UniProt ID Research Articles

In silico molecular docking studies of 2, 4 and 2, 6-di-tert-butylphenol against NAGK and SPP1 proteins to recuperate rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that primarily affects the synovial joints. It is connected to progressive disability, early death and socioeconomic burdens. So, there is an urgent need to develop novel drugs that effectively treat patients at each stage of disease progression. N-acetylglucosamine kinase (NAGK) and secreted phosphoprotein 1(SPP1) are two attractive drug targets for RA. NAGK protein converts endogenous N-acetyl-D-glucosamine (GlcNAc) into GlcNAc 6-phosphate. GlcNAc has suppressive effects on experimental RA in mouse models. SPP1 stimulates the synthesis of a key proinflammatory cytokine implicated in the pathogenesis of RA. In order to design new drug candidates, crystal structure of human NAGK (PDB Id: 2CH5) was retrieved from the protein data bank. Amino acid sequence of SPP1 (Uniprot Id: P10451) was retrieved from uniprot database and modeled by the Phyre2 server. Computed SPP1 model energy was minimized using the YASARA energy minimization server and validated by the Ramachandran plot and the ProSA-web error-detection tool. 2, 4-Di-tert-butylphenol and 2, 6-Di-Tert-butylphenol are known compound isolates from bark extract of Schleichera oleosa having anti-arthritic and anti-inflammatory activities as accessed by PASS online server. These compounds were docked against active site of NAGK and SPP1 proteins using MTiAutoDock server. The binding affinity of these compounds against NAGK and SPP1 proteins ranges from -6.12 to -7.17 kcal/mol. The findings of this study will open the way for the development of herbal remedies for rheumatoid arthritis based on the Schleichera oleosa plant, potentially leading to the development of novel drugs.

Validation and in vivo characterization of research antibodies for Moesin, CD44, Midkine, and sFRP-1.

Background: A major goal of the Target Enablement to Accelerate Therapy Development for Alzheimer’s disease (TREAT-AD) program is to develop and identify high-quality tools to test target or mechanistic hypotheses. As part of this initiative, it is important that commercial reagents including research antibodies being used to interrogate drug targets have confirmed validation data in knock-out cell lines. Ideally, these antibodies should also have utility for both in vitro and in vivo studies such that the levels of target proteins in target tissues can be quantified. Methods: We evaluated commercial antibodies against TREAT-AD protein targets Moesin (Uniprot ID: P26038), CD44 (Uniprot ID: P16070), Midkine (Uniprot ID: P21741) and Secreted frizzled-related protein 1, referred to as “sFRP-1” (sFRP-1; Uniprot ID: Q8N474). Moesin, Midkine and sFRP-1, that were confirmed as selective based on data in knock-out cell lines. Western blot analysis was used to compare protein levels in brain homogenates from a mouse model with AD-relevant pathology (5XFAD) versus age-matched C57BL/6J control mice. Results: Anti-Moesin ab52490 reacted in mouse brain homogenate with a predicted molecular weight of 68 kDa. Moesin protein expression was 2.8 times higher in 5xFAD compared to WT. Anti-CD44 ab189524 reacted with a band at the predicted size of 82 kDa. CD44 protein expression was 1.9 times higher in 5xFAD compared to WT. Anti-Midkine AF7769 reacted with a band ~16 kDa and a 17.8 times greater expression in 5xFAD compared to WT. Anti-sFRP-1 ab267466 reacted with a band at 35 kDa as predicted. sFRP-1 protein expression was 11.9 times greater in 5xFAD compared to WT. Conclusions: These data confirm the utility of these selective commercially available antibodies against Moesin, CD44, Midkine, and sFRP-1 for in vivo studies in mice and provide insight into the use of 5XFAD mice for in vivo target engagement studies for these target proteins.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Using evolutionary data to make sense of macromolecules with a "face-lifted" ConSurf.

The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae.

Pharmacokinetics of some newly synthesized 1, 5- benzothiazepine scaffolds: A molecular docking and molecular dynamics simulation approach

A highly efficient in silico pharmacokinetics was studied for some newly synthesized 1, 5- benzothiazepine derivatives. A total of fourteen title moieties were prepared by the condensation of substituted chalcones and 2- aminothiophenol that led to ring expansion in the presence of CuO nanocatalytic framework. The reported synthetic route is advantageous due to shorter reaction time and enhanced yield. The drug-target binding analysis revealed the potential therapeutic targets against 1,5- benzothiazepine derivatives using auto in silico consensus inverse docking (ACID) server. Among the seven selected 1,5- benzothiazepine derivatives, consensus inverse docking (CID) against PDB-binding database identified potential therapeutic targets using structure-based screening. The comparative analysis predicted 4-hydroxyphenylpyruvate dioxygenase (HPPD) (Uniprot ID: P32754), involved in the regulation of blood tyrosine levels by catalyzing the reaction of HPPD to homogentisic acid in tyrosine catabolism pathway. Dimethylglycine dehydrogenase (DMGDH) (Uniprot ID: Q9AGP8), a flavin adenine dinucleotide (FAD)‐ and tetrahydrofolate (THF)‐dependent, mitochondrial matrix enzyme that degrades choline and transfers electrons to the respiratory chain, one‐carbon metabolism, and folate receptor beta (Uniprot ID: P14207), which is a glycosylphosphatidylinositol-linked protein capturing ligandsfrom the extracellular matrix and passages them inside the cell throughendosomalpathway and recognized as an emerging biomarker for cancer and chronic inflammatory diseases, as potential targets for the selected derivatives. These findings warrant for in vitro cell-based experimental validation of the currently identified actives.

Indigenous Oral and Gut Phages Defeat the Deadly NDM-1 Superbug.

Antibiotics treat various diseases by targeting microorganisms by killing them or reducing their multiplication rate. New Delhi Metallo-beta-lactamase-1 (NDM-1) is produced by bacteria possessing the resistance gene blaNDM-1, the enzyme that makes bacteria resistant to beta-lactams. Bacteriophages, especially Lactococcus, have shown their ability to break down lactams. Hence, the current study computationally evaluated the binding potential of Lactococcus bacteriophages with NDM using Molecular docking and dynamics. Modelling of NDM I-TASSER for Main tail protein gp19 OS=Lactococcus phage LL-H or Lactobacillus delbrueckii subsp. lactis after downloading from UNIPROT ID- Q38344. Cluspro tool helps in Understanding cellular function and organization with protein-protein interactions. MD simulations(19) typically compute atom movements over time. Simulations were used to predict the ligand binding status in the physiological environment. The best binding affinity score was found -1040.6 Kcal/mol compared to other docking scores. MD simulations show in RMSD values for target remains within 1.0 Angstrom, which is acceptable. The ligand-protein fit to receptor protein RMSD values of 2.752 fluctuates within 1.5 Angstrom after equilibration. Lactococcus bacteriophages showed a strong affinity to the NDM. Hence, this hypothesis, supported by evidence from a computational approach, will solve this life-threatening superbug problem.

Identification of a Potential Vaccine against Treponema pallidum Using Subtractive Proteomics and Reverse-Vaccinology Approaches

Syphilis, a sexually transmitted infection, is a deadly disease caused by Treponema pallidum. It is a Gram-negative spirochete that can infect nearly every organ of the human body. It can be transmitted both sexually and perinatally. Since syphilis is the second most fatal sexually transmitted disease after AIDS, an efficient vaccine candidate is needed to establish long-term protection against infections by T. pallidum. This study used reverse-vaccinology-based immunoinformatic pathway subtractive proteomics to find the best antigenic proteins for multi-epitope vaccine production. Six essential virulent and antigenic proteins were identified, including the membrane lipoprotein TpN32 (UniProt ID: O07950), DNA translocase FtsK (UniProt ID: O83964), Protein Soj homolog (UniProt ID: O83296), site-determining protein (UniProt ID: F7IVD2), ABC transporter, ATP-binding protein (UniProt ID: O83930), and Sugar ABC superfamily ATP-binding cassette transporter, ABC protein (UniProt ID: O83782). We found that the multiepitope subunit vaccine consisting of 4 CTL, 4 HTL, and 11 B-cell epitopes mixed with the adjuvant TLR-2 agonist ESAT6 has potent antigenic characteristics and does not induce an allergic response. Before being docked at Toll-like receptors 2 and 4, the developed vaccine was modeled, improved, and validated. Docking studies revealed significant binding interactions, whereas molecular dynamics simulations demonstrated its stability. Furthermore, the immune system simulation indicated significant and long-lasting immunological responses. The vaccine was then reverse-transcribed into a DNA sequence and cloned into the pET28a (+) vector to validate translational activity as well as the microbial production process. The vaccine developed in this study requires further scientific consensus before it can be used against T. pallidum to confirm its safety and efficacy.

A Genome-Wide Alternative Splicing Landscape Specifically Associated with Durable Rice Blast Resistance

The rice blast, caused by the hemibiotrophic plant pathogen Magnaporthe oryzae, is a devastating disease that threatens rice crop production worldwide. The molecular interactions that underlie the rice-M. oryzae interaction have received much attention. However, genome-wide research focusing on alternative splicing (AS) has not been well-studied in rice—M. oryzae interactions. AS in plants leads to diverse proteomes without an expansion in gene numbers to regulate cellular processes during abiotic or biotic stress. The Pi21 gene negatively regulates rice resistance to M. oryzae infection, and thus the Pi21-RNAi silenced transgenic line (#241) exhibits partial but durable resistance. We compared the AS landscape in #241 and “Nipponbare” (Nip) during interacting with M. oryzae Guy11, and the alternative 3′ splice-site (A3SS) is the most common AS type. GO enrichment analysis of #241-specific differentially alternatively spliced genes (DASGs) revealed that WRKY transcription factors (TFs), bHLH TFs, F-box protein with leucine rich repeats, AAA-type ATPase, and protein kinase were enriched in the GO terms “response to jasmonate acid (JA)” and “ethylene (ET)” at 24 h post-inoculation (hpi). At 48 hpi, one #241-specific DASG, ubiquitin gene (Os08g0295000), was predicted to be involved in endoplasmic reticulum (ER) stress. In silico analysis combined with PCR amplification confirmed that multiple isoforms are produced by Os08g0295000 and a skipped exon (SE) event results in isoform switching during interaction with M. oryzae. Protein–protein interaction (PPI) network analysis predicted that Os08g0295000-encoding proteins may interact with SNARE protein Q9LGF8 (Uniprot ID) to cooperatively regulate rice’s response to M. oryzae. This study uncovered the AS profile of rice in response to M. oryzae, which will help to explore the linkage between AS and durable rice resistance.

Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis.

Moraxella catarrhalis (M. catarrhalis) is a gram-negative bacterium, responsible for major respiratory tract and middle ear infection in infants and adults. The recent emergence of the antibiotic resistance M. catarrhalis demands the prioritization of an effective drug target as a top priority. Fortunately, the failure of new drugs and host toxicity associated with traditional drug development approaches can be avoided by using an in silico subtractive genomics approach. In the current study, the advanced in silico genome subtraction approach was applied to identify potential and pathogen-specific drug targets against M. catarrhalis. We applied a series of subtraction methods from the whole genome of pathogen based on certain steps i.e. paralogous protein that have extensive homology with humans, essential, drug like, non-virulent, and resistant proteins. Only 38 potent drug targets were identified in this study. Eventually, one protein was identified as a potential new drug target and forwarded to the structure-based studies i.e. histidine kinase (UniProt ID: D5VAF6). Furthermore, virtual screening of 2000 compounds from the ZINC database was performed against the histidine kinase that resulted in the shortlisting of three compounds as the potential therapeutic candidates based on their binding energies and the properties exhibited using ADMET analysis. The identified protein gives a platform for the discovery of a lead drug candidate that may inhibit it and may help to eradicate the otitis media caused by drug-resistant M. catarrhalis. Nevertheless, the current study helped in creating a pipeline for drug target identification that may assist wet-lab research in the future.

In silico mutational analysis to identify the role and pathogenicity of BCL-w missense variants

BackgroundIntrinsic pathway of apoptosis is generally mediated by BCL-2 (B cell lymphoma 2) family of proteins; they either induce or inhibit the apoptosis. Overexpression of BCL-2 in cancer cell may lead to delay in apoptosis. BCL-w is the pro-survival member of the BCL-2 family. BCL2L2 gene is present on chromosome number 14 in humans, and it encodes BCL-w protein; BCL-w protein is 193 amino acids residues in length. Interactions among the BCL-2 proteins are very specific. The fate of cell is determined by the ratio of pro-apoptotic proteins to pro-survival proteins. BCL-w promotes cell survival. Studies suggested that overexpression of BCL-w protein is associated with many cancers including DLBCL, BL, colorectal cancers, gastric cancers, and many more. The cause of overexpression is translocations or gene amplification which will subsequently result in cancerous activity. ProcessFor in-silico analysis, BCL2L2 gene was retrieved from UniProt (UniProt ID: Q92843). 54 missense variants have been collected in BCL-w proteins from COSMIC database. Different tools were used to detect the deleteriousness of the variants. ResultIn silico mutational study reveals how the non-synonymous mutations directly affect the protein’s native structure and its function. Variant mutational analysis with PolyPhen-2 revealed that out of 55 variants, 28 of the missense mutations was probably damaging with a score ranging from 0.9 to 1, while 24 variants were benign with a score ranging from 0 to 0.4. ConclusionsThis in silico work aims to determine how missense mutations in BCL-w protein affect the activity of the protein, the stability of the protein, and to determine the pathogenicity of the variants. Prediction of pathogenicity of variants will reveal if the missense mutation has a damaging effect on the native structure of protein or not. Prediction of protein stability will reveal whether the mutation has a stabilizing or destabilizing effect on the protein.

Characterization of a small tRNA-binding protein that interacts with the archaeal proteasome complex.

The proteasome system allows the elimination of functional or structurally impaired proteins. This includes the degradation of nascent peptides. In Archaea, how the proteasome complex interacts with the translational machinery remains to be described. Here, we characterized a small orphan protein, Q9UZY3 (UniProt ID), conserved in Thermococcales. The protein was identified in native pull‐down experiments using the proteasome regulatory complex (proteasome‐activating nucleotidase [PAN]) as bait. X‐ray crystallography and small‐angle X‐ray scattering experiments revealed that the protein is monomeric and adopts a β‐barrel core structure with an oligonucleotide/oligosaccharide‐binding (OB)‐fold, typically found in translation elongation factors. Mobility shift experiment showed that Q9UZY3 displays transfer ribonucleic acid (tRNA)‐binding properties. Pull‐downs, co‐immunoprecipitation and isothermal titration calorimetry (ITC) studies revealed that Q9UZY3 interacts in vitro with PAN. Native pull‐downs and proteomic analysis using different versions of Q9UZY3 showed that the protein interacts with the assembled PAN–20S proteasome machinery in Pyrococcus abyssi (Pa) cellular extracts. The protein was therefore named Pbp11, for Proteasome‐Binding Protein of 11 kDa. Interestingly, the interaction network of Pbp11 also includes ribosomal proteins, tRNA‐processing enzymes and exosome subunits dependent on Pbp11's N‐terminal domain that was found to be essential for tRNA binding. Together these data suggest that Pbp11 participates in an interface between the proteasome and the translational machinery.

Abstract 4106: Curating protein complexes from multiple resources and validate their co-regulations at RNA/protein level in pan-cancer

Abstract Protein complexes are sets of proteins that not only physically interact with each other, but also play important roles in progression of diseases like cancer. However, little is known about the co-regulation patterns of complexes and how many of them could be potential therapeutic targets. We collected 16,680 complexes from CORUM, Reactome, Complex Portal, and Havugimana PC. Harmonization of subunits of complexes by UniProt ID removed 4,112 redundant complexes. We further found that 3,018/12,559 complexes have at least one cancer driver gene involved, of which 2,408 complexes were found to have druggable genes, according to CiVic and OncoKB. Co-regulation patterns of 12,559 complexes were annotated utilizing gene expression and protein expression data from 1,101 cases across 10 cancer types (BR, COAD, OV, UCEC, HNSCC, PDAC, LSCC, LUAD, GBM, and ccRCC) from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Pairwise correlations across subunits of each complex at protein and RNA levels were calculated separately. Correlation >0.8 would suggest strong interaction of the subunits of the complex. Protein-only co-regulation complex was declared for protein pairwise correlation >0.8 and RNA pairwise correlation <0.5. RNA-only co-regulation complex was defined vice versa. We found more protein-only co-regulation complexes than RNA-only co-regulation complexes. This validated that RNA expression doesn’t always correlate with protein expression and few complexes were co-regulated at both RNA and protein levels. Our study suggests the value of utilizing proteome data to understand the formation of complex and inner relationships between subunits of complexes. Citation Format: Yizhe Song, Daniel Cui Zhou, Yize Li, Liang-Bo Wang, Michael C. Wendl, Gad Getz, Li Ding. Curating protein complexes from multiple resources and validate their co-regulations at RNA/protein level in pan-cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4106.

In Silico Modeling and Docking Analysis of CTX-M-5, Cefotaxime-Hydrolyzing β-Lactamase from Human-Associated Salmonella Typhimurium

Objective: To computationally model the CTX-M-5 β-lactamase and establish its structure, which is exclusively present in human-associated Salmonella. Methods: The CTX-M-5 aminoacid sequence (Uniprot ID:O65975) of Salmonella enterica subsp. enterica serovar typhimurium was retrieved from UniProt database and subjected to homology modeling using MODELLER 9v7. The homology models were duly validated using RAMPAGE tool by generating Ramachandran plots, ERRAT graphs, and ProSA score. DoGSiteScorer server and ConSurf server were used to detect the cavities, pockets, and clefts to identify conserved amino acid sites in the predicted model. Subsequently, the modeled structure was docked using CLC Drug Discovery Workbench against proven drugs and known inhibitors. Results: Obtained high-quality homology model with 91.7% of the residues in favorable regions in Ramachandran plot and qualified in other quality parameters. Docking studies resulted in a higher dock score for PNK (D-benzylpenicilloic acid) molecule when compared to other reported inhibitors. Conclusion: This in silico study suggests that the compound PNK could be an efficient ligand for CTX-M-5 β-lactamase and serve as a potent inhibitor of CTX-M-5.

PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.

P200 Combining protein quantitative trait and genetic risk score analysis to identify biomarkers of treatment response to TNFi in patients with rheumatoid arthritis

Abstract Background/Aims Rheumatoid arthritis (RA) is an inflammatory joint disease that can lead to disability if inflammation is not controlled. Biologic disease modifying anti-rheumatic drugs (bDMARDs), including tumour necrosis factor inhibitors (TNFi), are an effective line of therapy for moderate to severe RA. However, treatment response to TNFi is not universal and cannot be predicted from clinical factors. In this study, a protein quantitative trait loci (pQTL) study and genetic risk score (GRS) analysis were combined to identify protein-based biomarkers that are predictive of TNFi treatment response. Methods Protein expression data were generated using Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) in serum samples taken from 80 RA patients about to commence therapy with etanercept. Protein expression was log2 transformed and the k-nearest neighbour model was used to impute missing values. A pQTL study was performed in patients with available imputed genome-wide genetic variation data to detect cis-acting genetic markers (p < 1E-05). GRS for pQTLs were subsequently tested in 1,430 RA patients with available genome-wide genetic and TNFi treatment response data (improvement in DAS28 between pre-treatment and 3/6 months on drug). The GRS analysis was adjusted for baseline disease activity, sex, conventional synthetic DMARD co-therapy and the first 10 principal components, calculated from the genetic datasets. Results Following imputation, expression levels for 271 proteins were analysed in 69 RA patients with available genotype data. 514 cis-pQTLs were found for 16 proteins. GRS for the proteins Apolipoprotein(a) (UniProt ID P08519, p = 0.017) and Carboxypeptidase N subunit 2 (P22792, p = 0.027) were found to be modestly associated with treatment response; however, scores for both proteins explained less than 1% of the variance in DAS28 difference between time-points. Conclusion This study identified two protein-based genetic biomarkers of treatment response to TNFi. However, genetic scores based on these proteins are unlikely to be useful predictors, explaining little variance in on-treatment disease activity. Disclosure Z. Li: None. S.F. Ling: None. N. Nair: None. J.D. Isaacs: None. K.L. Hyrich: Honoraria; Abbvie. A.W. Morgan: None. A.G. Wilson: None. A. Barton: None. D. Plant: None.

Purification and characterization of a fungal aspartic peptidase from Trichoderma reesei and its application for food and animal feed protein hydrolyses.

Various neutral and alkaline peptidases are commercially available for use in protein hydrolysis under neutral to alkaline conditions. However, the hydrolysis of proteins under acidic conditions by applying fungal aspartic peptidases (FAPs) has not been investigated in depth so far. The aim of this study, thus, was to purify a FAP from the commercial enzyme preparation, ROHALASE® BXL, determine its biochemical characteristics, and investigate its application for the hydrolysis of food and animal feed proteins under acidic conditions. A Trichoderma reesei derived FAP, with an apparent molecular mass of 45.8kDa (sodium dodecyl sulfate-polyacrylamide gel electrophoresis; SDS-PAGE) was purified 13.8-fold with a yield of 37% from ROHALASE® BXL. The FAP was identified as an aspartate protease (UniProt ID: G0R8T0) by inhibition and nano-LC-ESI-MS/MS studies. The FAP showed the highest activity at 50°C and pH4.0. Monovalent cations, organic solvents, and reducing agents were tolerated well by the FAP. The FAP underwent an apparent competitive product inhibition by soy protein hydrolysate and whey protein hydrolysate with apparent Ki -values of 1.75 and 30.2mg*mL-1 , respectively. The FAP showed promising results in food (soy protein isolate and whey protein isolate) and animal feed protein hydrolyses. For the latter, an increase in the soluble protein content of 109% was noted after 30 min. Our results demonstrate the applicability of fungal aspartic endopeptidases in the food and animal feed industry. Efficient protein hydrolysis of industrially relevant substrates such as acidic whey or animal feed proteins could be conducted by applying fungal aspartic peptidases. © 2022 Society of Chemical Industry.

IDENTIFICATION OF FGFR INHIBITOR AS ST2 RECEPTOR/INTERLEUKIN-1 RECEPTOR-LIKE 1 INHIBITOR IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE DUE TO EXPOSURE TO E-CIGARETTES BY NETWORK PHARMACOLOGY AND MOLECULAR DOCKING PREDICTION

Objective: This study was designed to search for candidate drugs that act on IL-33 and ST2, which was carried out using a bioinformatics approach. Methods: We first analyzed Network Electronic Cigarette Smokes Predictions of therapeutic targets by Cytoscape. We collected from the Swiss TargetPrediction database [http://www. swisstargetprediction. ch/] by inputting each compound structure of the electronic cigarette smoke in SDF format. The target protein data is then supplemented with UniProt ID data to uniform protein identity. We then identified COPD Related Targets in Humans by Cytoscape. Subsequently, we identified key receptors in the pathogenesis of COPD. All target proteins that have a significant role in the pathogenesis of COPD exposed to cigarette smoke will be known from the combination of this network. Results: Based on the validation results of the protein receptor for ST2, a protein is used as a receptor with PDB ID: 1IRA. After analyzed by PyMol software, a protein with PDB ID 1IRA it has no missing residue in its sequenceDrug candidates analyzed by the structural similarity with the native ligand using PubChem and DRUGBANK analysis are follow: N-acetylmannosamine, Aceneuramic acid, Ceramide AP, Ceramide NP, Hg9a-9, Nonanoyl-N-hydroxyethylglucamide, N-Acetyl-2-deoxy-2-amino-galactose, N-Acetyllactosamine, MLi/2,6-dimethyl-4-[6-[5-[1-methylcyclopropyl] oxy-1H-indazol-3-yl] pyrimidin-4-yl] morpholine, Terazosin, BMS-911543, NAG Inhibitor, FGFR Inhibitor/sodium; 2-amino-5-[1-methoxy-2-methylindolizine-3-carbonyl] benzoate. After docking, the smallest or more negative binding affinity values are obtained. The stronger the FGFR Inhibitor ligand showed the interaction with the Receptor with a binding affinity value of-9.0 kcal/mol with mode/position 0, and RMSD 0.0. The second smallest binding affinity value is the NAG ligand with a-8.5 kcal/mol with mode/position 0 and RMSD 0.0. Conclusion: The findings revealed that FGFR Inhibitor was a suitable repurposing medication for anti-COPD development via the IL-33/ST-2 signaling pathway.

A Comprehensive Analysis of Human CYP3A4 Crystal Structures as a Potential Tool for Molecular Docking-Based Site of Metabolism and Enzyme Inhibition Studies

The notable ability of human liver cytochrome P450 3A4 (CYP3A4) to metabolize diverse xenobiotics encourages researchers to explore in-depth the mechanism of enzyme action. Numerous CYP3A4 protein crystal structures have been deposited in protein data bank (PDB) and are majorly used in molecular docking analysis. The quality of the molecular docking results depends on the three-dimensional CYP3A4 protein crystal structures from the PDB. Present review endeavors to provide a brief outline of some technical parameters of CYP3A4 PDB entries as valuable information for molecular docking research. PDB entries between 22 April 2004 and 2 June 2021 were compiled and the active sites were thoroughly observed. The present review identified 76 deposited PDB entries and described basic information that includes CYP3A4 from human genetic, Escherichia coli (E. coli) use for protein expression, crystal structure obtained from X-ray diffraction method, taxonomy ID 9606, Uniprot ID P08684, ligand–protein structure description, co-crystal ligand, protein site deposit and resolution ranges between 1.7[Formula: see text]Å and 2.95[Formula: see text]Å. The observation of protein–ligand interactions showed the various residues on the active site depending on the ligand. The residues Ala305, Ser119, Ala370, Phe304, Phe108, Phe213 and Phe215 have been found to frequently interact with ligands from CYP3A4 PDB. Literature surveys of 17 co-crystal ligands reveal multiple mechanisms that include competitive inhibition, noncompetitive inhibition, mixed-mode inhibition, mechanism-based inhibition, substrate with metabolite, inducer, or combination modes of action. This overview may help researchers choose a trustworthy CYP3A4 protein structure from the PDB database to apply the protein in molecular docking analysis for drug discovery.

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity.

Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin-angiotensin-aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php.

Effects of Normal and Aberrant Glycosylation on the Stability of α1-Anti Trypsin Through Molecular Dynamic Simulation

The Alpha-1 antitrypsin belonging to serpin family is a protease inhibitor, the level of which rises by a factor of ten during inflammation Purpose: To investigate the stability of normal and aberrantly glycosylated α1-antitrypsin through molecular dynamics simulation Study Design: Experimental study Methodology: Current project was conducted in the department of Biochemistry at Institute of Basic Medical Sciences Khyber Medical University, Peshawar. A1AT FASTA sequence was retrieved from UniProt database (UniProt ID: P01009). Post-Translational Modifications (PTM) regions were identified from the same database. GLYCONNECT database was used to understand N-linked glycation with the asparagine residues found at position 70, 107, and 271 amino acid residue regions Statistical analysis: Different bioinformatics analyses such that Root Mean Square Deviation, Radius of gyration, Root Mean Square fluctuation, Hydrogen-bonding, Secondary Structure Determination, and Principal Component Analysis were executed for 100 ns molecular dynamics simulation run Results: RMSd, RMSf, and Rgyr significantly differ between the native type and cancer isoform. More H-bonding and strong protein stability and folding were seen in the native type. PCA analysis further confirms native type compact motion in the parallel direction during MD simulation Conclusion: It was concluded that glycated protein appears to have high structural stability than its aberrant glycated protein. However, it will be utilized for the prompt production of the anti-cancer drugs to effectively treating cancer disease Key Words: Alpha-1 Antitrypsin, Aberrant Glycosylation and Molecular Dynamic Simulation.