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Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery ( r = 0.65), maternal age ( r = 0.59), gravidity ( r = 0.56), and BMI ( r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Background: Changing morbidity and mortality from COVID-19 has been associated with the emergence of new SARS-CoV-2 variants. Whereby, acquisition of mutations in the Spike glycoprotein enhanced host receptor binding, cell entry and antibody escape. Understanding these can help predict the impact of these changes. We used genome sequence data to investigate mutation rates and entropy of SARS-CoV-2 during pandemic surges between 2020 and 2022. Methods: 1,637 SARS-CoV-2 genomes from Pakistan were analyzed using the Augur phylogenetic pipeline. Substitution rates and entropy of genomes were calculated year wise and, entropy in the Spike gene was compared for 2020, 2021 and 2022 (defined as periods A, B and C). Central Findings: In period A, G clades were predominant and SARS-CoV-2 genome substitution rate was 6.06 x 10 per site per year. In period B, Delta variant were dominant and substitution rates increased to 9.74 x 10 . In period C, Omicron variants dominated with substitution rates at 5.02 x 10 . The rate of genome-wide entropy was the highest during B particularly, in the Spike gene such as, E484K and K417N. During C, genome-wide mutations were increased whilst entropy was reduced. Conclusions: The highest SARS-CoV-2 genome substitution rates in 2021 were associated with the Delta wave, which had the greatest morbidity and mortality. These stabilized during the Omicron wave in 2022, when COVID-19 numbers were high mortality was lower. Assessment of SARS-CoV-2 evolution should be monitored together with phylogeographical analysis can help predict future outbreaks and guide public health interventions.

BackgroundWe investigated the genome diversity of SARS-CoV-2 associated with the early COVID-19 period to investigate evolution of the virus in Pakistan.Materials and methodsWe studied ninety SARS-CoV-2 strains isolated between March and October 2020. Whole genome sequences from our laboratory and available genomes were used to investigate phylogeny, genetic variantion and mutation rates of SARS-CoV-2 strains in Pakistan. Site specific entropy analysis compared mutation rates between strains isolated before and after June 2020.ResultsIn March, strains belonging to L, S, V and GH clades were observed but by October, only L and GH strains were present. The highest diversity of clades was present in Sindh and Islamabad Capital Territory and the least in Punjab province. Initial introductions of SARS-CoV-2 GH (B.1.255, B.1) and S (A) clades were associated with overseas travelers. Additionally, GH (B.1.255, B.1, B.1.160, B.1.36), L (B, B.6, B.4), V (B.4) and S (A) clades were transmitted locally. SARS-CoV-2 genomes clustered with global strains except for ten which matched Pakistani isolates. RNA substitution rates were estimated at 5.86 x10−4. The most frequent mutations were 5’ UTR 241C > T, Spike glycoprotein D614G, RNA dependent RNA polymerase (RdRp) P4715L and Orf3a Q57H. Strains up until June 2020 exhibited an overall higher mean and site-specific entropy as compared with sequences after June. Relative entropy was higher across GH as compared with GR and L clades. More sites were under selection pressure in GH strains but this was not significant for any particular site.ConclusionsThe higher entropy and diversity observed in early pandemic as compared with later strains suggests increasing stability of the genomes in subsequent COVID-19 waves. This would likely lead to the selection of site-specific changes that are advantageous to the virus, as has been currently observed through the pandemic.

Objectives Current developments in sequencing techniques have enabled rapid and high-throughput generation of sequence data. However, there is a growing gap between the generation of raw sequencing data and the extraction of meaningful biological information. Variant annotation is a crucial step in the analysis of genome sequencing data. Incorrect or incomplete annotations can cause researchers to dilute interesting variants in a pool of false positives. We require consistent, accurate and reliable annotation of variants for making diagnostic and treatment decisions. Current annotation depends on the set of transcripts, and software used can be managed, with sufficient care, in the research context. Careful thought needs to be given to the choice of transcript sets and software packages for variant annotation in sequencing studies. In this project, the main objective is to analyze the genetic variants observed in Pakistani population data within the 1000 genomes project (1KGP). Results We characterized only SNVs and InDels types of genetic variations, in total ~ 1.4 million variants. Besides this, we also annotated the genetic variants with multiple annotations tools, ANNOVAR and SnpEff and compared the differential results. Our population-specific catalogue will enhance future studies on the functional impact at protein level. Electronic supplementary material The online version of this article (10.1186/s13104-019-4508-5) contains supplementary material, which is available to authorized users.

Obesity is the fifth primary hazard for mortality in the world; hence different therapeutic targets are explored to overcome this problem. Endocannabinoid is identified as the emerging target for the treatment of obesity as Cannabinoid 1 (CB1) receptor over-activation resulted in abdominal obesity. Potent antagonists or inverse agonists for CB1 receptor are the new strategies to develop anti-obesity drugs. Here, ligand-based 3D-QSAR studies was performed on 100 analogues belonging to a class of 1,2,4-tirazole containing diarylpyrazolylcarboxamide as CB1 receptor antagonists. We developed three CoMFA models using different charge schemes, AM1BCC, Gasteiger-Huckle and MMFF. These models produced almost similar statistical results (q2cv = 0.725, 0.692, 0.719 and r2ncv = 0.929, 0.924, 0.928 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). The said models were validated through 20 external test set compounds which resulted in significant r2pred values (r2pred = 0.747, 0.743 and 0.745 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). Comparatively, AM1BCC model provided slightly better statistics among all three tested charges scheme models, hence AM1BCC model was further utilized to generate CoMSIA models considering different field combinations. The best selected CoMSIA model also produced substantial q2cv = 0.788, r2ncv = 0.916 and r2pred = 0.836 values. Furthermore, two new molecules were designed by modifying the same scaffolds on the basis contour map analysis. The activities of newly designed molecules were predicted through obtained CoMFA model ranked as better than their parent molecules. Moreover, these newly designed compounds were successfully docked on the complex crystal structure of CB1 receptor (PDB ID: 5XRA). The docked conformation of these newly designed inhibitor interacted with Ser173, His178, Lys192, Thr197 and Ser383 mainly by hydrophobic and pi-pi stacking interactions. The obtained results signify the potential of the developed model; suggesting that the models can be useful to test and design potent novel CB1 receptor antagonists or inverse agonists prior to the synthesis.

The maximum quantum yield of photosystem II (as reflected by variable to maximum chlorophyll a fluorescence, Fv /Fm ) is regarded as one of the most important photosynthetic parameters. The genetic basis underlying natural variation in Fv /Fm , which shows low level of variations in plants under non-stress conditions, is not easy to be exploited using the conventional gene cloning approaches. Thus, in order to answer this question, we have followed another strategy: we used genome-wide association study (GWAS) and transgenic analysis in a rice mini-core collection. We report here that four single-nucleotide polymorphisms, located in the promoter region of β-glucosidase 5 (BGlu-5), are associated with observed variation in Fv /Fm . Indeed, our transgenic analysis showed a good correlation between BGlu-5 and Fv /Fm . Thus, our work demonstrates the feasibility of using GWAS to study natural variation in Fv /Fm , suggesting that cis-element polymorphism, affecting the BGlu-5 expression level, may, indirectly, contribute to Fv /Fm variation in rice through the gibberellin signaling pathway. Further research is needed to understand the mechanism of our novel observation.

This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth., Key Points Question What maternal biological modalities are associated with preterm birth (PTB)? Findings In this diagnostic/prognostic study of 81 pregnant women from 5 birth cohorts in low- and middle-income countries, several correlates of preterm birth in urine and blood were found to be associated with PTB. Although cohort-specific signatures were present, a machine learning algorithm was able to generate a model that was capable of predicting PTB across the cohorts. Meaning Results of this study suggest that most PTBs can be predicted using blood and urine samples collected early in the pregnancy, providing opportunities for interventions., Importance Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

ObjectivesPakistan has a high infectious disease burden with about 265,000 reported cases of COVID-19. We investigated the genomic diversity of SARS-CoV-2 strains and present the first data on viruses circulating in the country.MethodsWe performed whole-genome sequencing and data analysis of SARS-CoV-2 eleven strains isolated in March and May.ResultsStrains from travelers clustered with those from China, Saudi Arabia, India, USA and Australia. Five of eight SARS-CoV-2 strains were GH clade with Spike glycoprotein D614G, Ns3 gene Q57H, and RNA dependent RNA polymerase (RdRp) P4715L mutations. Two were S (ORF8 L84S and N S202N) and three were L clade and one was an I clade strain. One GH and one L strain each displayed Orf1ab L3606F indicating further evolutionary transitions.ConclusionsThis data reveals SARS-CoV-2 strains of L, G, S and I have been circulating in Pakistan from March, at the start of the pandemic. It indicates viral diversity regarding infection in this populous region. Continuing molecular genomic surveillance of SARS-CoV-2 in the context of disease severity will be important to understand virus transmission patterns and host related determinants of COVID-19 in Pakistan.

Background: Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations. Methods: Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by Inductively coupled plasma mass spectrometry (ICP-MS) analysis. The associations between maternal Se with PTB and gestational duration were analyzed using linear and logistic regressions. The results were then combined using fixed and random effect meta-analysis. Findings: In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/ml (range: 26.1 to 228.7 ng/ml) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB (p = 0.04) and gestational duration (p = 2.9e-6) with effect size estimates of an OR= 1.04 (95% CI: 1.0 to 1.07) for PTB per 10 ng/ml decrease in Se concentration and 0.44 days (95% CI: 0.26 – 0.62) longer gestation per 10 ng/ml increase in Se concentration. However, there was a substantial heterogeneity among study cohorts. The largest effect sizes were observed in UK (Liverpool) cohort, while the most statistically significant associations were observed in samples from Malawi. After excluding these two cohorts, the fixed effect meta-analysis was no longer significant, and the random-effect meta-analysis of all data sets also did not achieve statistical significance. Interpretation: While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalize across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Targeted Se supplementation could play a role in reducing PTB in some settings, however further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations are considered. Funding Statement: Payment for access to data and article-processing charges for this publication was covered by The Bill & Melinda Gates Foundation (Grant no: OPP1175128, OPP1152451). The NEST study acknowledges the support from National Institute of Environmental Health Sciences, the US Environmental Protection Agency, the National Institute of Diabetes and Digestive and Kidney Diseases, and the Duke Cancer Institute. Th CPPOP study acknowledge support from the UCSF California Preterm Birth Initiative. The iLiNS-DYAD-M trial acknowledge the support by a grant to the University of California, Davis from The Bill & Melinda Gates Foundation [OPP49817] and a grant to the University of California, Davis from the Office of Health, Infectious Diseases, and Nutrition, Bureau for Global Health, U.S. Agency for International Development (USAID) through the Food and Nutrition Technical Assistance III Project (FANTA). MDIG, AMANHI, GAPPS and INTERBIO cohorts acknowledge the support by The Bill & Melinda Gates Foundation. Declaration of Interests: The authors declare no conflicts of interest regarding the content of this paper. Ethics Approval Statement: Our study protocol was approved by the Institute Review Board (IRB) of the CCHMC and by the corresponding Ethics Committees of each participating institution.

Ovarian Cancer (OVCA) is the most occurring gynecological cancer worldwide, often diagnosed at a later stage and ultimate results in a high death rate. To overcome this serious health concern, it is important to understand the molecular mechanisms and equally significant to identify the putative biomarkers as well as the therapeutic drug targets for the early diagnosis and treatment of OVCA. In doing so, a strategy is designed to study the most frequently diagnosed cases of OVCA called as High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines with the combination of computational biology, biostatistics and cancer informatics approaches. This study is directed to investigate the global gene expression profiling, and to perform the analyses of identified global Differently Expressed Genes (DEGs) of OVCA. The microarray dataset (GSE71524) is comprised of tumor and cell line samples of OVCA and it was used for the identification of DEGs in the current study. The STRING database was used to construct Protein-Protein Interaction (PPI) network of DEGs, and hub genes were identified by the CytoHubba. In addition, a functional enrichment analysis of up- and down-regulated DEGs was performed by a bioinformatics database called as DAVID. The microRNAs (miRNAs) and transcription factors (TFs) analyses were conducted with the aid of biological tools, MAGIA and GenCOdis3, respectively. As a result, the genes comprised of CSF1R, TYROBP, PLEK, FGR, ACLY, ACACA, LAPTM5, C1 or f162, IL10RA and CD163 were identified as hub genes. Additionally, miRNA analysis resulted in finding an association of zinc finger protein with OVCA comes out after implementing different algorithms. On the other hand, in the TFs analysis resulted in various DEGs that were enriched by NFAT, NF1 and GABP TFs. In this study, it was observed that ACACA, ACLY and CSF1R DEGs showed significant occurrence in different steps, and therefore, these genes were studied, precisely. Nevertheless, the results may help to discover the potential biomarkers with deep understanding of molecular mechanisms. However, further validation is required to explain the OVCA pathogenesis.

Additional file 2. Analysis of PJL samples variants, This file includes the analytical description (with figures and tables) of PJL variants.

Leprosy is caused by Mycobacterium leprae a major health concern in several countries of the world particularly in Asia and Africa. The preventive measurement has been adopted by the combined efforts of the leprosy burden countries and WHO. However, the situation is getting worse due to the emergence of the resistant strains of the M. leprae. The continuous efforts are underway to discover new chemical agent as a therapeutic to cure the diseases caused by the resistant pathogens of bacterial origins. The resistant pathogens are still growing on alarming rate. In order to overcome the resistant pathogens, a relatively newer approach has been applied since last decade. One of them involves the computational subtractive genomics, in which the complete proteome of the bacterial pathogen is step-wise reduced to few potential drug targets. The steps include the finding of non-host proteins, essentiality of the proteins to the pathogens and involvement of the shortlisted proteins in essential metabolic pathways of the pathogen, which are necessary for the bacterial survival. In the current study, we applied computational subtractive genomics on complete proteome of the M. leprae and ended up with 16 hypothetical proteins as potential drug targets against which new active molecules can be proposed to ameliorate the activity to cure the disease associated with them. The study is innovative and has a potential to improve the research directions in unraveling the novel cure of leprosy.

Summary Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon. Applied to the whole exome dataset of 573 individuals, MACARON identifies 114 situations where multiple SNVs within a genetic codon induce an amino acid change that is different from those predicted by standard single SNV annotation tool. Such events are not uncommon and deserve to be studied in sequencing projects with inconclusive findings. Availability and implementation MACARON is written in python with codes available on the GENMED website (www.genmed.fr). Supplementary information Supplementary data are available at Bioinformatics online.

This docx file contains supplementary material about PJL population from 1000 genomes Project.

The objective is to assess the awareness level and practice for the consumption of expired pharmaceuticals in general public of Karachi, Pakistan. In this regard, a cross-sectional study was conducted and completed from December 2013 to September 2014. Self-administered study instrument was used which consisted of total 11 variables regarding the assessment of participants’ perspective on product expiry and its usage was distributed to 120 participants. Data were analyzed in SPSS version16.0. Primarily descriptive nature was used while frequency percentages of respondents were computed for categorical variables. Mean ± SD were computed for assessment of knowledge scores. Overall response rate was 83.33% in which 74 male and 26 females filled the study instrument. Average scores of respondents’ awareness regarding expiry of pharmaceutical products was 4.62 ± 0.75. The qualification of respondents includes the graduates’ count (64.9% male and 65.4% females) while 16.2% male and 23.1% female were postgraduate. Response distribution of respondents showed that 93.2% male and 92.3% female were aware about the product expiry concept. Similarly, 87.8% male and 96.2% female were in favor for their non-utilization, as 89.2% male and 96.2% female reported its severe harmful effects. Overall, 87.8% male and 76.9% female were interested to discard such products. Interestingly, 82.4% male 73.1% female were in full opinion to check the product expiry date before its purchase whereas, 78.4% male and 92.3% female have their opinion to purchase long expiry products. Box plot regarding products preference and total aggregated knowledge scores showed consistency. Results concluded that the knowledge, responses and practices of participants were in favor for the non-utilization of expired pharmaceuticals.

Class II major histocompatibility complex (MHC II) molecules as expressed by antigen-presenting cells are heterodimeric cell-surface glycoprotein receptors that are fundamental in initiating and propagating an immune response by presenting tumor-associated antigenic peptides to CD4(+)/T(H) cells. The loading efficiency of such peptides can be improved by small organic compounds (MHC Loading Enhancers-MLEs), that convert the non-receptive peptide conformation of MHC II to a peptide-receptive conformation. In a reversible reaction, these compounds open up the binding site of MHC II molecules by specific interactions with a yet undefined pocket. Here, we performed molecular docking and molecular dynamics simulation studies of adamantyl compounds on the predicted cavity around the P1 pocket of 2 allelic variants of HLA-DRs. The purpose was to investigate the suitability of adamantyl compounds as MLEs at the dimorphic β86 position. Docking studies revealed that besides numerous molecular interactions formed by the adamantyl compounds, Asnβ82, Tyrβ83, and Thrβ90 are the crucial amino acid residues that are characterized as the "sensors" of peptide loading. Molecular dynamics simulation studies exposed the dynamical structural changes that HLA-DRs adopted as a response to binding of 3-(1-adamantyl)-5-hydrazidocarbonyl-1H-pyrazole (AdCaPy). The conformations of AdCaPy complexed with the Glyβ86 HLA-DR allelic variant are well correlated with the stabilized form of peptide-loaded HLA-DRs, further confirming the role of AdCaPy as a MLE. Hydrogen bonding interaction analysis clearly demonstrated that after making suitable contacts with AdCaPy, HLA-DR changes its local conformation. However, AdCaPy complexed with HLA-DR having Valβ86 at the dimorphic position did not accommodate AdCaPy as MLE due to steric hindrance caused by the valine.

Dilated cardiomyopathy is a disease of left ventricular dysfunction accompanied by impairment of the β1-adrenergic receptor (β1-AR) signal cascade. The disturbed β1-AR function may be based on an elevated sympathetic tone observed in patients with heart failure. Prolonged adrenergic stimulation may induce metabolic and electrophysiological disturbances in the myocardium, resulting in tachyarrhythmia that leads to the development of heart failure in human and sudden death. Hence, β1-AR is considered as a promising drug target but attempts to develop effective and specific drug against this tempting pharmaceutical target is slowed down due to the lack of 3D structure of Homo sapiens β1-AR (hsβADR1). This study encompasses elucidation of 3D structural and physicochemical properties of hsβADR1 via threading-based homology modeling. Furthermore, the docking performance of several docking programs including Surflex-Dock, FRED, and GOLD were validated by re-docking and cross-docking experiments. GOLD and Surflex-Dock performed best in re-docking and cross docking experiments, respectively. Consequently, Surflex-Dock was used to predict the binding modes of four hsβADR1 agonists. This study provides clear understanding of hsβADR1 structure and its binding mechanism, thus help in providing the remedial solutions of cardiovascular, effective treatment of asthma and other diseases caused by malfunctioning of the target protein.

Multiple Drug Resistant (MDR) bacteria are no more inhibited by the front line antibiotics due to extreme resistance. Methicillin Resistant Staphylococcus aureus (MRSA) is one of the MDR pathogens notorious for its widespread infection around the world. The high resistance acquired by MRSA needs a serious concern and efforts should be carried out for the discovery of better therapeutics. With this aim, we designed a comparison of the metabolic pathways of the pathogen, MRSA strain 252 (MRSA252) with the human host (i.e., Homo sapiens) by using well-established in silico methods. We identified several metabolic pathways unique to MRSA (i.e., absent in the human host). Furthermore, a subtractive genomics analysis approach was applied for retrieval of proteins only from the unique metabolic pathways. Subsequently, proteins of unique MRSA pathways were compared with the host proteins. As a result, we have shortlisted few unique and essential proteins that could act as drug targets against MRSA. We further assessed the druggability potential of the shortlisted targets by comparing them with the DrugBank Database (DBD). The identified drug targets could be useful for an effective drug discovery phase. We also searched the sequences of unique as well as essential enzymes from MRSA in Protein Data Bank (PDB). We shortlisted at least 12 enzymes for which there was no corresponding deposition in PDB, reflecting that their crystal structures are yet to be solved! We selected Glutamate synthase out of those 12 enzymes owing to its participation in significant metabolic pathways of the pathogen e.g., Alanine, Aspartate, Glutamate and Nitrogen metabolism and its evident suitability as drug target among other MDR bacteria e.g., Mycobacteria. Due to the unavailability of any crystal structure of Glutamate synthase in PDB, we generated the 3D structure by homology modeling. The modeled structure was validated by multiple analysis tools. The active site of Glutamate synthase was identified by not only superimposing the template structure (PDB ID: 1E0A) over each other but also by the Parallel-ProBiS algorithm. The identified active site was further validated by cross-docking the co-crystallized ligand (2-oxoglutaric acid; AKG) of PDB ID: 1LLW. It was concluded that the comparative metabolic in silico analysis together with structure-based methods provides an effective approach for the identification of novel antibiotic targets against MRSA.

The kinome is a protein kinase complement of the human genome, categorized as serine/threonine and tyrosine kinases. These kinases catalyze phosphorylation reaction by using ATP as phosphoryl donor. Proviral Integration Site for Moloney Murine Leukemia Virus (PIM) kinase encodes serine/threonine protein kinases that recognized as proto-oncogene, responsible for rapid growth of cancerous cells. It is implicated in cell survival and function via cell cycle progression and its metabolism. PIM-3, sub-member of PIM kinases is a proto-oncogene, its overexpression inhibits apoptosis, and results in progression of hepatocellular carcinoma. PIM-3 is considered as a promising drug target but attempts to develop its specific inhibitors is slowed down due to the lack of 3D structure by any experimental technique. In silico techniques generally facilitate scientist to explore hidden structural features in order to improve drug discovery. In the present study, homology modeling, molecular docking and MD simulation techniques were utilized to explore the structure and dynamics of PIM-3 kinase. Induction of water molecules during molecular docking simulation explored differences in the hinge region between PIM-1 and PIM-3 kinases that may be responsible for specificity. Furthermore, role of water molecules in the active site was also explored via radial distribution function (RDF) after a 10 ns molecular dynamics (MD) simulations. Generated RDF plots exhibited the importance of water for inhibitor binding through their bridging capability that links the ligand with binding site residues.

Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions. We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source. We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors. European Commission Science Foundation Ireland University College Dublin

A novel series of anticytokine N-benzyl pyridinone derivatives that targets p38α MAP kinase has been analyzed by utilizing a combination of molecular modeling techniques. Statistically significant structure-based 3D-QSAR models were generated for both CoMFA and CoMSIA, and validated through acceptable predictive ability to support both internal and external set of compounds. Structural changes within the protein key backbone residues (Met109 and Gly110), DFG loop position, and side chain movements (Lys53 and Asn114) as resulted by different substituents on these inhibitors were also examined by molecular dynamics simulation. The protocol applied in this study could be helpful to rationalize potent compounds with better inhibitory activity and selectivity profiles against p38α MAP kinase.

Background Alzheimer's disease, known to be associated with the gradual loss of memory, is characterized by low concentration of acetylcholine in the hippocampus and cortex part of the brain. Inhibition of acetylcholinesterase has successfully been used as a drug target to treat Alzheimer's disease but drug resistance shown by butyrylcholinesterase remains a matter of concern in treating Alzheimer's disease. Apart from the many other reasons for Alzheimer's disease, its association with the genesis of fibrils by β-amyloid plaques is closely related to the increased activity of butyrylcholinesterase. Although few data are available on the inhibition of butyrylcholinesterase, studies have shown that that butyrylcholinesterase is a genetically validated drug target and its selective inhibition reduces the formation of β-amyloid plaques. Rationale We previously reported the inhibition of cholinesterases by 2,3-dihydro-1, 5-benzothiazepines, and considered this class of compounds as promising inhibitors for the cure of Alzheimer's disease. One compound from the same series, when substituted with a hydroxy group at C-3 in ring A and 2-thienyl moiety as ring B, showed greater activity against butyrylcholinesterase than to acetylcholinesterase. To provide insight into the binding mode of this compound (Compound A), molecular docking in combination with molecular dynamics simulation of 5000 ps in an explicit solvent system was carried out for both cholinesterases. Conclusion Molecular docking studies revealed that the potential of Compound A to inhibit cholinesterases was attributable to the cumulative effects of strong hydrogen bonds, cationic-π, π-π interactions and hydrophobic interactions. A comparison of the docking results of Compound A against both cholinesterases showed that amino acid residues in different sub-sites were engaged to stabilize the docked complex. The relatively high affinity of Compound A for butyrylcholinesterase was due to the additional hydrophobic interaction between the 2-thiophene moiety of Compound A and Ile69. The involvement of one catalytic triad residue (His438) of butyrylcholinesterase with the 3'-hydroxy group on ring A increases the selectivity of Compound A. C-C bond rotation around ring A also stabilizes and enhances the interaction of Compound A with butyrylcholinesterase. Furthermore, the classical network of hydrogen bonding interactions as formed by the catalytic triad of butyrylcholinesterase is disturbed by Compound A. This study may open a new avenue for structure-based drug design for Alzheimer's disease by considering the 3D-pharmacophoric features of the complex responsible for discriminating these two closely-related cholinesterases.