Protein-ligand Docking Simulation Research Articles

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

Binding order of substrate and cofactor in sulfonamide monooxygenase during sulfa drug degradation: in silico studies

For decades, sulfonamide antibiotics have been used across industries such as agriculture and animal husbandry. However, the use and inadvertent misuse of these antibiotics have resulted in the advent of sulfonamide-drug-resistant strains due to antibiotic pollution. Enzymatic bioremediation of antibiotics remains a potential emerging solution to combat antibiotic pollution. Here, we propose an enzymatic model for the degradation of sulfonamides by Microbacterium sp. We have employed a multi-pronged computational strategy involving – protein structure modelling, ligand docking and molecular dynamics simulations to decipher a plausible binding order for the enzymatic degradation of sulfonamides by the bacterial sulfonamide monooxygenase, SulX. Our results enable us to predict that this degradation is achieved through the sequential binding of the antibiotic sulfonamide followed by the reduced flavin cofactor FMNH2, thereby laying the computational foundation for further advancements in enzyme-mediated degradation of the antibiotic. We also provide a list of experiments which may be performed to verify and follow-up on our in-silico studies. Communicated by Ramaswamy H. Sarma

Structural insights into hydrolytic defluorination of difluoroacetate by microbial fluoroacetate dehalogenases.

Fluorine forms the strongest single bond to carbon with the highest bond dissociation energy among natural products. However, fluoroacetate dehalogenases (FADs) have been shown to hydrolyze this bond in fluoroacetate under mild reaction conditions. Furthermore, two recent studies demonstrated that the FAD RPA1163 from Rhodopseudomonas palustris can also accept bulkier substrates. In this study, we explored the substrate promiscuity of microbial FADs and their ability to defluorinate polyfluorinated organic acids. Enzymatic screening of eight purified dehalogenases with reported fluoroacetate defluorination activity revealed significant hydrolytic activity against difluoroacetate in three proteins. Product analysis using liquid chromatography-mass spectrometry identified glyoxylic acid as the final product of enzymatic DFA defluorination. The crystal structures of DAR3835 from Dechloromonas aromatica and NOS0089 from Nostoc sp. were determined in the apo-state along with the DAR3835 H274N glycolyl intermediate. Structure-based site-directed mutagenesis of DAR3835 demonstrated a key role for the catalytic triad and other active site residues in the defluorination of both fluoroacetate and difluoroacetate. Computational analysis of the dimer structures of DAR3835, NOS0089, and RPA1163 indicated the presence of one substrate access tunnel in each protomer. Moreover, protein-ligand docking simulations suggested similar catalytic mechanisms for the defluorination of both fluoroacetate and difluoroacetate, with difluoroacetate being defluorinated via two consecutive defluorination reactions producing glyoxylate as the final product. Thus, our findings provide molecular insights into substrate promiscuity and catalytic mechanism of FADs, which are promising biocatalysts for applications in synthetic chemistry and bioremediation of fluorochemicals.

AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation.

We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.

Computational studies on the interaction of Omicron subvariants (BA.1, BA.2, and BA.3) with ACE2 and polyphenols.

The SARS-CoV-2 Omicron variant BA.2 is spreading widely across the globe. The World Health Organization (WHO) designated BA.2 as a variant of concern due to its high transmission rate and pathogenicity. To elucidate the structural changes caused by mutations, we conducted a comparative analysis of BA.2 with variants BA.1 and BA.3. In the present study, we aimed to investigate the interactions of the spike glycoprotein receptor-binding domain (SGp RBD) of Omicron variants BA.1, BA.2, and BA.3 with the human receptor hACE2. Further, a library of 233 polyphenols was screened by molecular docking with the SGp RBDs of Omicron variants BA.1, BA.2, and BA.3. Protein-protein and protein-ligand molecular docking simulations were performed with AutoDock Vina and the ClusPro 2.0 server, respectively. The protein-ligand interactions were evaluated by BIOVIA Discovery Studio and ChimeraX 1.4. The molecular dynamics simulations for 100 ns were performed using GROMACS 2021. Compared to other variants of concern, the structural changes in Omicron caused by mutations at key positions improved its ability to cause infection. Despite multiple mutations, many important polyphenols bind effectively at the RBDs of Omicron variants, with the required pharmacokinetic and ADME features and obeying the Lipinski rule. Even though Omicron variants have multiple mutations and their transmission rate is relatively high, the computed binding affinities of lead polyphenols like epigallocatechin-3-O-gallate (EGCG) and luteolin-7-O-glucuronide (L7G) indicate that traditional medicines and proper immunity booster diets may be useful in the long-term fight against SARS-CoV-2.

Omicron SARS-CoV-2 Spike-1 Protein’s Decreased Binding Affinity to α7nAChr: Implications for Autonomic Dysregulation of the Parasympathetic Nervous System and the Cholinergic Anti-Inflammatory Pathway—An In Silico Analysis

Omicron is the dominant strain of COVID-19 in the United States and worldwide. Although this variant is highly transmissible and may evade natural immunity, vaccines, and therapeutic antibodies, preclinical results in animal models and clinical data in humans suggest omicron causes a less severe form of infection. The molecular basis for the attenuation of virulence when compared to previous variants is currently not well understood. Using protein–ligand docking simulations to evaluate and compare the capacity of SARS-CoV-2 spike-1 proteins with the different COVID-19 variants to bind to the human α7nAChr (i.e., the core receptor under the control of the vagus nerve regulating the parasympathetic nervous system and the cholinergic anti-inflammatory pathway), we found that 10 out of the 14 mutated residues on the RBD of the B.1.1.529 (Omicron) spike, compared to between 0 and 2 in all previous variants, were present at the interaction interface of the α7nAChr. We also demonstrated, through protein–protein docking simulations, that these genetic alterations cause a dramatic decrease in the ability of the Omicron SARS-CoV-2 spike-1 protein to bind to the α7nAChr. These results suggest, for the first time, that the attenuated nature of Omicron infection in humans and animals compared to previous variants may be attributable to a particular set of genetic alterations, specifically affecting the binding site of the SARS-CoV-2 spike-1 protein to the α7nAChr.

Critical contributions of pre-S1 shoulder and distal TRP box in DAG-activated TRPC6 channel by PIP2 regulation

Phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2 or PIP2) regulates the activities of numerous membrane proteins, including diacylglycerol(DAG)-activated TRPC3/6/7 channels. Although PIP2 binding is known to support DAG-activated TRP channel activity, its binding site remains unknown. We screened for PIP2 binding sites within TRPC6 channels through extensive mutagenesis. Using voltage-sensitive phosphatase (DrVSP), we found that Arg437 and Lys442, located in the channel’s pre-S1 domain/shoulder, are crucial for interaction with PIP2. To gain structural insights, we conducted computer protein–ligand docking simulations with the pre-S1 domain/shoulder of TRPC6 channels. Further, the functional significance of PIP2 binding to the pre-S1 shoulder was assessed for receptor-operated channel functions, cross-reactivity to DAG activation, and the kinetic model simulation. These results revealed that basic residues in the pre-S1 domain/shoulder play a central role in the regulation of PIP2-dependent gating. In addition, neutralizing mutation of K771 in the distal TRP box reversed the effect of PIP2 depletion from inhibiting to potentiating channel activity. A similar effect was seen in TRPV1 channels, which suggests that TRPC6 possesses a common but robust polarity switch mediating the PIP2-dependent effect. Overall, these mutagenesis studies reveal functional and structural insights for how basic residues and channel segments in TRP channels are controlled through phosphoinositides recognition.

Maclurin Exhibits Antioxidant and Anti-Tyrosinase Activities, Suppressing Melanogenesis.

Maclurin is rich in some edible fruits such as Morus alba (white mulberry) and Garcinia mangostana. Although maclurin showed anti-cancer and antioxidant effects, its roles in ultraviolet (UV)-induced melanogenesis have not been studied. Here, we investigated the effects of maclurin in melanogenesis using skin cells and a three-dimensional human skin model. When the cytotoxicity of maclurin was examined in B16F10 cells, no cytotoxicity was found up to 20 μM. Maclurin suppressed UVB-mediated tyrosinase activation and melanin accumulation in B16F10 cells without changes in mRNA levels of melanogenesis-related genes including tyrosinase, TRP1, TRP2, CREB, and MITF. Moreover, maclurin reduced melanin contents in melan-a cells, a cell line for normal melanocytes. When applied to a human skin model consisting of the epidermis and melanocytes, maclurin significantly reduced UVB-induced melanin accumulation (~47%) in a concentration-dependent manner based on microscopic observation and Fontana-Masson staining. Protein–ligand docking simulation followed by binding residue analysis showed that maclurin may bind to inactivate tyrosinase by forming multiple hydrogen bonds and hydrophobic and aromatic interactions with the residues of tyrosinase. Together, our study suggests that maclurin may be applied as an anti-melanogenic agent.

Novel perspective into the interaction behavior study of the cyanidin with human serum albumin-holo transferrin complex: Spectroscopic, calorimetric and molecular modeling approaches

We examined the inducement of an interaction between two carrier proteins, human serum albumin (HSA) and human holo transferrin (HTF) within the presence of cyanidinin the form of binary and ternary systems, which was conducted by different spectroscopic, isothermal titration calorimetric (ITC), and molecular dynamics simulation techniques. The results of fluorescence spectroscopy verified the occurrence of this interaction by displaying the regular quenching of emission spectra of proteins as single and complex forms by cyanidin. Moreover, fluorescence emission suggested that, HSA-cyanidin, HTF-cyanidin and (HSA-HTF) cyanidin complexes follows an static mechanism with the binding constants of 8.81 × 104, 3.40 × 104, and 7.10 × 104 M−1, respectively, which were strongly reflected to moderate the affinity of cyanidin to both single and complex forms of proteins. The measured thermodynamic parameters obtained by ITC, determined the necessity of electrostatic forces for binding of cyanidin to HSA and HTF. Besides, obtaining a negative ΔG0 is indicative of spontaneous mode of binding process in both conditions. The different values of thermodynamic parameters of cyanidin binding to the forms of HSA, HTF and HSA-HTF complex clearly proved the similarity in the type of interaction forces between cyanidin and proteins as single and complex forms with various values. The observed changes in synchronous fluorescence results were related to the micro-environment properties of Tyr and Trp residues. The induced alternations in the secondary construction of both proteins were confirmed through the outcomes of quantitative analysis performed by circular dichroism spectroscopy (CD) techniques. FRET theory was exerted to calculate, the existing distance across the donor and acceptor, which were reported to be 1.85 nm, 1.97 nm, and 1.91 nm for the cases of HSA-cyanidin, HTF-cyanidin and (HSA-HTF) cyanidin complexes, respectively. Molecular displacement and protein–ligand docking simulations confirmed the binding of cyanidin to HSA and HTF through the binary and ternary systems.

Investigation on substrate specificity and catalytic activity of serine protease neuropsin.

Neuropsin is one of serine proteases mainly found at the hippocampus and the amygdala, where it contributes to the long-term potentiation and memory acquisition by rebuilding of synaptic connections. Despite of the importance of neuropsin, the substrate specificity and regulation mechanisms of neuropsin have been unclear. Thus, we investigated the substrate specificity and the catalytic activity of neuropsin by the protein-ligand docking and molecular dynamics (MD) simulations and succeeded to reproduce the trend of the experimental results. Our study revealed that the substrate specificity and the activity of neuropsin depended on multiple factors: the substrate charge, the substrate orientation, the hydrogen bond network within the catalytic triad and the substrate, and the formation of the oxyanion hole. The apo neuropsin was not reactive without proper alignment of catalytic triad. The substrate binding induced the reactive alignment of catalytic triad. Then the substrate-neuropsin interaction forms the oxyanion hole that stabilizes the transition state and reduces the free-energy barrier of the following scission reaction.

The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.

One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these techniques following two approaches. Firstly, using the experimental structures for which affinity data is available. Secondly, using protein-ligand docking simulations. In this review, we describe recently published machine learning models based on crystal structures, for which binding affinity and thermodynamic data are available. We used experimental structures available at the protein data bank and binding affinity and thermodynamic data was accessed through BindingDB, Binding MOAD, and PDBbind databases. We reviewed machine learning models to predict binding created using open source programs, such as SAnDReS and Taba. Analysis of machine learning models trained against datasets, composed of crystal structure complexes indicated the high predictive performance of these models when compared with classical scoring functions. The rapid increase in the number of crystal structures of protein-ligand complexes created a favorable scenario for developing machine learning models to predict binding affinity. These models rely on experimental data from two sources, the structural and the affinity data. The combination of experimental data generates computational models that outperform the classical scoring functions.

Natural Compounds Activities against SARS-CoV-2 Mpro through Bioinformatics Approaches for Development of Antivirus Candidates

Coronavirus infection (COVID-19) caused by SARS-CoV-2 appears as a pandemic that has spread to almost all countries in the world. Antiviral therapy using natural compounds is one alternative approach to overcome this infectious disease. The therapeutic mechanism is proven effective against the main protease (Mpro) of SARS-CoV-2. This research aims to perform bioinformatics studies, including ligand-docking simulations and protein-protein docking simulations, to identify, evaluate, and explore five compounds' activity on SARS-CoV-2 Mpro and their effects against Angiotensin-Converting Enzyme 2 (ACE-2). Protein-ligand docking simulations show kaempferol, flavonol, and their glycosides (Afzelin and Juglanin) and other flavonoids (Quercetin, Naringenin, and Genistein) have a high affinity towards SARS-CoV-2 Mpro. These results were then confirmed using protein-protein docking simulations to observe the ability of five compounds to prevent the attachment of ACE-2 to the active site. Based on the results of the bioinformatics studies, Quercetin has the best affinity, with a binding free energy value of −33.18 kJ/mol. The five compounds are predicted to be able to interact strongly with SARS-CoV-2. The results in this research are useful for further studies in the development of novel anti-infective drugs for COVID-19 that target SARS-CoV-2 Mpro.

Cloning and expression analysis of fused holin-endolysin from RL bacteriophage; Exhibits broad activity against multi drug resistant pathogens

Antibiotic resistance has become a major risk to community health over last few years because of antibiotics overuse around the globe and lack of new antibiotics development. Phages and their lytic enzymes are considered as an effective alternative of antibiotics to control drug resistant bacterial pathogens. Endolysins prove to be a promising class of antibacterials due to their specificity and less chances of resistance development in bacterial pathogens. Though large number of endolysins has been reported against gram positive bacteria, very few reported against gram negative bacteria due to the presence of outer membrane, which acts as physical barrier against endolysin attack to peptidoglycan. In the current study, we have expressed endolysin (RL_Lys) and holin fused at the N terminus of endolysin (RL_Hlys) from RL phage infecting multi drug resistant (MDR) Pseudomonas aeruginosa. Both endolysin variants were found active against wide range of MDR strains P. aeruginosa, Klebsella pneumonia, Salmonella Sp. and Methicillin Resistant Staphylococcus aureus (MRSA). Broth reduction assay showed that RL_Hlys is more active than RL_Lys due to presence of holin, which assist the endolysin access towards cell wall. The protein ligand docking and molecular dynamic simulation results showed that C- terminus region of endolysin play vital role in cell wall binding and even in the absence of holin, hydrolyze a broad range of gram negative bacterial pathogens. The significant activity of RL-Lys and RL_Hlys against a broad range of MDR gram negative and positive bacterial pathogens makes them good candidates for antibiotic alternatives.

Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2

The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

In silico characterization of coding and non-coding SNPs of the androgen receptor gene

The Androgen Receptor (AR), an important ligand-dependent nuclear transcription factor, encoded by the AR gene, functions in regulating the cell proliferation, migration, invasion, and apoptosis pathways. Single nucleotide polymorphisms (SNPs) of AR have been implicated in playing a role in several diseases. This study sought to explore the molecular consequences of the disease susceptible germ-line missense and non-coding SNPs of AR gene, by using different computational methods. 586 Missense SNPs were retrieved from dbSNP, DisGeNET, ClinVar, and LitVar databases, and then analyzed using nine different functionality prediction servers such as SIFT, PolyPhen-2, PROVEAN, PhD-SNP, etc. These sieved the most deleterious SNPs, which was then followed by the analysis of conservation, surface exposure, post-translational modification, stability, secondary structure, protein-ligand docking and molecular dynamics simulation. Among the missense SNPs, 21 SNPs were identified as most deleterious by all nine filtration tools. These high-risk disease-causing mutations mostly belong to highly conserved regions (buried or exposed) and affect protein stability and post-translational modifications. Based on the phenotypic effect predicted by ModPred and HOPE, and integrating the result with modeled mutated structures, two variants S866P and P893Q were considered to have the most severe functional consequences on protein 3D structure. Furthermore, based on the structural variations in mutant structure modeling, 98 noncoding pathogenic SNPs were identified as having an effect on the transcription factors’ binding and gene expression regulation. In contrast, 39 AR-SNPs were investigated using the PolymiRTS database. It was found that they affect miRNA target sites as well as destroying or creating miRNA target seeds. Significantly, these SNPs, distributed in random coil, alpha helix, extended sheet, and beta-turn, are most likely to affect the phenotypic characteristics of AR by altering hydrophobicity, flexibility, charges, H-bonds, salt bridges, and spatial structure. This current study is a comprehensive analysis, using an integrated in-silico approach on AR gene variants. It may provide a platform to conduct large-scale experiments involving AR polymorphism.

Inhibition of Programmed Death Receptor-1/Programmed Death Ligand-1 Interactions by Ginsenoside Metabolites.

Evidence suggests that programmed death receptor-1/programmed death ligand-1 (PD-1/PD-L1) targeted inhibitors act as an immune checkpoint blockade, indicating that these compounds may be useful in cancer immunotherapy by inhibiting the immune response between T-cells and tumors. Previous studies have shown that ginsenosides can regulate the expression of PD-1 and PD-L1 in target diseases; however, it remains unknown whether ginsenosides act as a blockade of PD-1/PD-L1 interactions. In this study, we used competitive ELISA to investigate 12 ginsenosides for their ability to block PD-1/PD-L1 interactions. In addition, we performed a protein–ligand docking simulation and examined the hydrophobic interactions and hydrogen bonds formed at the interfaces between the ginsenosides and PD-L1/PD-1. Eight out of the 12 ginsenosides studied showed inhibition of PD-1/PD-L1 interactions at 35% at the maximum concentration (1 μM). Among them, Rg3 and Compound K (C-K) demonstrated the highest inhibitory effects. Rg3 and C-K were further identified for their interaction efficacy with PD-1/PD-L1, which supported our results demonstrating the blocking activity of these compounds against PD-1/PD-L1 binding interactions. Collectively, our findings suggest that some ginsenosides, including Rg3 and C-K, inhibit PD-1/PD-L1 binding interactions. Therefore, these compounds may prove useful as part of an overall immuno-oncological strategy.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation.

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Characterization of a thermostable, allosteric L-asparaginase from Anoxybacillus flavithermus

L-asparaginase is an important enzyme with diverse applications in food industry and therapeutics. However, the enzyme currently employed in the treatment of leukaemias, comes with undesired L-glutaminase activity. A gene encoding 38 kDa L-asparaginase form Anoxybacillus flavithermus was cloned and expressed in Escherichia coli as a soluble and active enzyme. Heat treatment and Ni-affinity column chromatography provided highly purified enzyme possessing a specific activity of 165 units mg−1. The enzyme exhibited allosteric behaviour with a Hill coefficient of 1.60 and K0.5 of 25 mM with L-asparagine as specific substrate. No detectable activity was observed in the presence of D-asparagine, l-glutamine and d-glutamine. Purified AfASNase showed optimum activity at 60 °C and pH 7.0. The enzyme had ability to withstand up to 6 M urea and showed complete inactivation when treated with 1 M guanidine hydrochloride. Protein-Ligand docking and molecular dynamic simulations indicated that the regulatory site is formed by T262-T263-C265-G269-Thr294 and is located on a domain different from the one carrying the well-established active site. AfASNase is reported as first thermostable L-asparaginase with allosteric regulation. Hitherto, AfASNase presents the first characterization of recombinant L-asparaginase from the genus Anoxybacillus.

Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene.

Single Nucleotide Polymorphisms (SNPs) are the most common candidate mutations in human beings that play a vital role in the genetic basis of certain diseases. Previous studies revealed that Solute Carrier Family 26 Member 4 (SLC26A4) being an essential gene of the multi-faceted transporter family SLC26 facilitates reflexive movement of Iodide into follicular lumen through apical membrane of thyrocyte. SLC26A4 gene encodes Pendred protein, a membrane glycoprotein, highly hydrophobic in nature, present at the apical membrane of thyrocyte functioning as transporter of iodide for thyroid cells. A minor genetic variation in SLC26A4 can cause Pendred syndrome, a syndrome associated with thyroid glands and deafness. In this study, we performed in-silico analysis of 674 missense SNPs of SLC26A4 using different computational platforms. The bunch of tools including SNPNEXUS, SNAP-2, PhD-SNP, SNPs&GO, I-Mutant, ConSurf, and ModPred were used to predict 23 highly confident damaging and disease causing nsSNPs (G209V, G197R, L458P, S427P, Q101P, W472R, N392Y, V359E, R409C, Q235R, R409P, G139V, G497S, H723R, D87G, Y127H, F667C, G334A, G95R, S427C, R291W, Q383H and E384G) that could potentially alter the SLC26A4 gene. Moreover, protein structure prediction, protein-ligand docking and Molecular Dynamics simulation were performed to confirm the impact of two evident alterations (Y127H and G334A) on the protein structure and function.

Hesperidin improves insulin resistance via down-regulation of inflammatory responses: Biochemical analysis and in silico validation.

Leptin resistance and co-existing insulin resistance is considered as hallmark of diet-induced obesity. Here, we investigated therapeutic potential of hesperidin to improve leptin and insulin resistance using high fat diet (HFD)-induced obese experimental animal model. We also performed in silico studies to validate therapeutic effectiveness of hesperidin by performing protein-ligand docking and molecular dynamics simulation studies. Group 1 was identified as control group receiving vehicle only. Group 2 was marked as non-treated group receiving 60% HFD. While, other groups were treated daily with orlistat (120 mg/kg/d), hesperidin (55 mg/kg/d), combination of hesperidin (55 mg/kg/d) + orlistat (120 mg/kg/d). Hesperidin alone (P<0.001) and particularly in combination with orlistat (P<0.001), resulted in controlling the levels of HFD-altered biomarkers including random and fasting state of glycemia, leptin and insulin resistance. Similarly, hesperidin also improved the serum and tissue levels of leptin, interleukin-6 and tumor necrosis factor-alpha more significantly (P<0.05) when compared with that of orlistat. These results were found to be in accordance with the results of histopathological examination of pancreas, liver and adipose tissues. In-silico studies also proved that hesperidin binds to leptin receptor with higher affinity as compared to that of orlistat and induces the favorable variations in geometrical conformation of leptin receptor to promote its association with leptin which may lead to the cascades of reactions culminating the lipolysis of fats that may ultimately lead to cure obesity. The results of this study may be a significant expectation among the forthcoming treatment strategies for leptin and insulin resistance.