBLs alongside restoration of sulbactam activity. The current in silico findings upon further experimental validations can pave path towards designing of successful therapeutic strategy against XDR strains of A. baumannii.

In Silico Insights into the Arsenic Binding Mechanism Deploying Application of Computational Biology-Based Toolsets

An assortment of environmental matrices includes arsenic (As) in its different oxidation states, which is often linked to concerns that pose a threat to public health worldwide. The current difficulty lies in addressing toxicological concerns and achieving sustained detoxification of As. Multiple conventional degradation methods are accessible; however, they are indeed labor-intensive, expensive, and reliant on prolonged laboratory evaluations. Molecular interaction and atomic level degradation mechanisms for enzyme-As exploration are, however, underexplored in those approaches. A feasible approach in this case for tackling this accompanying concern of As might be to cope with undertaking multivalent computational methodologies and tools. This work aimed to provide molecular-level insight into the enzyme-aided As degradation mechanism. AutoDock Vina, CABS-flex 2.0, and Desmond high-performance molecular dynamics simulation (MDS) were utilized in the current investigation to simulate multivalent molecular processes on two protein sets: arsenate reductase (ArsC) and laccase (LAC) corresponding arsenate (ART) and arsenite (AST), which served as model ligands to comprehend binding, conformational, and energy attributes. The structural configurations of both proteins exhibited variability in flexibility and structure framework within the range of 3.5-4.5 Å. The LAC-ART complex exhibited the lowest calculated binding affinity, measuring -5.82 ± 0.01 kcal/mol. Meanwhile, active site residues ILE-200 and HIS-206 were demonstrated to engage in H-bonding with the ART ligand. In contrast to ArsC, the ligand binding affinity of this bound complex was considerably greater. Additional validation of docked complexes was carried out by deploying Desmond MDS of 100 ns to capture protein and ligand conformation behavior. The system achieved stability during the 100 ns simulation run, as confirmed by the average P-L RMSD, which was ∼1 Å. As a preliminary test of the enzyme's ability to catalyze As species, corresponding computational insights might be advantageous for bridging gaps and regulatory consideration.

The evaluations of the inhibition of orlistat on Clostridium perfringens sialidase (NanI) activity by in vitro and in silico approaches

Clostridium perfringens (C. perfringens) is a bacterium that causes serious problems in humans and animals such as food poisoning, gas gangrene and infections. C. perfringens has three sialidases (NanH, NanI, NanJ) and inhibition of NanI constitutes an approach in the treatment of C. perfringens since NanI provides the carbohydrate source necessary for the growth of bacteria. In our study, the inhibition effect of some drugs belonging to different drug groups on NanI activity was investigated. Among these drugs, orlistat (0.21±0.05 μM) was determined to have a lower IC50 value than the positive control quercetin (15.58±1.59 μM). It was determined in vitro by spectrofluorometric method. Additionally, NanI molecular docking studies with orlistatand quercetin were performed using iGemdock, DockThor and SwissDock. Orlistat (-93.93, -8.649 and -10.03 kcal/mol, respectively) was found to have a higher binding affinity than quercetin (-92.68, -7.491 and -8.70 kcal/mol, respectively), and the results were in line with in vitro studies. The results may suggest that orlistat is a molecule with drug potential for C. perfringens because it inhibits the drug target NanI, and that the inhibition efficiency can be increased by studies with orlistat derivatives.

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.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

Impact of PmrB mutations on clinical Klebsiella pneumoniae with variable colistin-susceptibilities: Structural insights and potent therapeutic solutions

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections continue to impose high morbidity threats to hospitalized patients worldwide, limiting therapeutic options to last-resort antibiotics like colistin. However, the dynamic genomic landscape of colistin-resistant K. pneumoniae (COLR-Kp) invoked ardent exploration of underlying molecular signatures for therapeutic propositions/designs. We unveiled the structural impact of the widespread and emerging PmrB mutations involved in colistin resistance (COLR) in K. pneumoniae. In the present study, clinical isolates of K. pneumoniae expressed variable susceptibilities to colistin (>0.5 μg/mL for resistant and ≤0.25 μg/mL for susceptible) despite mutations such as T157P, G207D and T246A. The protein sequences extracted from in-house sequenced genomes were used to model mutant PmrB proteins and analyze the underlying structural alterations. The mutations were contrasted based on molecular dynamics simulation trajectories, free-energy landscapes and structural flexibility profiles. The altered backbone flexibilities can be an essential factor for mutant selection by COLR K. pneumoniae and can provide clues to deal with emerging mutants. Furthermore, PmrB having high druggability confidence (>0.99), was explored as a potential target for 1396 virtually screened FDA-approved drug candidates. Among the top-10 compounds (scores >70), amphotericin B was found to be potential candidate with high affinity (Binding energy <-8 kcal/mol) and stable interactions (RMSF <0.7 Å) against PmrB druggable pockets, despite the mutations, which encourages future adjunct therapeutic research against COLR-Kp.

Design, In silico Screening, Synthesis, Characterisation and DFT-based Electronic Properties of Dihydropyridine-based Molecule as L-type Calcium Channel Blocker

BACKGROUND

People of all nationalities and social classes are now affected by the growing issue of hypertension. Over time, there has been a consistent rise in the fatality rate. A range of therapeutic compounds, on the other hand, are often used to handle hypertension.

OBJECTIVES

The objectives of this study are first to design potential antihypertensive drugs based on the DHP scaffold, secondly, to analyse drug-likeness properties of the ligands and investigate their molecular mechanisms of binding to the model protein Cav1.2 and finally to synthesise the best ligand.

MATERIALS AND METHODS

Due to the lack of 3D structures for human Cav1.2, the protein structure was modelled using a homology modelling approach. A protein-ligand complex's strength and binding interaction were investigated using molecular docking and molecular dynamics techniques. DFT-based electronic properties of the ligand were calculated using the M06-2X/ def2- TZVP level of theory. The SwissADME website was used to study the ADMET properties.

RESULTS

In this study, a series of DHP compounds (19 compounds) were properly designed to act as calcium channel blockers. Among these compounds, compound 16 showed excellent binding scores (-11.6 kcal/mol). This compound was synthesised with good yield and characterised. To assess the structural features of the synthesised molecule quantum chemical calculations were performed.

CONCLUSION

Based on molecular docking, molecular dynamics simulations, and drug-likeness properties of compound 16 can be used as a potential calcium channel blocker.

Proteome based analysis of circulating SARS-CoV-2 variants: approach to a universal vaccine candidate

The discovery of the first infectious variant in Wuhan, China, in December 2019, has posed concerns over global health due to the spread of COVID-19 and subsequent variants. While the majority of patients experience flu-like symptoms such as cold and fever, a small percentage, particularly those with compromised immune systems, progress from mild illness to fatality. COVID-19 is caused by a RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach involved utilizing immunoinformatic to identify vaccine candidates with multiple epitopes and ligand-binding regions in reported SARS-CoV-2 variants. Through analysis of the spike glycoprotein, we identified dominant epitopes for T-cells and B-cells, resulting in a vaccine construct containing two helper T-cell epitopes, six cytotoxic T-cell epitopes, and four linear B-cell epitopes. Prior to conjugation with adjuvants and linkers, all epitopes were evaluated for antigenicity, toxicity, and allergenicity. Additionally, we assessed the vaccine Toll-Like Receptors complex (2, 3, and 4). The vaccine construct demonstrated antigenicity, non-toxicity, and non-allergenicity, thereby enabling the host to generate antibodies with favorable physicochemical characteristics. Furthermore, the 3D structure of the B-cell construct exhibited a ProSA-web z-score plot with a value of -1.71, indicating the reliability of the designed structure. The Ramachandran plot analysis revealed that 99.6% of the amino acid residues in the vaccine subunit were located in the high favored observation region, further establishing its strong candidacy as a vaccination option.

Structure Elucidation and Interaction Dynamics of MefA-MsrD Efflux Proteins in Streptococcus pneumoniae: Impact on Macrolide Susceptibility

Macrolides are empirically used to treat bacterial community-acquired pneumonia (CAP). Streptococcus pneumoniae, being the major pathogen responsible for bacterial CAP with high mortality rates, express MefA-MsrD efflux pumps to hinder macrolide susceptibility. Despite its importance, the structural features of the efflux-protein complex and its impact on macrolide susceptibility have not yet been elucidated explicitly. Therefore, in the present study, combining homology, threading, and dynamics approaches, MefA and MsrD proteins in pathogenic S. pneumoniae were modeled. Both membrane (lipid-bilayer) and cytoplasmic (aqueous) environments were considered to simulate the MefA and MsrD proteins in their ideal cellular conditions followed by dynamics analyses. The simulated MefA structure represented a typical major facilitator superfamily protein structure with 13 transmembrane helices. MefA-MsrD interaction via clustering-based docking revealed low-energy conformers with stable intermolecular interactions. The higher clinical MIC value of azithromycin over erythromycin was reflected upon erythromycin eliciting stronger interactions (dissociation constant or ki = ∼52 μM) with the cytoplasmic ATP-binding MsrD than azithromycin (ki = ∼112 μM). The strong (binding energy = -132.1 ± 9.5 kcal/mol) and highly stable (root-mean-square fluctuation < 1.0 Å) physical association between MefA with MsrD was validated and was found to be unaffected by the antibiotic binding. Higher propensity of the macrolides to interact with MsrD than MefA established the importance of the former in macrolide susceptibility. Ours is probably the first report on the structural arrangements in the MefA-MsrD efflux complex and the macrolide susceptibility in S. pneumoniae. This study provides a novel lead for experimental explorations and efflux-pump inhibitor designs.

Structural Basis for the Enzymatic Activity of the HACE1 HECT-Type E3 Ligase Through N-Terminal Helix Dimerization

HACE1 is an ankyrin repeat (AKR) containing HECT-type E3 ubiquitin ligase that interacts with and ubiquitinates multiple substrates. While HACE1 is a well-known tumor suppressor, its structure and mode of ubiquitination are not understood. The authors present the cryo-EM structures of human HACE1 along with in vitro functional studies that provide insights into how the enzymatic activity of HACE1 is regulated. HACE1 comprises of an N-terminal AKR domain, a middle (MID) domain, and a C-terminal HECT domain. Its unique G-shaped architecture interacts as a homodimer, with monomers arranged in an antiparallel manner. In this dimeric arrangement, HACE1 ubiquitination activity is hampered, as the N-terminal helix of one monomer restricts access to the C-terminal domain of the other. The in vitro ubiquitination assays, hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis, mutagenesis, and in silico modeling suggest that the HACE1 MID domain plays a crucial role along with the AKRs in RAC1 substrate recognition.

Understanding the Molecular Regulation of Serotonin Receptor 5-HTR1B-β-Arrestin1 Complex in Stress and Anxiety Disorders

The serotonin receptor subtype 5-HTR1B is widely distributed in the brain with an important role in various behavioral implications including neurological conditions and psychiatric disorders. The neuromodulatory action of 5-HTR1B largely depends upon its arrestin mediated signaling pathway. In this study, we tried to investigate the role of unusually long intracellular loop 3 (ICL3) region of the serotonin receptor 5-HTR1B in interaction with β-arrestin1 (Arr2) to compensate for the absence of the long cytoplasmic tail. Molecular modeling and docking tools were employed to obtain a suitable molecular conformation of the ICL3 region in complex with Arr2 which dictates the specific complex formation of 5-HTR1B with Arr2. This reveals the novel molecular mechanism of phosphorylated ICL3 mediated GPCR-arrestin interaction in the absence of the long cytoplasmic tail. The in-cell disulfide cross-linking experiments and molecular dynamics simulations of the complex further validate the model of 5-HTR1B-ICL3-Arr2 complex. Two serine residues (Ser281 and Ser295) within the 5-HTR1B-ICL3 region were found to be occupying the electropositive pocket of Arr2 in our model and might be crucial for phosphorylation and specific Arr2 binding. The alignment studies of these residues showed them to be conserved only across 5-HTR1B mammalian species. Thus, our studies were able to predict a molecular conformation of 5-HTR1B-Arr2 and identify the role of long ICL3 in the signaling process which might be crucial in designing targeted drugs (biased agonists) that promote GPCR-Arr2 signaling to deter the effects of stress and anxiety-like disorders.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Bioactive phytocompounds against specific target proteins of Borrelia recurrentis responsible for louse-borne relapsing fever: Genomics and structural bioinformatics evidence

Louse-borne relapsing fever (LBRF) with high untreated mortality caused by spirochete Borrelia recurrentis is predominantly endemic to Sub-Saharan Africa and has re-emerged in parts of Eastern Europe, Asia and Latin America due to population migrations. Despite subtractive evolution of lice-borne pathogenic Borrelia spp. from tick-borne species, there has been no comprehensive report on conservation of protein targets across tick and lice-borne pathogenic Borrelia nor exploration of phytocompounds that are toxic to tick against lice. From the 19 available whole genomes including B. recurrentis, B. burgdorferi, B. hermsii, B. parkeri and B. miyamotoi, conservation of seven drug targets (>80% domain identity) viz. 30 S ribosomal subunit proteins (RSP) S3, S7, S8, S14, S19, penicillin-binding protein-2 and 50 S RSP L16 were deciphered through multiple sequence alignments. Twelve phytocompounds (hydroxy-tyrosol, baicalein, cis-2-decanoic acid, morin, oenin, rosemarinic acid, kaempferol, piceatannol, rottlerin, luteolin, fisetin and monolaurin) previously explored against Lyme disease spirochete B. burgdorferi when targeted against LBRF-causing B. recurrentis protein targets revealed high multi-target affinity (2%-20% higher than conventional antibiotics) through molecular docking. However, based on high binding affinity against all target proteins, stable coarse-grained dynamics (fluctuations <1 Å) and safe pharmacological profile, luteolin was prioritized. The study encourages experimental evaluation of the potent phytocompounds and similar protocols for investigating other emerging vector-borne diseases.

Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Ų); Rg (nm); PSA (Å); SASA (Ų), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where β-amyrin scored the most significant values in all aspects.

Genomics and structural insight into the masking of gentamicin-resistance in clinical Burkholderia pseudomallei strain VB29710 from India

The present study reported a rare gentamicin-susceptible β-lactamase (PenA, OXA-57) expressing clinical Burkholderia pseudomallei isolate VB29710 from India. Whole-genome sequencing and structural analyses revealed the insertion of R962 and L963 into AmrB, the transmembrane-protein of the AmrAB-OprA efflux-pump that affected aminoglycoside-efflux through local alterations in backbone conformation.

Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

Lignin-modifying enzymes (LMEs) are impactful biocatalysts in environmental remediation applications. However, LMEs-assisted experimental degradation neglects the molecular basis of pollutant degradation. Furthermore, throughout the remediation process, the inherent hazards of environmental pollutants remain untapped for in-depth toxicological endpoints. In this investigation, a predictive toxicological framework and a computational framework adopting LMEs were employed to assess the hazards of Priority Pollutants (PP) and its possible LMEs-assisted catalytic screening. The potential hazardous outcomes of PP were assessed using Quantitative structure-activity relationship (QSARs)-based techniques including Toxtree, ECOSAR, and T.E.S.T. tools. Toxicological findings revealed positive outcomes in a multitude of endpoints for all PP. The PP compound 2,3,7,8-TCDD (dioxin) was found to exhibit the lowest concentration of aquatic toxicity implementing aquatic model systems; LC₅₀ as 0.01, 0.01, 0.04 (mg L^-1) for Fish (96 H), Daphnid (48 H), Green algae (96 H) respectively. T.E.S.T. results revealed that chloroform, and 2-chlorophenol both seem to be developmental toxicants. Subsequently, LMEs-assisted docking procedure was employed in predictive mitigation of PP. The docking approach as predicted degradation revealed the far lowest docking energy score for Versatile peroxidase (VP)- 2,3,7,8-TCDD docked complex with a binding energy of -9.2 (kcal mol^-1), involved PHE-46, PRO-139, PRO-141, ILE-148, LEU-165, HIS-169, LEU-228, MET-262, and MET-265 as key interacting amino acid residues. Second most ranked but lesser than VP, Lignin peroxidase (LiP)- 2,3,7,8-TCDD docked complex exhibited a rather lower binding affinity score (-8.8 kcal mol^-1). Predictive degradation screening employing comparative docking revealed varying binding affinities, portraying that each LMEs member has independent feasibility to bind PP as substrate. Predictive findings endorsed the hazardous nature of associated PP in a multitude of endpoints, which could be attenuated by undertaking LMEs as a predictive approach to protect the environment and implement it in regulatory considerations.

Structural chemistry and molecular-level interactome reveals histidine kinase EvgS to subvert both antimicrobial resistance and virulence in Shigella flexneri 2a str. 301

Multi-drug resistant (MDR) Shigella flexneri 2a, one of the leading bacterial agents of diarrhoeal mortality, has posed challenges in treatment strategies. The present study was conducted to identify potential therapeutic biomarkers using gene interaction network (GIN) in order to understand the cellular and molecular level interactions of both antimicrobial resistance (AMR) and virulence genes through topological and clustering metrics. Statistically significant differential gene expression (DGE), structural chemistry and dynamics were incorporated to elucidate biomarker for sustainable therapeutic regimen against MDR S. flexneri. Functional enrichments and topological metrics revealed evgS, ybjZ, tolC, gyrA, parC and their direct interactors to be associated with diverse AMR mechanisms. Histidine kinase EvgS was considered as the hub protein due to its highest prevalence in the molecular interactome profiles of both the AMR (71.6%) and virulence (45.8%) clusters interconnecting several genes concerning two-component system (TCS). DGE profiles of ΔPhoPQ (deleted regulatory PhoP and sensor PhoQ) led to the upregulation of TCS comprising EvgSA thereby validating EvgS as a promising therapeutic biomarker. Druggability and structural stability of EvgS was assessed through thermal shifts, backbone stability and coarse dynamics refinement. Structure-function relationship was established revealing the C-terminal extracellular domain as the drug-binding site which was further validated through molecular dynamics simulation. Structure elucidation of identified biomarker followed by secondary and tertiary structural validation would prove pivotal for future therapeutic interventions against subverting both AMR and virulence posed by this strain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03325-w.

Identification of novel salt tolerance-associated proteins from the secretome of Enterococcus faecalis

The ability of bacteria to adapt to the external environment is fundamental for their survival. A halotolerant microorganism Enterococcus faecalis able to grow under high salt stress conditions was isolated in the present study. The SDS-PAGE analysis of the secretome showed a protein band with a molecular weight of 28 kDa, gradually increased with an increase in salt concentration, and the highest intensity was observed at 15% salt stress condition. LC-MS/MS analysis of this particular band identified fourteen different proteins, out of which nine proteins were uncharacterized. Further, the function of uncharacterized proteins was predicted based on structure-function relationship using a reverse template search approach deciphering uncharacterized protein into type III polyketide synthases, stress-induced protein-1, Eed-h3k79me3, ba42 protein, 3-methyladenine DNA glycosylase, Atxa protein, membrane-bound respiratory hydrogenase, type-i restriction-modification system methylation subunit and ManxA. STRING network analysis further a showed strong association among the proteins. The processes predicted involvement of these proteins in signal transduction, ions transport, synthesis of the protective layer, cellular homeostasis and regulation of gene expression and different metabolic pathways. Thus, the fourteen proteins identified in the secretome play an essential role in maintaining cellular homeostasis in E. faecalis under high-salinity stress. This may represent a novel and previously unreported strategy by E. faecalis to maintain their normal growth and physiology under high salinity conditions.

O-GlcNAc modification of leucyl-tRNA synthetase 1 integrates leucine and glucose availability to regulate mTORC1 and the metabolic fate of leucine

All living organisms have the ability to sense nutrient levels to coordinate cellular metabolism. Despite the importance of nutrient-sensing pathways that detect the levels of amino acids and glucose, how the availability of these two types of nutrients is integrated is unclear. Here, we show that glucose availability regulates the central nutrient effector mTORC1 through intracellular leucine sensor leucyl-tRNA synthetase 1 (LARS1). Glucose starvation results in O-GlcNAcylation of LARS1 on residue S1042. This modification inhibits the interaction of LARS1 with RagD GTPase and reduces the affinity of LARS1 for leucine by promoting phosphorylation of its leucine-binding site by the autophagy-activating kinase ULK1, decreasing mTORC1 activity. The lack of LARS1 O-GlcNAcylation constitutively activates mTORC1, supporting its ability to sense leucine, and deregulates protein synthesis and leucine catabolism under glucose starvation. This work demonstrates that LARS1 integrates leucine and glucose availability to regulate mTORC1 and the metabolic fate of leucine.

Leucyl-tRNA synthetase 1 (LARS1) is a leucine sensor for mTORC1 signaling and regulates leucine utilization depending on glucose availability. Here, the author show that O-GlcNAcylation of LARS1 is crucial for its ability to regulate mTORC1 activity and leucine metabolism upon glucose starvation.

Designing Anti-Microbial Peptides Against Major β-Lactamase Enzymes in Clinically Important Gram-Negative Bacterial Pathogens: An In-Silico Study

Anti-microbial resistance (AMR) creating healthcare concerns worldwide requires ardent exploration of therapeutic alternatives. Although anti-microbial peptides (AMP) are popular for broad-spectrum activity, recent evidence of increasing resistance to membrane-acting AMPs by ESKAPE pathogens has compelled us to design novel AMPs as therapeutic candidates. A library of 60 AMPs comprising natural AMPs and their mutants was constructed through in-silico methods. After physico-chemical property evaluations, each peptide in the library was subjected to flexible molecular docking against four major β-lactamases in Gram-negative ESKAPE pathogens. Among the potent AMP mutants, a Lactoferricin B-Mutant (M4) possessed uniformly high affinity with SHV1, OXA48, NDM1, and AmpC having energies -842.0Kcal/mol, -774.8Kcal/mol, -1103.3Kcal/mol, and -858.8Kcal/mol respectively. Coarse-grained clustering and flexibility analysis further accounted for the residue-level stable configurations of the protein-peptide complexes with high affinity. Highest affinity of Lactoferricin B_M4 was found with NDM1 due to H-bonds, salt-bridges, and hydrophobic interactions with the metallo-β-lactamase domain including crucial active-site residue Asp124. Molecular dynamics simulation further confirmed the stability of Lactoferricin B_M4-NDM1 complex having low residue-level root-mean square deviations (RMSD), atomic-level fluctuations, and radius of gyration (Rg). The study encourages experimental validations and similar methods to identify potential AMPs against drug-resistant pathogens.

An in silico hierarchal approach for drug candidate mining and validation of natural product inhibitors against pyrimidine biosynthesis enzyme in the antibiotic-resistant Shigella flexneri

Shigella flexneri is the main causative agent of the communicable diarrheal disease, shigellosis. It is estimated that about 80-165 million cases and > 1 million deaths occur every year due to this disease. S. flexneri causes dysentery mostly in young children, elderly and immunocompromised patients, all over the globe. Recently, due to the emergence of S. flexneri antibiotic resistance strains, it is a dire need to predict novel therapeutic drug targets in the bacterium and screen natural products against it, which could eliminate the curse of antibiotic resistance. Therefore, in current study, available antibiotic-resistant genomes (n = 179) of S. flexneri were downloaded from PATRIC database and a pan-genome and resistome analysis was conducted. Around 5059 genes made up the accessory, 2469 genes made up the core, and 1558 genes made up the unique genome fraction, with 44, 34, and 13 antibiotic-resistant genes in each fraction, respectively. Core genome fraction (27% of the pan-genome), which was common to all strains, was used for subtractive genomics and resulted in 384 non-homologous, and 85 druggable targets. Dihydroorotase was chosen for further analysis and docked with natural product libraries (Ayurvedic and Streptomycin compounds), while the control was orotic acid or vitamin B13 (which is a natural binder of this protein). Dynamics simulation of 50 ns was carried out to validate findings for top-scored inhibitors. The current study proposed dihydroorotase as a significant drug target in S. flexneri and 4-tritriacontanone & patupilone compounds as potent drugs against shigellosis. Further experiments are required to ascertain validity of our findings.

Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease

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GIN analysis revealed PARK2, LRRK2, PARK7, PINK1 and SNCA as hub-genes.

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Topologically favoured Parkin was considered as a therapeutic target.

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ADMET screening identified a novel UDCA derivative as potential lead candidate.

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Chemical reactivity and ligand stability were analysed through DFT simulation.

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Docking and MDS established novel lead as potential Parkin inhibitor.

Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.

In-Silico Vaccine Design Based on a Novel Vaccine Candidate Against Infections Caused by Acinetobacter baumannii

Acinetobacter baumannii is notorious for causing serious infections of the skin, lungs, soft tissues, bloodstream, and urinary tract. Despite the overwhelming information available so far, there has still been no approved vaccine in the market to prevent these infections. Therefore, this study focuses on developing a rational vaccine design using the technique of epitope mapping to curb the infections caused by A. baumannii. An outer membrane protein with immunogenic potential as well as all the properties of a good vaccine candidate was selected and used to calculate epitopes for selection on the basis of a low percentile rank, high binding scores, good immunological properties, and non-allergenicity. Thus, a 240 amino-acid vaccine sequence was obtained by manually joining all the epitopes in sequence-wise manner with the appropriate linkers, namely AAY, GPGPG, and EAAAK. Additionally, a 50S ribosomal protein L7/L12, agonist to the human innate immune receptors was attached to the N-terminus to increase the overall immune response towards the vaccine. As a result, enhanced overall protein stability, expression, immunostimulatory capabilities, and solubility of the designed construct were observed. Molecular dynamic simulations revealed the compactness and stability of the polypeptide construct. Moreover, molecular docking exhibited strong binding of the designed vaccine with TLR-4 and TLR-9. In-silico immune simulations indicated an immense increment in T-cell and B-cell populations. Bioinformatic tools also significantly assisted with optimizing codons which allowed for successful cloning of constructs into desired host vectors. Using in-silico tools to design a vaccine against A. baumannii demonstrated that this construct could pave the way for successfully combating infections caused by multidrug-resistant bacteria.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

Aerobactin Seems To Be a Promising Marker Compared With Unstable RmpA2 for the Identification of Hypervirulent Carbapenem-Resistant Klebsiella pneumoniae: In Silico and In Vitro Evidence

Background

The incidence of hypervirulent (hv) carbapenem-resistant (CR) Klebsiella pneumoniae (Kp) is increasing globally among various clones and is also responsible for nosocomial infections. The CR-hvKp is formed by the uptake of a virulence plasmid by endemic high-risk clones or by the uptake of plasmids carrying antimicrobial resistance genes by the virulent clones. Here, we describe CR-hvKp from India belonging to high-risk clones that have acquired a virulence plasmid and are phenotypically unidentified due to lack of hypermucoviscosity.

Methods

Twenty-seven CRKp isolates were identified to possess rmpA2 by whole-genome sequencing; and resistance and virulence determinants were characterized. By in silico protein modeling (and validation), protein backbone stability analysis, and coarse dynamics study, the fitness of RmpA, RmpA2, and aerobactin-associated proteins-IucA and IutA, were determined to establish a reliable marker for clinical identification of CR-hvKp.

Results

The CR-hvKp belonged to multidrug-resistant (MDR) high-risk clones such as CG11, CG43, ST15, and ST231 and carried OXA-232 as the predominant carbapenemase followed by NDM. The virulence plasmid belonged to IncHI1B replicon type and carried frameshifted and truncated rmpA and rmpA2. This resulted in a lack of hypermucoviscous phenotype. However, functional aerobactin was expressed in all high-risk clones. In silico analysis portrayed that IucA and IutA were more stable than classical RmpA. Furthermore, IucA and IutA had lower conformational fluctuations in the functional domains than the non-functional RmpA2, which increases the fitness cost of the latter for its maintenance and expression among CR-hvKp. Hence, RmpA and RmpA2 are likely to be lost among CR-hvKp owing to the increased fitness cost while coding for essential antimicrobial resistance and virulence factors.

Conclusion

Increasing incidence of convergence of AMR and virulence is observed among K. pneumoniae globally, which warrants the need for reliable markers for identifying CR-hvKp. The presence of non-functional RmpA2 among high-risk clones highlights the significance of molecular identification of CR-hvKp. The negative string test due to non-functional RmpA2 among CR-hvKp isolates challenges phenotypic screening and faster identification of this pathotype. This can potentially be counteracted by projecting aerobactin as a stable, constitutively expressed, and functional marker for rapidly evolving CR-hvKp.

MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics

The web server, MDM-TASK-web, combines the MD-TASK and MODE-TASK software suites, which are aimed at the coarse-grained analysis of static and all-atom MD-simulated proteins, using a variety of non-conventional approaches, such as dynamic residue network analysis, perturbation-response scanning, dynamic cross-correlation, essential dynamics and normal mode analysis. Altogether, these tools allow for the exploration of protein dynamics at various levels of detail, spanning single residue perturbations and weighted contact network representations, to global residue centrality measurements and the investigation of global protein motion. Typically, following molecular dynamic simulations designed to investigate intrinsic and extrinsic protein perturbations (for instance induced by allosteric and orthosteric ligands, protein binding, temperature, pH and mutations), this selection of tools can be used to further describe protein dynamics. This may lead to the discovery of key residues involved in biological processes, such as drug resistance. The server simplifies the set-up required for running these tools and visualizing their results. Several scripts from the tool suites were updated and new ones were also added and integrated with 2D/3D visualization via the web interface. An embedded work-flow, integrated documentation and visualization tools shorten the number of steps to follow, starting from calculations to result visualization. The Django-powered web server (available at https://mdmtaskweb.rubi.ru.ac.za/) is compatible with all major web browsers. All scripts implemented in the web platform are freely available at https://github.com/RUBi-ZA/MD-TASK/tree/mdm-task-web and https://github.com/RUBi-ZA/MODE-TASK/tree/mdm-task-web.

Homology modelling and analysis of structure predictions of human tumour necrosis factor ligand superfamily member 8

Background

Tumour necrosis factor ligand superfamily member 8 (TNFL8) is a cytokine that plays vital roles in immune activations and inflammatory responses through its interaction with the tumour necrosis factor superfamily member. Despite multiple studies on the involvement of its receptor in the inflammatory response, there is limited information on the molecular characterization and structural elucidation of the cytokine. Considering the significance of the cytokine, the three-dimensional structure of TNFL8 model was generated by homology modelling through the Iterative Threading ASSEmbly Refinement (I-TASSER) server and validated through PROCHECK and Qualitative Model Energy Analysis (QMEAN) servers.

Results

The predicted structure has 90.00% of residues in the most favoured region of the Ramachandran plot while the QMEAN value gives − 3.06. The sequence and structural alignment between the generated model of the cytokine and template (1XU2) reveal that similar active site residues such as ILE-142, THR-175, GLU-178, and PHE-228 could be involved in binding pocket formation. However, docking studies of the 3D model of TNFL8 with eight phytochemicals from the extract of Xylopia aethiopica (Dunal) A. Rich revealed the phytochemical bound to two different binding sites which could be the active regions of the cytokine that could be essential for inhibition. More so, the docking analysis showed most of the phytochemicals have good binding affinity to the cytokine with ellagic acid showing the highest affinity with a binding energy of − 6.58 ± 0.18 kcal/mol.

Conclusion

The proposed model may shed light on the mechanisms of TNFL8 binding and provide insights into the identification of potential molecular targets for the development of the novel compound for the regulation of the functional activities of the cytokine.

Discovery of potential multi-target-directed ligands by targeting host-specific SARS-CoV-2 structurally conserved main protease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the current COVID-19 pandemic. Worldwide this disease has infected over 2.5 million individuals with a mortality rate ranging from 5 to 10%. There are several efforts going on in the drug discovery to control the SARS-CoV-2 viral infection. The main protease (MPro) plays a critical role in viral replication and maturation, thus can serve as the primary drug target. To understand the structural evolution of MPro, we have performed phylogenetic and Sequence Similarity Network analysis, that depicted divergence of Coronaviridae MPro in five clusters specific to viral hosts. This clustering was corroborated with the comparison of MPro structures. Furthermore, it has been observed that backbone and binding site conformations are conserved despite variation in some of the residues. These attributes can be exploited to repurpose available viral protease inhibitors against SARS-CoV-2 MPro. In agreement with this, we performed screening of ∼7100 molecules including active ingredients present in the Ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 MPro as the primary target. We identified several natural molecules like δ-viniferin, myricitrin, taiwanhomoflavone A, lactucopicrin 15-oxalate, nympholide A, afzelin, biorobin, hesperidin and phyllaemblicin B that strongly binds to SARS-CoV-2 MPro. Intrestingly, these molecules also showed strong binding with other potential targets of SARS-CoV-2 infection like viral receptor human angiotensin-converting enzyme 2 (hACE-2) and RNA dependent RNA polymerase (RdRp). We anticipate that our approach for identification of multi-target-directed ligand will provide new avenues for drug discovery against SARS-CoV-2 infection.Communicated by Ramaswamy H. Sarma.

In silico structure evaluation of BAG3 and elucidating its association with bacterial infections through protein-protein and host-pathogen interaction analysis

BAG3, a co-chaperone protein with a Bcl-2-associated athanogene (BAG) domain, has diverse functionalities in protein-folding, apoptosis, inflammation, and cell cycle regulatory cross-talks. It has been well characterised in cardiac diseases, cancers, and viral pathogenesis. The multiple roles of BAG3 are attributed to its functional regions like BAG, Tryptophan-rich (WW), isoleucine-proline-valine-rich (IPV), and proline-rich (PXXP) domains. However, to study its structural impact on various functions, the experimental 3D structure of BAG3 protein was not available. Hence, the structure was predicted through in silico modelling and validated through computational tools and molecular dynamics simulation studies. To the best of our knowledge, the role of BAG3 in bacterial infections is not explicitly reported. We attempted to study them through an in-silico protein-protein interaction network and host-pathogen interaction analysis. From structure-function relationships, it was identified that the WW and PXXP domains were associated with cellular cytoskeleton rearrangement and adhesion-mediated response, which might be involved in BAG3-related intracellular bacterial proliferation. From functional enrichment analysis, Gene Ontology terms and topological matrices, 18 host proteins and 29 pathogen proteins were identified in the BAG3 interactome pertaining to Legionellosis, Tuberculosis, Salmonellosis, Shigellosis, and Pertussis through differential phosphorylation events associated with serine metabolism. Furthermore, it was evident that direct (MAPK8, MAPK14) and associated (MAPK1, HSPD1, NFKBIA, TLR2, RHOA) interactors of BAG3 could be considered as therapeutic markers to curb down intracellular bacterial propagation in humans.

Factors deciding the assembly and thermostability of the DmrB cage

Dihydromethanopterin reductase (DmrB), is a naturally occurring cage protein found in various archaeal and a few bacterial species. It exists as 24mer with cubic geometry where 8 trimeric subunits are present at the corners of each cube. Each trimer is made up of three monomeric units and six FMN, where two molecules of FMN are present at the interface of each monomer. DmrB is involved in the conversion of dihydromethanopterin to tetrahydromethanopterin using FMN as a redox equivalent. In the present study, we have used spectroscopic and biochemical techniques along with complementary bio-informatic work to understand the assembly principles of the DmrB. Our results show a concentration dependant self-assembly of DmrB which is mediated by ionic interactions. The co-factor FMN stabilizes and preserves the secondary and quaternary structure of DmrB against thermal insult, indicating that the higher order assembly of DmrB is very thermostable. Our work provides an interesting piece of information regarding the role of the co-factors in the thermostability of these classes of cage proteins. The understanding of the assembly and disassembly of this thermostable cage would enable the downstream usage of this system in various nano-biotechnological applications.

In silico exploration of lignin peroxidase for unraveling the degradation mechanism employing lignin model compounds

Lignin peroxidase is a heme-containing biocatalyst, well-known for its diverse applications in the fields from environmental chemistry to biotechnology. LiP-mediated oxidative catalysis is H₂O₂-dependent, and can oxidize phenolic, and non-phenolic substrates by oxidative cleavage of the C-C and C-O bonds of lignin. In contrast to fungi-derived LiP, the binding affinity of bacterial-derived LiP to lignin at the molecular level is poorly known to date. Tremendous wet-lab studies have been unveiled that provide degradation and biotransformation information on kraft lignin, whilst studies on the completely transformed compounds and the degradation of each transformed compounds simultaneously during degradation are scarce. To gain an understanding of the degradation process using docking, and MDS based studies, we assessed the binding affinity of selected lignin model compounds with bacterial origin LiP and validated such docked complexes exploiting 30 ns molecular dynamics simulations. We selected and picked a total of 12 lignin model compounds for molecular modeling analysis, namely two chlorinated lignin model compounds (monomer) (2-chlorosyringaldehyde and 5-chlorovanillin), eight standard lignin model compounds (veratryl alcohol, syringyl alcohol, sinapyl alcohol, methyl hydroquinone, guaiacol, coniferyl alcohol, catechol, and 4-methoxy phenol), while, two 4-O-5, and β-O-4 linkage-based multimeric model compounds (dimer: 2-methoxy-6-(2-methoxy-4-methylphenoxy)-4-methylphenol; trimer: syringyl β-O-4 syringyl β-O-4 sinapyl alcohol). Far more specific binding residues were observed from XP-Glide docking, as TYR, HIP (protonated histidine), PHE, VAL, ASP, THR, LYS and GLN. The binding affinity was confirmed by the Gibbs free energy or binding energy (ΔG) score; furthermore, it is found that the maximum binding energy seems to be observed for 4-methoxyphenol with a Glide score of -3.438 with Pi-Pi stacking and H-bond type bonding interactions, whilst the lowest XP Gscore as -8.136 with Pi-Pi stacking and H-bond (side chain) type bonding interactions were found for the trimer model compound. The docked complexes were further evaluated for deep rigorous structural and functional fluctuation analyses through high-performance molecular dynamics simulations-DESMOND, after a post simulation run of 30 ns. The RMSD trajectory analyses of the protein-ligands were found to be in the equilibrium state at the end of simulation run for multimeric lignin model compounds. In addition, ionic ligand-protein interaction occurs among chlorinated compounds, while hydrophobic and H-bond contacts have frequently been observed in all lignin-model compounds. The findings herein demonstrate that bacterial LiP can effectively catalyze multiple lignin model compounds, and it might further be used as an effective tool for sustainable mitigation of diverse environmental contaminants.

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of -780.6 and -641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC50 value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.

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.