SAVES Server Research Articles

Computational analysis to comprehend the structure-function properties of fibrinolytic enzymes from Bacillus spp for their efficient integration into industrial applications

BackgroundThe fibrinolytic enzymes from Bacillus sp. are proposed as therapeutics in preventing thrombosis. Computational-based analyses of these enzymes’ amino acid composition, basic physiological properties, presence of functional domain and motifs, and secondary and tertiary structure analyses can lead to developing a specific enzyme with improved catalytic activity and other properties that may increase their therapeutic potential. MethodsThe nucleotide sequences of fibrinolytic enzymes produced by the genus Bacillus and its corresponding protein sequences were retrieved from the NCBI database and aligned using the PRALINE programme. The varied physiochemical parameters and structural and functional analysis of the enzyme sequences were carried out with the ExPASy-ProtParam tool, MEME server, SOPMA, PDBsum tool, CYS-REC tool, SWISS-MODEL, SAVES servers, TMHMM program, GlobPlot, and peptide cutter software. The assessed in-silico data were compared with the published experimental results for validation. ResultsThe alignment of sixty fibrinolytic serine protease enzymes (molecular mass 12–86 kDa) sequences showed 49 enzymes possess a conserved domain with a catalytic triad of Asp196, His242, and Ser569. The predicted instability and aliphatic indexes were 1.94–37.77, and 68.9–93.41, respectively, indicating high thermostability. The random coil means value suggested the predominance of this secondary structure in these proteases. A set of 50 amino acid residues representing motif 3 signifies the Peptidase S8/S53 domain that was invariably observed in 56 sequences. Additionally, 28 sequences have transmembrane helices, with two having the most disordered areas, and they pose 25 enzyme cleavage sites. A comparative analysis of the experimental work with the results of in-silico study put forward the characteristics of the enzyme sequences JF739176.1 and MF677779.1 to be considered when creating a potential mutant enzyme as these sequences are stable at high pH with thermostability and to exhibit αβ-fibrinogenase activity in both experimental and in-silico studies.

Computational Analysis and NGS for Human LDHC Complexed with NAD+ and Ethylamino Acetic Acid (Lung Cancer)

Abstract: This study presents a multidimensional exploration of the structural and functional characteristics of the human Lactate Dehydrogenase C (LDHC) complexed with NAD+ and ethylamino acetic acid (EAA), focusing on its implications in lung cancer. Leveraging data from the Protein Data Bank (PDB) and Molecular Modeling Database (MMDB), a comprehensive analysis was conducted using various computational tools and resources. Structural insights were gained through PDBsum and RasMol, elucidating molecular interactions and identifying potential drug-target sites. Sequence analysis via BLAST and Multiple Sequence Alignment provided further understanding of conserved sequences and structural motifs. Visualization and manipulation of molecular structures were facilitated by PyMOL, enabling the identification of active drug-target sites. Proteinligand docking studies using the CB-Dock2 server assessed the efficiency of LDHC complexation with NAD+. Structural validation through the SAVES server ensured the reliability of the complex structure. Additionally, biological network analysis using the STRING database revealed associations with gene ontology and structural classification of the protein, offering insights into targeted interventions in the ancestry of the protein. This comprehensive approach provides valuable insights into LDHC's role in cancer biology and offers promising avenues for therapeutic development in lung cancer.

Statistical optimized production of Phytase from Hanseniaspora guilliermondii S1 and studies on purification, homology modelling and growth promotion effect

This investigation was performed to obtain a promising phytase enzyme producing yeast. In this regard, the PSM was used to isolate the phytase-producing Hanseniaspora guilliermondii S1 (MG663578) from sugarcane juice. The SSF optimum conditions for phytase generation were optimized using (OVAT) one-variable-at-a-time strategy using both Box-Behnken design and shake flask method (g/100 ml: 0.05 yeast extract, 0.15 Peptone, 0.05 malt extract 0.50 dextrose, pH 5.8 and 28ᵒC). The protein model developed was shown to be adequate for phytase production (91% accuracy), with the greatest phytase productivity in shake flask with substrate jack fruit seed powder being 395 ± 0.43 U/ml compared to 365U/ml for the BBD projected value. Crude Phytase was partially purified with a protein recovery of 43%, revealing a molecular weight of 120 kDa. It had an enzyme kinetic value of Km 3.3 mM and a Vmax of 19.1 mol/min. The 3D structure of PhyS1 amino acid sequences (PhyS1. B99990002) was simulated using Modeler 9.23, and the validated result revealed that 86.7% were in the favored region by Ramachandran plot. The SAVES server verified the 3D PDB file as satisfactory, and the model (in.pdb format) was uploaded in the PMDB database with the accession number ID: PM0082974. At the lab level, Hanseniaspora guilliermondii S1 (MG663578) producing phytase exhibited successful plant growth promotion activity in Ragi - CO 19 (Eleusine coracana L.) and Rice -Navarai - IR 64 (Oryza sativa L.). As a result, a phytase-based formulation for sustainable agriculture must be developed and tested on a large scale in diverse geographical areas of agricultural lands to determine its effect and potential on plant development.

Next Generation Sequencing and Insilico Identification of Human Anaplastic Lymphoma Kinase in Human Cancer with Biopython

Abstract: In this comprehensive molecular investigation, the focus centers on the anaplastic lymphoma kinase (ALK) within the context of lung cancer. Specifically, utilizing the sample with the PDB code 3AOX, the study employs a multifaceted approach combining advanced bioinformatics tools and structural biology methodologies.Commencing with RasMol, the analysis of the protein sample reveals a complex network of 196 hydrogen bonds within the ALK structure. This foundational information sets the stage for subsequent investigations. Visual representation is meticulously addressed, with RasMol utilized for color-coding alpha helices in magenta, beta helices in yellow, and the remaining residues in white. The presentation mode emphasizes helices in red, sheets in yellow, and loops in green, providing a vivid depiction of the structural elements within the ALK protein.Moving beyond visualization, PyMOL is engaged to represent the surface form, providing insights into the interactions between ALK and its environment. This is particularly crucial in understanding the structural dynamics of ALK in the context of lung cancer, where the protein sample serves as a representative model.Active site identification in Chain A is pursued using tools such as PyMol, offering a glimpse into potential functional regions crucial for ALK's role in lung cancer pathogenesis. The study extends to biophysical aspects, incorporating protein-ligand docking studies with Mektovi and Almita through CB DOCK. This approach sheds light on the intricate molecular interactions between ALK and specific ligands, providing valuable information for targeted therapies.Structural validation becomes paramount in ensuring the reliability of the 3AOX sample. The ERRAT structure validation server evaluates the overall quality factor, while Procheck on the SAVES server scrutinizes the Ramachandran plot, ensuring the conformational consistency of ALK's backbone dihedral angles.Biopython scripts play a pivotal role in extracting and analyzing data related to the alpha-beta class representation within the CATH database. This bioinformatics analysis adds another layer of understanding to ALK's structural classification, offering insights into its role in lung cancer.

Molecular phylogeny, Sequence-based drug design, Docking built virtual screening, dynamics simulations, and ADMET properties of thiazolino 2-pyridone amide derivatives as an inhibitor of Chlamydia trachomatis and SARS-CoV-2 protein

The propagation of emerging diseases and the expensive cost and time lost by using the classic methods, especially in the current scenario with the world being plagued by SARS-CoV-2 and Chlamydia trachomatis diseases, make finding another way to invent new medication very important. That's why we used computational approaches to predict protein-ligand interactions of thiazolino 2-pyridone amide derivatives. The high-throughput virtual screening requires extensive combing through existing datasets in the hope of finding possible matches to screen for new molecules able to inhibit SARS-CoV-2 and Chlamydia trachomatis diseases. In this study, 46 thiazolino-2-pyridone amide derivatives were chosen for planning the powerful inhibitors by utilizing various strategies: QSAR analysis, phylogenetic analysis, homology modeling, docking simulation, molecular dynamics (MD) simulation, as well as ADMET Screening. The 2D QSAR investigation uncovers that these compounds show a satisfactory connection with bioactivity. From that point onward, phylogenetic analysis and homology modeling were used to model the selected receptors, which were then evaluated using both the SAVES and PROSA servers, indicating the best correctness of the modeled protein with the experimental results. Additionally, a docking simulation investigation was carried out to comprehend the 46 thiazolino-2-pyridone amide derivatives' interactions with homologous proteins. Additionally, MD simulations coupled with MM/GBSA verified the chosen complex systems' stability over 1000 ps. Two compounds were chosen as possible inhibitors based on these findings. The expected thiazolino-2-pyridone amide's oral bioavailability and toxicity have been discovered under the ADMET. Thus, these discoveries can be leveraged to develop novel molecules with the necessary action.

Identification of Disalicyloyl Curcumin as a Potential DNA Polymerase Inhibitor for Marek's Disease Herpesvirus: A Computational Study Using Virtual Screening and Molecular Dynamics Simulations.

Marek's disease virus (MDV) is a highly contagious and persistent virus that causes T-lymphoma in chickens, posing a significant threat to the poultry industry despite the availability of vaccines. The emergence of new virulent strains has further intensified the challenge of designing effective antiviral drugs for MDV. In this study, our main objective was to identify novel antiviral phytochemicals through in silico analysis. We employed Alphafold to construct a three-dimensional (3D) structure of the MDV DNA polymerase, a crucial enzyme involved in viral replication. To ensure the accuracy of the structural model, we validated it using tools available at the SAVES server. Subsequently, a diverse dataset containing thousands of compounds, primarily derived from plant sources, was subjected to molecular docking with the MDV DNA polymerase model, utilizing AutoDock software V 4.2. Through comprehensive analysis of the docking results, we identified Disalicyloyl curcumin as a promising drug candidate that exhibited remarkable binding affinity, with a minimum energy of -12.66 Kcal/mol, specifically targeting the DNA polymerase enzyme. To further assess its potential, we performed molecular dynamics simulations, which confirmed the stability of Disalicyloyl curcumin within the MDV system. Experimental validation of its inhibitory activity in vitro can provide substantial support for its effectiveness. The outcomes of our study hold significant implications for the poultry industry, as the discovery of efficient antiviral phytochemicals against MDV could substantially mitigate the economic losses associated with this devastating disease.

Investigation of SLC6A9 and SLC5A1 as a promising therapeutic target for obesity and diabetes using in silico characterization, 3D structure prediction and molecular docking analysis

AbstractBackgroundObesity is a multi‐factorial clinical condition resulting from energy dyshomeostasis. Obesity is known to be linked with the development of Type‐1 and Type‐2 diabetes due to abnormal glucose regulation. Additionally, it is associated with the onset of insulin resistance as well as atypical nutrient and hormonal sensing in the CNS. The role of GLYT1 has been implicated in the Dorsal Vagal Complex (DVC) which forms the brain‐liver axis for glucose release into the bloodstream. On the other hand, SGLT1 is majorly responsible for glucose absorption in the intestinal lumen.MethodThe current work focuses on in silico approaches to characterize the transporter proteins: Glycine Transporter‐1 (GLYT1) and Sodium‐Glucose Linked Transporter‐1 (SGLT1) as a potential target for the treatment of obesity and diabetes in an insulin independent manner. The sequence of the two proteins were obtained from the UniProt database which was used to calculate various physicochemical parameters giving detailed insight into its molecular weight, amino acid composition, theoretical PI, extinction coefficients and GRAVY index.ResultPrimary sequence analysis using ProtParam revealed the stability of SLC6A9 and SLC5A1 in test‐tube classifying it as stable and unstable respectively while secondary structure analysis using SOPMA shed light on its alpha helix and beta sheet composition as well as random coils. Three‐dimensional (3D) protein structures were generated using SWISS‐MODEL, an integrated web‐based homology modeling server, which were then verified using the SAVES server. Docking studies were performed using AutoDock4 which yielded potential drug candidates. The validated models of SLC6A9 and SLC5A1 were successfully submitted to PMDB ‐Protein Model Database with PMDB ID: PM0082326 and PM0082333 respectively which could be utilized for further Docking analysis and spectroscopy experiments.ConclusionA thorough explication of these results can aid the future research in development of a novel therapeutic strategy to battle this enigmatic rising epidemic of obesity and diabetes.

Recurrence mutation in RBBP8 gene causing non-syndromic autosomal recessive primary microcephaly; geometric simulation approach for insight into predicted computational models.

Mapping of RBBP8 gene mutation that produce autosomal recessive primary microcephaly. Insilco RBBP8 protein models prediction and analysis. Consanguineous Pakistani family affected with non-syndromic primary microcephaly was mapped a biallelic sequence variant (c.1807_1808delAT) in the RBBP8 gene via whole-exome sequencing. The deleted variant in the RBBP8 gene in affected siblings (V:4, V:6) of primary microcephaly was confirmed by sanger sequencing. Identified variant c.1807_1808delAT that truncated the protein translation p. Ile603Lysfs*7 and impaired the functioning of RBBP8 protein. This sequence variant was only reported previously in Atypical Seckel syndrome and Jawad syndrome, while we mapped it in the non-syndromic primary microcephaly family. We predicted 3D protein models by using Insilco tools like I TASSER, Swiss model, and phyre2 of wild RBBP8 protein of 897 amino acids and 608 amino acids of the mutant protein. These models were validated through the online SAVES server and Ramachandran plot and refined by using the Galaxy WEB server. A predicted and refined wild protein 3D model was deposited with accession number PM0083523 in Protein Model Database. A normal mode-based geometric simulation approach was used through the NMSim program, to find out the structural diversity of wild and mutant proteins which were evaluated by RMSD and RMSF. Higher RMSD and RMSF in mutant protein reduced the stability of the protein. The high possibility of this variant results in nonsense-mediated decay of mRNA, leading to the loss of protein functioning which causes primary microcephaly.

Inhibitor Assessment against the LpxC Enzyme of Antibiotic-resistant Acinetobacter baumannii Using Virtual Screening, Dynamics Simulation, and in vitro Assays.

Bacterial resistance is currently a significant global public health problem. Acinetobacter baumannii has been ranked in the list of the World Health Organization as the most critical and priority pathogen for which new antibiotics are urgently needed. In this context, computational methods play a central role in the modern drug discovery process. The purpose of the current study was to identify new potential therapeutic molecules to neutralize MDR A. baumannii bacteria. A total of 3686 proteins retrieved from the A. baumannii proteome were subjected to subtractive proteomic analysis to narrow down the spectrum of drug targets. The SWISS-MODEL server was used to perform a 3D homology model of the selected target protein. The SAVES server was used to evaluate the overall quality of the model. A dataset of 74500 analogues retrieved from the PubChem database was docked with LpxC using the AutoDock software. In this study, we predicted a putative new inhibitor for the Lpxc enzyme of A. baumannii. The LpxC enzyme was selected as the most appropriate drug target for A. baumannii. According to the virtual screening results, N-[(2S)-3-amino-1-(hydroxyamino)-1-oxopropan-2-yl]-4-(4-bromophenyl) benzamide (CS250) could be a promising drug candidate targeting the LpxC enzyme. This molecule shows polar interactions with six amino acids and non-polar interactions with eight other residues. In vitro experimental validation was performed through the inhibition assay. To the best of our knowledge, this is the first study that suggests CS250 as a promising inhibitory molecule that can be exploited to target this gram-negative pathogen.

Comparative In-silico Analysis of Structural and Functional Attributes of Microbial Lipases

Lipase is the third most demanding enzyme group and is a major component of fat metabolism. It is omnipresent in nature. Microbial lipases are highly specific and economically efficient enzymes. They have applications in a variety of industries. In this study, the computational result elucidates structural and functional attributes of eight selected lipases from Uniprot. Primary, secondary, and tertiary structures obtained from Swiss and Profunc are analyzed. Further, they are validated with the help of Saves Server. COACH and COFACTOR is used for functional analysis. Lastly, phylogenetic tree is developed using MegaX software. Our in-silico analysis predicted lipases as alpha-beta-gamma protein with different ligand binding sites at different amino acid residue positions. Furthermore, they exhibited almost similar functional traits like catalytic activity and hydrolase activity. Overall, this study provides an insight into structural-functional characteristics of different lipases of microbial origin.

In silico structural, phylogenetic and drug target analysis of putrescine monooxygenase from Shewanella putrefaciens-95

BackgroundThe enormous and irresponsible use of antibiotics has led to the emergence of resistant strains of bacteria globally. A new approach to combat this crisis has been nutritional immunity limiting the availability of nutrients to pathogens. Targeting the siderophore biosynthetic pathway that helps in iron acquisition, an essential microelement in the bacterial system has been the topic of interest in recent days that backs the concept of nutritional immunity. Supporting this view, we have chosen to study a key enzyme in the biosynthetic pathway of putrebactin called putrescine monooxygenase (SpPMO) from Shewanella putrefaciens. In our previous study, we co-expressed putrescine monooxygenase recombinantly in Escherichia coli BL21 Star (DE3). The bioinformatic analysis and screening of inhibitors will broaden the scope of SpPMO as a drug target. ResultsIn the present study, we have analysed the physicochemical properties of the target enzyme and other N-hydroxylating monooxygenases (NMOs) using ExPASy server. The target enzyme SpPMO and most of the selected NMOs have a slightly acidic isoelectric point and are medially thermostable and generally insoluble. The multiple sequence alignment identified the GXGXX(N/A), DXXXFATGYXXXXP motives and conserved amino acids involved in FAD binding, NADP binding, secondary structure formation and substrate binding. The phylogenetic analysis indicated the distribution of the monooxygenases into different clades according to their substrate specificity. Further, a 3D model of SpPMO was predicted using I-TASSER online tool with DfoA from Erwinia amylovora as a template. The model was validated using the SAVES server and deposited to the Protein Model Database with the accession number PM0082222. The molecular docking analysis with different substrates revealed the presence of a putrescine binding pocket made of conserved amino acids and another binding pocket present on the surface of the protein wherein all other ligands interact with high binding affinity. The molecular docking of naturally occurring inhibitor molecules with SpPMO 3D model identified curcumin and niazirin with 1.83 and 2.81 μM inhibition constants as two promising inhibitors. Further studies on kinetic parameters of curcumin and niazirin inhibitors in vitro determined the Ki to be 2.6±0.0036 μM and 18.38±0.008 μM respectively. ConclusionThis analysis will help us understand the structural, phylogenetic and drug target aspects of putrescine monooxygenase from Shewanella putrefaciens-95 in detail. It sheds light on the precautionary measures that can be developed to inhibit the enzyme and thereby the secondary infections caused by them. Graphical abstract▪ Highlights•The physicochemical properties of putrescine monooxygenase from Shewanella putrefaciens-95 were compared with other known N-hydroxylating monooxygenases.•The phylogenetic analysis of SpPMO and other selected class B monooxygenases clearly classified NMOs according to their substrate affinity.•The molecular docking analysis of various substrates to SpPMO enzyme showed the presence of two binding pockets.•Curcumin and niazirin were identified as two potential natural inhibitors to SpPMO enzyme with 2.6±0.0036 μM and 18.38±0.008 μM inhibition constants respectively.

Primary microcephaly families mapped with different microcephalic genes by using whole exome sequencing; Insilco 3D Model's prediction of STIL, CENPJ, and CEP135 protein

BackgroundPrimary microcephaly (MCPH- OMIM 251200) is inherited from an autosomal recessive, heterogeneous, non-progressive, and neurodevelopmental disorder. It is considered by small occipital-frontal circumference (OFC) for a person's age and gender at birth. MethodsPrimary microcephaly affected consanguineous families (A, B, C, D) in various remote villages of Punjab, Pakistan were identified. Blood samples were collected followed by we used salting-out method for DNA extraction. WES (whole exome sequencing) was used to analyze the mutations and filtered variants that were confirmed by sanger sequencing. ResultsWhole-exome sequencing revealed a sequence variant c.453 + 5G > A in intron 5 of the STIL gene that generated a frameshift and truncated protein (p. Asp89Glyfs*8) by introducing a premature stop codon in family A. We have mapped a mutation in exon 2 (c.18delC) of the CENPJ gene in family B. This variant stopped the transcript synthesis by the premature introduction of the stop codon (p. Ser7Profs*2). The sequence variant in exon 23 (9557C > G) of the ASPM gene in family C, causing a sudden halted the translation process (p. Ser3186X). In family D revealed a variant in intron 11 (c.1473 + 1G > A) of the CEP135 gene caused a frameshift mutation (p. Glu417Glyfs*2) and terminated the protein synthesis, these variants were previously reported and our study supports them. However, no three-dimensional structure models of STIL, CENPJ, and CEP135 protein have been solved in the protein database. As a result, we predicted 3D protein models for the first time in this study by using Insilco technologies such as I TASSER, Swiss model, and phyre2. The validation of these protein models was done by using Ramachandran plot and SAVES server while refinement of the selected models was done by using Galaxy WEB server. In the Protein Model Database, the 3D model of STIL protein predicted and refined was submitted with the entry number PM0084074, whereas the 3D model of CENPJ protein was deposited with the accession number PM0084076. The 3D model of CEP135 protein was deposited in the Protein Model Database (PMDB – https://bioinformatics.cineca.it/PMDB/) with the accession number PM0084077. ConclusionsWe exposed DNA variants in primary microcephaly-affected individuals which impaired the expression of genes that are necessary to build a proper functioning of the brain. This study provides supporting evidence to the pathogenicity of previously reported mutations. Inslico protein models prediction provides functional exploration for clinicians and geneticists for establishing reliable diagnostic strategies for MCPH families.

In silico characterization and structural modeling of bacterial metalloprotease of family M4

BackgroundThe M4 family of metalloproteases is comprised of a large number of zinc-containing metalloproteases. A large number of these enzymes are important virulence factors of pathogenic bacteria and therefore potential drug targets. Whereas some enzymes have potential for biotechnological applications, the M4 family of metalloproteases is known almost exclusively from bacteria. The aim of the study was to identify the structure and properties of M4 metalloprotease proteins. ResultsA total of 31 protein sequences of M4 metalloprotease retrieved from UniProt representing different species of bacteria have been characterized for various physiochemical properties. They were thermostable, hydrophillic protein of a molecular mass ranging from 38 to 66 KDa. Correlation on the basis of both enzymes and respective genes has also been studied by phylogenetic tree. B. cereus M4 metalloprotease (PDB ID: 1NPC) was selected as a representative species for secondary and tertiary structures among the M4 metalloprotease proteins. The secondary structure displaying 11 helices (H1-H11) is involved in 15 helix-helix interactions, while 4 β-sheet motifs composed of 15 β-strands in PDBsum. Possible disulfide bridges were absent in most of the cases. The tertiary structure of B. cereus M4 metalloprotease was validated by QMEAN4 and SAVES server (Ramachandran plot, verify 3D, and ERRAT) which proved the stability, reliability, and consistency of the tertiary structure of the protein. Functional analysis was done in terms of membrane protein topology, disease-causing region prediction, proteolytic cleavage sites prediction, and network generation. Transmembrane helix prediction showed absence of transmembrane helix in protein. Protein-protein interaction networks demonstrated that bacillolysin of B. cereus interacted with ten other proteins in a high confidence score. Five disorder regions were identified. Active sites analysis showed the zinc-binding residues—His-143, His-147, and Glu-167, with Glu-144 acting as the catalytic residues. ConclusionMoreover, this theoretical overview will help researchers to get a details idea about the protein structure and it may also help to design enzymes with desirable characteristics for exploiting them at industrial level or potential drug targets.

Computational Study on Identification of Potential Elephantiasis Inhibitors Against UDP-Galactopyranose Mutase (UGM)

Background: Lymphatic filariasis, regularly known as elephantiasis, is a dismissed tropical malady. A filarial parasite causes the disease when it is transmitted to humans through mosquitoes. The World Health Organization distinguished that this is one of the subsequent driving reasons for lasting and long haul inability. Inaccessibility of immunization and medication opposition of a large portion of the ebb and flow hostile to filarial drugs necessitate quest of novel medication that focuses on creating elective medications. UDP-galactopyranose mutase (UGM) is a flavoenzyme that catalyzes the change of UDP-galactopyranose mutase to UDP-galactofuranose, which is a focal response in galactofuranose biosynthesis. This UGM is fundamental for some pathogens however, it is missing in people, makes UGM a potential medication target. Objective: In the current investigation, UGM from the parasitic nematode Brugia malayi has been considered as an objective during in silico medicate planning of powerful filarial inhibitor. Methods: Here, we build up the homology model of UGM protein dependent on the gem structure of 4DSG. To break down the quality and unwavering quality of the created model, model approval was performed utilizing the SAVES server. Mixes from Specs, Enamine, and Maybridge databases were screened to recognize a potential ligand that could hinder the action of the UGM protein utilizing Glide HTVS and Glide XP. Results: Because of the scoring boundaries, the best 6 hit mixes were chosen and exposed to ADME forecast utilizing QikProp module from Schrodinger. To check the security of docked buildings, an atomic element study was completed. Conclusion: The consequences of this examination give six novel lead mixes to building up an enemy of filarial medication focusing on the UGM protein.

Computational drug re-purposing targeting the spike glycoprotein of SARS-CoV-2 as an effective strategy to neutralize COVID-19

COVID-19 has intensified into a global pandemic with over a million deaths worldwide. Experimental research analyses have been implemented and executed with the sole rationale to counteract SARS-CoV-2, which has initiated potent therapeutic strategy development in coherence with computational biology validation focusing on the characterized viral drug targets signified by proteomic and genomic data. Spike glycoprotein is one of such potential drug target that promotes viral attachment to the host cellular membrane by binding to its receptor ACE-2 via its Receptor-Binding Domain (RBD). Multiple Sequence alignment and relative phylogenetic analysis revealed significant sequential disparities of SARS-CoV-2 as compared to previously encountered SARS-CoV and MERS-CoV strains. We implemented a drug re-purposing approach wherein the inhibitory efficacy of a cluster of thirty known drug candidates comprising of antivirals, antibiotics and phytochemicals (selection contingent on their present developmental status in underway clinical trials) was elucidated by subjecting them to molecular docking analyses against the spike protein RBD model (developed using homology modelling and validated using SAVES server 5.0) and the composite trimeric structures of spike glycoprotein of SARS-CoV-2. Our results indicated that Camostat, Favipiravir, Tenofovir, Raltegravir and Stavudine showed significant interactions with spike RBD of SARS-CoV-2. Proficient bioavailability coupled with no predicted in silico toxicity rendered them as prospective alternatives for designing and development of novel combinatorial therapy formulations for improving existing treatment regimes to combat COVID-19.

In-silico analysis and homology modelling of antioxidant proteins present in Pisum sativum

Garden Peas are edible seeds that have an essential nutrient required for human diet. Peas contain phytochemicals that shows antioxidant activities. A computational approach was adopted to analyse the characteristic properties and structure of the Peas antioxidant proteins. Superoxidase dismutase (SOD), catalase, phospholipid hydroperoxide g lutathione p eroxidase-like p rotein (PHGPX) and ascorbate peroxidase (APX) are the antioxidant proteins which have been used in this study. Physiochemical properties such as isoelectric point, molecular weight, aliphatic index etc. were analysed by using the Expasy's Prot Param server. Secondary structure of these proteins was analysed to study the functional characteristic of the protein. The crystal structure of the ascorbate peroxidase was available, but the crystal structures of other proteins were not available in any of the protein structure database. Hence the threedimensional structures of these proteins were generated using Swiss Modeller and Geno3D followed by validation through SAVES server. This analysis will provide additional help for the analysis of crystal structure for further experimentation.

Computational guided drug repurposing for targeting 2′-O-ribose methyltransferase of SARS-CoV-2

AimsThe recent outbreak of pandemic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led the world towards a global health emergency. Currently, no proper medicine or effective treatment strategies are available; therefore, repurposing of FDA approved drugs may play an important role in overcoming the situation. Materials and methodsThe SARS-CoV-2 genome encodes for 2-O-methyltransferase (2′OMTase), which plays a key role in methylation of viral RNA for evading host immune system. In the present study, the protein sequence of 2′OMTase of SARS-CoV-2 was analyzed, and its structure was modeled by a comparative modeling approach and validated. The library of 3000 drugs was screened against the active site of 2′OMTase followed by re-docking analysis. The apo and ligand-bound 2′OMTase were further validated and analyzed by using molecular dynamics simulation. Key findingsThe modeled structure displayed the conserved characteristic fold of class I MTase family. The quality assessment analysis by SAVES server reveals that the modeled structure follows protein folding rules and of excellent quality. The docking analysis displayed that the active site of 2′OMTase accommodates an array of drugs, which includes alkaloids, antivirals, cardiac glycosides, anticancer, steroids, and other drugs. The redocking and MD simulation analysis of the best 5 FDA approved drugs reveals that these drugs form a stable conformation with the 2′OMTase. SignificanceThe results suggested that these drugs may be used as potential inhibitors for 2′OMTase for combating the SARS-CoV-2 infection.

Design and Implementation of Loan Management System using ISI Server, PhP and MySql

Credit institutions are critical organizations whose role in society and modern economy is very important for issuing loan to individuals, businesses and other organizations. Before such institutions came to the scene, there was no secure place for individuals and businesses to get credit, which caused a lot of chaos. The proposed loan management system brings various loan portfolios on one platform to assist the atomization of servicing and management of such credit institutions. Branches of credit institutions are connected with the loan management system. If client provides all required information and submit all required documents, the loan underwriter will verify and approve the loan from the system. The proposed system will automatically inform to the applicant with required information for proceeding. Loan underwriter can manage and monitor multiple loan portfolios from multiple locations through a single platform starting from prospecting to closure. The proposed system also simplifies the decision making processes. The architecture of loan management system using ISI Server, PhP and MySql is proposed in this work. The suggested architecture consists of four primary components: Clients, internet link, server, and branches of credit institutions. Clients can register, apply and trace their loan through online via desktop, laptop, and portable tablet or mobile. A dependable and safe server is taken into account to actualize the framework. In addition, reliability and security problems considered for designing the system are stated here. Some portion of the suggested architecture is currently being implemented and tested that is also outlined in this paper.

A computational study targeting the mutated L321F of ERG11 gene in C. albicans, associated with fluconazole resistance with bioactive compounds from Acacianilotica

BackgroundEmergence of fluconazole resistance mediated by L321F mutation in the ERG11 gene poses a serious impediment in the candidal treatment process. This leads to the search of novel target proteins to develop newer drugs against fluconazole resistant C. albicans. The present investigation is thus aimed to explore the inhibitory potential of bioactive compounds from A. nilotica against the wild and mutated ERG11 gene of C. albicans. Materials and methodsHomology modelling of the wild and mutated ERG11 target gene was done by Modeller 9.2, with SDM I-mutant form of ERG11 together with SAVES server and Ramachandran plot validation. 2D and 3D structures of the bioactive compounds from A. nilotica were optimized by ACD chemsketch. Molinspirational assessments for the molecular properties of the ligands and their drug likeliness were estimated. In silico inhibitory potential of the selected ligands against wild and mutated ERG11 was done by AutoDock 2.0 and was visualized with Accelrys discovery studio tool. ResultsApigenin proved to be the best candidate to target mutant ERG11 with a binding energy of −8.33 Kcal/mol followed by catechin with six hydrogen bonds with more drug likeliness. Molinspiration assessments showed zero violations for all the bioactive compounds from A. nilotica and the TPSA scores of the ligands showed the values<140Å towards the best oral bio-availability. ConclusionThe findings of the study emphasize that kaempferol, apigenin and catechin from A. nilotica seem to possess a promising inhibitory effect against the wild and mutated ERG11 of C. albicans suggesting ERG11 as the best target to combat fluconazole resistant C. albicans with further in vivo validation targeting the same.

Identification of Anticancer Lead Molecules against PRR11 Protein Target with Combination of Protein Modelling Through Threading Approach, Structure Based Chemical Screening of ZINC Database and Pharmacokinetic Properties

Abstract: PRR11 is a cell cycle regulator involved in pathogenesis of cancer. Therapeutically it has been identified as target to inhibit cancer growth. In this study, we employed insilico strategies for determining 3D structure of PRR11 and identification of lead molecules. Molecular modelling of PRR11 was carried out with the help of @TOME2, modeller 9.10 and ModLoop, further structure refinement conceded with SAVES server. ZINC database was virtually screened against reference marine natural compound and docking simulations were performed in AutoDock Vina. Pharmacokinetic properties of leads have been analysed in DataWarrior. Collectively, the results revealed that the 3D structure of target was successfully build with refinement of 30 loops, the reliable structure showed that 83.87% amino acid residues present in allowed regions of Ramachandran plot, Errat overall quality factor was 83.807, passed in Prove and more than 65% amino acids have scored >=0.2 in the 3D/1D plot of PRR11. Based on small molecule screening of ZINC database against PRR11 with resulted pharmacokinetic descriptors, 5 lead molecules (28863059, 79642438, 27766155, 27855322 and 28365298) have predicted as PRR11 inhibitors, which are very prominent in binding energy values, ADME and adverse effects (MTIR) calculations than reference marine natural compound. ZINC database has been given a choice to purchase lead molecules for further evaluations in invitro/invivo studies; it could be an opportunity to further prove of this insilico predictions. Key words: PRR11, ZINC Database, Threading, Docking, Pharmacokinetics.