Combining machine learning and structure-based approaches to develop oncogene PIM kinase inhibitors.

Introduction: PIM kinases are targets for therapeutic intervention since they are associated with a number of malignancies by boosting cell survival and proliferation. Over the past years, the rate of new PIM inhibitors discovery has increased significantly, however, new generation of potent molecules with the right pharmacologic profiles were in demand that can probably lead to the development of Pim kinase inhibitors that are effective against human cancer. Method: In the current study, a machine learning and structure based approaches were used to generate novel and effective chemical therapeutics for PIM-1 kinase. Four different machine learning methods, namely, support vector machine, random forest, k-nearest neighbour and XGBoost have been used for the development of models. Total, 54 Descriptors have been selected using the Boruta method. Results: SVM, Random Forest and XGBoost shows better performance as compared to k-NN. An ensemble approach was implemented and, finally, four potential molecules (CHEMBL303779, CHEMBL690270, MHC07198, and CHEMBL748285) were found to be effective for the modulation of PIM-1 activity. Molecular docking and molecular dynamic simulation corroborated the potentiality of the selected molecules. The molecular dynamics (MD) simulation study indicated the stability between protein and ligands. Discussion: Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery against PIM kinase.

Identification and functional characterization of two rare LDLR stop gain variants (p.C231* and p.R744*) in Saudi familial hypercholesterolemia patients.

BACKGROUND: Familial hypercholesterolemia (FH) is a globally underdiagnosed inherited metabolic disorder. Owing to limited published data from Arab world, this study was conducted with the aim of identifying the genetic and molecular basis of FH in highly consanguineous Saudi population. METHODS: We performed clinical screening, biochemical profiling, whole exome sequencing and variant segregation analysis of two Saudi FH families. Additionally, 500 normolipic individuals were screened to ensure the absence of FH variant in general Saudi population. Functional characterization of FH variants on secondary structure characteristics of RNA and protein molecules was performed using different bioinformatics modelling approaches. RESULTS: WES analysis identified two independent rare LDLR gene stop gain variants (p.C231* and p.R744*) consistent to the clinical presentation of FH patients from two different families. RNAfold analysis has shown that both variants were predicted to disturb the free energy dynamics of LDLR mRNA molecule and destabilize its folding pattern and function. PSIPRED based structural modelling analysis has suggested that both variants bring drastic changes disturbing the secondary structural elements of LDLR molecule. The p.C231* and p.R744* variants are responsible for partial or no protein product, thus they are class 1 variants causing loss of function (LoF) LDLR variants. CONCLUSIONS: This study highlights the effectiveness of the WES, sanger sequencing, and computational analysis in expanding FH variant spectrum in culturally distinct populations like Saudi Arabia. Genetic testing of FH patients is very essential in better clinical diagnosis, screening, treatment, and management and prevention of cardiovascular disease burden in the society.

A comparative mRNA- and miRNA transcriptomics reveals novel molecular signatures associated with metastatic prostate cancers.

Background: Prostate cancer (PC) is a fatally aggressive urogenital cancer killing millions of men, globally. Thus, this study aims to identify key miRNAs, target genes, and drug targets associated with prostate cancer metastasis. Methods: The miRNA and mRNA expression datasets of 148 prostate tissue biopsies (39 tumours and 109 normal tissues), were analysed by differential gene expression analysis, protein interactome mapping, biological pathway analysis, miRNA-mRNA networking, drug target analysis, and survival curve analysis. Results: The dysregulated expression of 53 miRNAs and their 250 target genes involved in Hedgehog, ErbB, and cAMP signalling pathways connected to cell growth, migration, and proliferation of prostate cancer cells was detected. The subsequent miRNA-mRNA network and expression status analysis have helped us in narrowing down their number to 3 hub miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p, and hsa-miR-582-5p) and 9 hub genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2). Further investigations with different systems biology methods have prioritized NR3C1, ABHD2, and GSK3B as potential genes involved in prostate cancer metastasis owing to their high mutation load and expression status. Interestingly, down regulation of NR3C1 seems to improve the prostate cancer patient survival rate beyond 150 months. The NR3C1, ABHD2, and GSK3B genes are predicted to be targeted by hsa-miR-582-5p, besides some antibodies, PROTACs and inhibitory molecules. Conclusion: This study identified key miRNAs (miR-548c-3p and miR-582-5p) and target genes (NR3C1, ABHD2, and GSK3B) as potential biomarkers for metastatic prostate cancers from large-scale gene expression data using systems biology approaches.

Structural characterization and conformational dynamics of alpha-1 antitrypsin pathogenic variants causing alpha-1-antitrypsin deficiency.

Background: Alpha-1 antitrypsin deficiency (A1ATD) is a progressive lung disease caused by inherited pathogenic variants in the SERPINA1 gene. However, their actual role in maintenance of structural and functional characteristics of the corresponding α-1 anti-trypsin (A1AT) protein is not well characterized. Methods: The A1ATD causative SERPINA1 missense variants were initially collected from variant databases, and they were filtered based on their pathogenicity potential. Then, the tertiary protein models were constructed and the impact of individual variants on secondary structure, stability, protein-protein interactions, and molecular dynamic (MD) features of the A1AT protein was studied using diverse computational methods. Results: We identified that A1ATD linked SERPINA1 missense variants like F76S, S77F, L278P, E288V, G216C, and H358R are highly deleterious as per the consensual prediction scores of SIFT, PolyPhen, FATHMM, M-CAP and REVEL computational methods. All these variants were predicted to alter free energy dynamics and destabilize the A1AT protein. These variants were seen to cause minor structural drifts at residue level (RMSD = <2Å) of the protein. Interestingly, S77F and L278P variants subtly alter the size of secondary structural elements like beta pleated sheets and loops. The residue level fluctuations at 100 ns simulation confirm the highly damaging structural consequences of all the six missense variants on the conformation dynamics of the A1AT protein. Moreover, these variants were also predicted to cause functional deformities by negatively impacting the binding energy of A1AT protein with NE ligand molecule. Conclusion: This study adds a new computational biology dimension to interpret the genotype-protein phenotype relationship between SERPINA1 pathogenic variants with its structural plasticity and functional behavior with NE ligand molecule contributing to the Alpha-1-antitrypsin deficiency. Our results support that A1ATD complications correlates with the conformational flexibility and its propensity of A1AT protein polymerization when misfolded.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer.

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Identification of miRNA-mRNA-TFs regulatory network and crucial pathways involved in asthma through advanced systems biology approaches.

Asthma is a life-threatening and chronic inflammatory lung disease that is posing a true global health challenge. The genetic basis of the disease is fairly well examined. However, the molecular crosstalk between microRNAs (miRNAs), target genes, and transcription factors (TFs) networks and their contribution to disease pathogenesis and progression is not well explored. Therefore, this study was aimed at dissecting the molecular network between mRNAs, miRNAs, and TFs using robust computational biology approaches. The transcriptomic data of bronchial epithelial cells of severe asthma patients and healthy controls was studied by different systems biology approaches like differentially expressed gene detection, functional enrichment, miRNA-target gene pairing, and mRNA-miRNA-TF molecular networking. We detected the differential expression of 1703 (673 up-and 1030 down-regulated) genes and 71 (41 up-and 30 down-regulated) miRNAs in the bronchial epithelial cells of asthma patients. The DEGs were found to be enriched in key pathways like IL-17 signaling (KEGG: 04657), Th1 and Th2 cell differentiation (KEGG: 04658), and the Th17 cell differentiation (KEGG: 04659) (p-values = 0.001). The results from miRNAs-target gene pairs-transcription factors (TFs) have detected the key roles of 3 miRs (miR-181a-2-3p; miR-203a-3p; miR-335-5p), 6 TFs (TFAM, FOXO1, GFI1, IRF2, SOX9, and HLF) and 32 miRNA target genes in eliciting autoimmune reactions in bronchial epithelial cells of the respiratory tract. Through systemic implementation of comprehensive system biology tools, this study has identified key miRNAs, TFs, and miRNA target gene pairs as potential tissue-based asthma biomarkers.

Granulomatous hepatitis in a Saudi child with IL2RA defect: a case report and literature review.

Interleukin-2 receptor alpha (IL2RA) defect (OMIM- # 606367) is an immune disease where affected patients are vulnerable to developing recurrent microbial infections in addition to lymphadenopathy and dermatological manifestations. This condition is known to be caused by pathogenic variants in the IL2RA gene, which are inherited in an autosomal recessive fashion. In this case report, we present a patient with IL2RA defect from Saudi Arabia who presented with chronic diarrhea, poor weight gain, mild villous atrophy, malnutrition, hepatomegaly, nonspecific inflammation, and an eczematous skin rash. His genetic analysis revealed a novel, homozygous, and likely pathogenic variant, that is, c.504 C>A (Cys168Ter), located in the exon 4of the IL2RA gene, which was inherited from his parents in an autosomal recessive mode of inheritance. This variant produces a 272-amino-acid shorter IL2RA protein chain, which most likely becomes degraded in the cytosol. Thus, we assume that the c.504 C>A is a null allele that abolishes the synthesis of IL2RA, malforms the IL-2 receptor complex, and eventually causes immunodeficiency manifestations. To our knowledge, this is the first time a person with IL2RA defect has shown signs of granulomatous hepatitis on a liver biopsy.

Integrative global co-expression analysis identifies key microRNA-target gene networks as key blood biomarkers for obesity.

BACKGROUND: Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. METHODS: In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. RESULTS: The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. CONCLUSIONS: This study identifies potential obesity related blood biomarkers from large-scale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes.

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

Computational approaches for discovering significant microRNAs, microRNA-mRNA regulatory pathways, and therapeutic protein targets in endometrial cancer.

Endometrial cancer (EC) is a urogenital cancer affecting millions of post-menopausal women, globally. This study aims to identify key miRNAs, target genes, and drug targets associated with EC metastasis. The global miRNA and mRNA expression datasets of endometrial tissue biopsies (24 tumors +3 healthy tissues for mRNA and 18 tumor +4 healthy tissues for miRNAs), were extensively analyzed by mapping of DEGs, DEMi, biological pathway enrichment, miRNA-mRNA networking, drug target identification, and survival curve output for differentially expressed genes. Our results reveal the dysregulated expression of 26 miRNAs and their 66 target genes involved in focal adhesions, p53 signaling pathway, ECM-receptor interaction, Hedgehog signaling pathway, fat digestion and absorption, glioma as well as retinol metabolism involved in cell growth, migration, and proliferation of endometrial cancer cells. The subsequent miRNA-mRNA network and expression status analysis have narrowed down to 2 hub miRNAs (hsa-mir-200a, hsa-mir-429) and 6 hub genes (PTCH1, FOSB, PDGFRA, CCND2, ABL1, ALDH1A1). Further investigations with different systems biology methods have prioritized ALDH1A1, ABL1 and CCND2 as potential genes involved in endometrial cancer metastasis owing to their high mutation load and expression status. Interestingly, overexpression of PTCH1, ABL1 and FOSB genes are reported to be associated with a low survival rate among cancer patients. The upregulated hsa-mir-200a-b is associated with the decreased expression of the PTCH1, CCND2, PDGFRA, FOSB and ABL1 genes in endometrial cancer tissue while hsa-mir-429 is correlated with the decreased expression of the ALDH1A1 gene, besides some antibodies, PROTACs and inhibitory molecules. In conclusion, this study identified key miRNAs (hsa-mir-200a, hsa-mir-429) and target genes ALDH1A1, ABL1 and CCND2 as potential biomarkers for metastatic endometrial cancers from large-scale gene expression data using systems biology approaches.

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches.

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.

Molecular profiling of lamellar ichthyosis pathogenic missense mutations on the structural and stability aspects of TGM1 protein.

Lamellar ichthyosis (LI) is a rare inherited disease where affected infants present a extensive skin scaling characterized by hyperkeratosis. Inherited mutations in the Transglutaminase 1 (TGM1) protein is one of the known causative genetic factor for the LI. The main objective of this study is to explore the impact of LI causative missense mutations on the structural and stability aspects of TGM1 protein using structural modeling, molecular docking and molecular dynamics approaches. By testing all LI causative TMG1 mutations against multiple stability prediction methods, we found that L362R and L388P mutations positioned in the Transglut_core domain were most destabilizing to the stability of TGM1 protein. These 2 mutations were 3D protein modeled and further analyzed by molecular docking and dynamic simulation methods. Molecular docking of these TGM1 mutant structures with chitosan, a natural polyphenolic compound and known inducer for transglutaminase enzyme, has shown stable molecular interactions between the native TGM1-chitosan and TGM1(L388P)-chitosan complex, when compared to the TGM1(L362R)-chitosan complex. Interestingly, molecular dynamics analysis have also yielded similar findings, where L388P-chitosan complex is shown to develop B-sheets and attain better stability, whereas TGM1-L362R complex possessed coils over the simulation period, pointing its highly destabilizing behavior on the protein structure. This study concludes that missense mutations in Transglut_core domain of the TGM1 are deleterious to the stability and structural changes of TGM1 protein and also suggest that chitosan molecule could act as a natural activator against few pathogenic TGM1 mutations. Communicated by Ramaswamy H. Sarma.

Molecular insights into the coding region mutations of low-density lipoprotein receptor adaptor protein 1 (LDLRAP1) linked to familial hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a lipid disorder caused by pathogenic mutations in LDLRAP1 gene. The present study has aimed to deepen our understanding about the pathogenicity predictions of FH causative genetic mutations, as well as their relationship to phenotype changes in LDLRAP1 protein, by utilizing multidirectional computational analysis. METHODS: FH linked LDLRAP1 mutations were mined from databases, and the prediction ability of several pathogenicity classifiers against these clinical variants, was assessed through different statistical measures. Furthermore, these mutations were 3D modelled in protein structures to assess their impact on protein phenotype changes. RESULTS: Our findings suggest that Polyphen-2, when compared with SIFT, M-CAP and CADD tools, can make better pathogenicity predictions for FH causative LDLRAP1 mutations. Through, 3D simulation and superimposition analysis of LDLRAP1 protein structures, it was found that missense mutations do not create any gross changes in the protein structure, although they could induce subtle structural changes at the level of amino acid residues. Near native molecular dynamic analysis revealed that missense mutations could induce variable degrees of fluctuation differences guiding the protein flexibility. Stability analysis showed that most missense mutations shifts the free energy equilibrium and hence they destabilize the protein. Molecular docking analysis demonstrates the molecular shifts in hydrogen and ionic bonds and Van der waals bonding properties, which further cause differences in the binding energy of LDLR-LDLRAP1 proteins. CONCLUSIONS: The diverse computational approaches used in the present study may provide a new dimension for exploring the structure-function relationship of the novel and deleterious LDLRAP1 mutations linked to FH.

The genetic association study of TP53 polymorphisms in Saudi obese patients.

Obesity is a multifactorial metabolic disorder characterized by low grade chronic inflammation. Rare and novel mutations in genes which are vital in several key pathways have been reported to alter the energy expenditure which regulates body weight. The TP53 or p53 gene plays a prominent role in regulating various metabolic activities such as glycolysis, lipolysis, and glycogen synthesis. Recent genome-wide association studies reported that tumor suppressor gene p53 variants play a critical role in the predisposition of type 2 diabetes and obesity. Till date, no reports are available from the Arabian population; hence the present study was intended to assess the association between p53 variants with risk of obesity development in the Saudi population. We have selected three p53 polymorphisms, rs1642785 (C > G), and rs9894946 (A > G), and rs1042522 (Pro72Arg; C > G) and assessed their association with obesity risk in the Saudi population. Phenotypic and biochemical parameters were also evaluated to check their association with p53 genotypes and obesity. Genotyping was carried out on 136 obese and 122 normal samples. We observed that there is significantly increased prevalence p52 Pro72Arg (rs1042522) polymorphism in obese persons when compared to controls at GG genotype in overall comparison (OR: 2.169, 95% CI: 1.086-4.334, p = 0.02716). Male obese subjects showed three-fold higher risk at GG genotype (OR: 3.275, 95% CI: 1.230-8.716, p = 0.01560) and two-fold risk at G allele (OR: 1.827, 95% CI: 1.128-2.958, p = 0.01388) of p53 variant Pro72Arg respectively. This variant has also shown significant influence on cholesterol, LDL level, and random insulin levels in obese subjects (p ≤ 0.05). In conclusion, p53 Pro72Arg variant is highly prevalent among obese individuals and may act as a genetic modifier for obesity development among Saudis.

Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network.

Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.

Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR.

The somatic mutations in ATP binding cleft of the tyrosine kinase binding domain of EGFR are known to occur in 15-40% of non-small cell lung cancer (NSCLC) patients. Although first and second generation anti-EGFR inhibitors are widely used to treat these patients, their therapeutic efficacy is modest and often results in adverse effects or drug resistance. Therefore, there is a need to develop novel as well as safe anti-EGFR drugs. The rapid emergence of computational drug designing provided a great opportunity to both discover and predict the efficacy of novel EGFR inhibitors from plant sources. In the present study, we designed several chemical analogues of edible curcumin (CUCM) compound and assessed their drug likeliness, ADME and toxicity properties using a diverse range of advanced computational methods. We also have examined the structural plasticity and binding characteristics of EGFR wild-type and mutant forms (S769L and K846R) against ligand molecules like Gefitinib, native CUCM, and different CUCM analogues. Through multidimensional experimental approaches, we conclude that CUCM-36 ((1E,4Z,6E)-1-(3,4-Diphenoxyphenyl)-5-hydroxy-7-(4-hydroxy-3-phenoxyphenyl)-1,4,6-heptatrien-3-one) is the best anti-EGFR compound with high drug-likeness, ADME properties, and low toxicity properties. CUCM-36 compound has demonstrated better affinity towards both wild-type (ΔG is -8.5 kcal/Mol) and mutant forms (V769L & K846R; ΔG for both is >-9.20 kcal/Mol) compared to natural CUCM and Gefitinib inhibitor. This study advises the future laboratory assays to develop CUCM-36 as a novel drug compound for treating EGFR positive non-small cell lung cancer patients.

Computational Molecular Phenotypic Analysis of PTPN22 (W620R), IL6R (D358A), and TYK2 (P1104A) Gene Mutations of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder of bone joints caused by the complex interplay between several factors like body physiology, the environment with genetic background. The recent meta-analysis of GWAS has expanded the total number of RA-associated loci to more than 100, out of which approximately ∼97% (98 variants) loci are located in non-coding regions, and the other ∼3% (3 variants) are in three different non-HLA genes, i.e., TYK2 (Prp1104Ala), IL6R (Asp358Ala), and PTPN22 (Trp620Arg). However, whether these variants prompt changes in the protein phenotype with regards to its stability, structure, and interaction with other molecules, remains unknown. Thus, we selected the three clinically pathogenic variants described above, as positive controls and applied diverse computational methods to scrutinize if those mutations cause changes in the protein phenotype. Both wild type and mutant protein structures of PTPN22 (W620R), IL6R (D358A), and TYK2 (P1104A) were modeled and studied for structural deviations. Furthermore, we have also studied the secondary structure characteristics, solvent accessibility and stability, and the molecular interaction deformities caused by the amino acid substitutions. We observed that simple nucleotide predictions of SIFT, PolyPhen, CADD and FATHMM yields mixed findings in screening the RA-missense variants which showed a ≥P-value threshold of 5 × 10-8 in genome wide association studies. However, structure-based analysis confirms that mutant structures shows subtle but significant changes at their core regions, but their functional domains seems to lose wild type like functional interaction. Our findings suggest that the multidirectional computational analysis of clinically potential RA-mutations could act as a primary screening step before undertaking functional biology assays.

Computational Protein Phenotype Characterization of IL10RA Mutations Causative to Early Onset Inflammatory Bowel Disease (IBD).

The deleterious amino acid substitution mutations in IL-10 receptor alpha gene are most frequently reported in several autoimmune diseases including early onset-inflammatory bowel disease (IBD). Despite the important role of IL-10 RA in maintaining immune homeostasis, the specific structural and functional implications of these mutations on protein phenotype, stability, ligand binding and post translational characteristics is not well explored. Therefore, this study performed the multidimensional computational analysis of IL10RA missense variations causative to pediatric or early onset inflammatory bowel disease (<5 years of age). Our computational algorithmic screening identified the deleterious nature of p. W45G, p. Y57C, p. W69G, p.T84I, p.Y91C, p.R101W, p.R117C, and p.R117H, IBD causative IL10-RA mutations. The sensitivity and specificity analysis of different computational methods showed that CADD outperform SIFT, PolyPhen 2.0, FATHMM, LRT, MetaLR, MetaSVM, PROVEAN and Condel in predicting the pathogenicity of IL10RA mutations. Our three-dimensional protein modeling assays showed that the point mutations cause major drifts in the structural plasticity of IL10 RA molecule and negatively influence its stability. Findings from molecular docking analysis have shown that these point mutations decrease the binding affinity of IL10RA toward IL10 and may likely to disturb the IL10 signaling pathway. This study provides an easy frame work for phenotypic characterization of mutant IL10RA molecule in terms of structure, flexibility and stability aspects. Our approach may also add a new dimension to conventional functional biology assays in quickly studying IL10 RA mutations and also for designing and developing inhibitors for mutant IL10RA molecule.

Ursolic acid rich Ocimum sanctum L leaf extract loaded nanostructured lipid carriers ameliorate adjuvant induced arthritis in rats by inhibition of COX-1, COX-2, TNF-α and IL-1: Pharmacological and docking studies.

BACKGROUND: Ursolic acid (UA) is a promising molecule with anti-inflammatory, analgesic and potential anti-arthritic activity. METHODS: This study was undertaken to make formulation and evaluation of Ocimum sanctum L. leaf extract (OLE) loaded nano-structured lipid carriers (OLE-NLCs) for improved transdermal delivery of UA. Different surfactants, solid lipids and liquid lipids were used for the preparation of NLCs. The NLCs were developed using emulsion solvent diffusion and evaporation method. Different physicochemical properties, entrapment efficacy, in vitro release evaluation, and ex vivo permeation studies of the prepared NLCs were carried out. The in vivo anti-arthritic activity of OLE-loaded NLC gel and control gel formulation (OLE free NLC gel) against Complete Freund's Adjuvant (CFA) induced arthritis in wister albino rats was also carried out. RESULTS: OLE-NLCs were composed of spherical particles having a mean particle size of ~120 nm, polydispersity index of ~0.162 and zeta potential of ~ -27 mV. The high entrapment efficiency (EE) of UA ~89.56% was attained. The in vitro release study demonstrated a prolonged release of UA from the NLCs up to 12 h. The developed formulation was found to be significantly better with respect to the drug permeation amount with an enhancement ratio of 2.69 as compared with marketed formulation. The in vivo biological activity investigations, studies showed that the newly prepared NLCs formulation of OLE showed excellent anti-arthritic activity and the results were found at par with standard marketed diclofenac gel for its analgesic and anti-arthritic activities. These results were also supported by radiological analysis and molecular docking studies. CONCLUSION: The overall results proved that the prepared OLE-NLCs were very effective for the treatment of arthritis and the results were found at par with standard marketed the standard formulation of diclofenac gel.

Expanded Somatic Mutation Spectrum of MED12 Gene in Uterine Leiomyomas of Saudi Arabian Women.

MED12, a subunit of mediator complex genes is known to harbor genetic mutations, (mostly in exon 2), causal to the genesis of uterine leiomyomas among Caucasian, African American, and Asian women. However, the precise relationship between genetic mutations vs. protein or disease phenotype is not well-explained. Therefore, we sought to replicate the MED12 mutation frequency in leiomyomas of Saudi Arabian women, who represents ethnically and culturally distinct population. We performed molecular screening of MED12 gene (in 308 chromosomes belonging to 154 uterine biopsies), analyzed the genotype-disease phenotype correlations and determined the biophysical characteristics of mutated protein through diverse computational approaches. We discovered that >44% (34/77) leiomyomas of Arab women carry a spectrum of MED12 mutations (30 missense, 1 splice site, and 3 indels). In addition to known codon 44, we observed novel somatic mutations in codons 36, 38, and 55. Most genetically mutated tumors (27/30; 90%) demonstrated only one type of genetic change, highlighting that even single allele change in MED12 can have profound impact in transforming the normal uterine myometrium to leiomyomas. An interesting inverse correlation between tumor size and LH is observed when tumor is positive to MED12 mutation (p < 0.05). Our computational investigations suggest that amino acid substitution mutations in exon-2 region of MED12 might contribute to potential alterations in phenotype as well as the stability of MED12 protein. Our study, being the first one from Arab world, confirms the previous findings that somatic MED12 mutations are critical to development and progression of uterine leiomyomas irrespective of the ethnic background. We recommend that mutation screening, particularly codon 44 of MED12 can assist in molecular diagnostics of uterine leiomyomas in majority of the patients.