Network analysis of transcriptomics data for the prediction and prioritization of membrane-associated biomarkers for idiopathic pulmonary fibrosis (IPF) by bioinformatics approach.

Idiopathic pulmonary fibrosis (IPF) is a rare yet crucial persistent lung disorder that actuates scarring of lung tissues, which makes breathing difficult. Smoking, environmental pollution, and certain viral infections could initiate lung scarring. However, the molecular mechanism involved in IPF remains elusive. To develop an efficient therapeutic arsenal against IPF, it is vital to understand the pathology and deviations in biochemical pathways that lead to disorder. In this study, we availed network analysis and other computational pipelines to delineate the prominent membrane proteins as diagnostic biomarkers and therapeutic targets for IPF. This study yielded a significant role of glycosaminoglycan binding, endothelin, and GABA-B receptor signaling pathway in IPF pathogenesis. Furthermore, ADCY8, CRH, FGB, GPR17, MCHR1, NMUR1, and SAA1 genes were found to be immensely involved with IPF, and the enrichment pathway analysis suggests that most of the pathways were corresponding to membrane transport and signal transduction functionalities. This analysis could help in better understanding the molecular mechanism behind IPF to develop an efficient therapeutic target or biomarkers for IPF.

Effect of novel leukemia mutations (K75E & E222K) on interferon regulatory factor 1 and its interaction with DNA: insights from molecular dynamics simulations and docking studies.

Interferon regulatory factor 1 (IRF-1) plays a vital role in cell proliferation and cell differentiation by acting as a tumor suppressor gene and its role is linked to various types of cancers, including leukemia and pre-leukemia myelodysplasia. Mutations in the coding region of the IRF-1 are likely to influence the IRF-1 and its DNA binding affinity. The molecular mechanism of the DNA recognition with the IRF-1 protein upon mutations is still unknown. In this study, we have elucidated the structural and functional behavior of the wild-type and mutant (K75E and E222K) IRF-1 proteins and their corresponding molecular mechanisms with DNA recognition at the molecular level, using molecular dynamics simulations. Furthermore, we also applied the docking approach to examine the binding between the IRF-1 protein and DNA upon mutations. This study evidently explains that, due to mutations, the IRF-1 structure loses its stability and becomes more flexible than the wild-type protein. This structural loss might affect IRF-1-DNA interaction and lead to the inhibition of cancer suppression. Identifying the effects of IRF-1 at the molecular level will be beneficial for designing drugs for IRF-1 associated cancers. These drugs should be designed so that they can help reactivate the IRF-1 function, by increasing the transcriptional activity, to treat leukemia.

Deciphering the Role of Filamin B Calponin-Homology Domain in Causing the Larsen Syndrome, Boomerang Dysplasia, and Atelosteogenesis Type I Spectrum Disorders via a Computational Approach.

Filamins (FLN) are a family of actin-binding proteins involved in regulating the cytoskeleton and signaling phenomenon by developing a network with F-actin and FLN-binding partners. The FLN family comprises three conserved isoforms in mammals: FLNA, FLNB, and FLNC. FLNB is a multidomain monomer protein with domains containing an actin-binding N-terminal domain (ABD 1-242), encompassing two calponin-homology domains (assigned CH1 and CH2). Primary variants in FLNB mostly occur in the domain (CH2) and surrounding the hinge-1 region. The four autosomal dominant disorders that are associated with FLNB variants are Larsen syndrome, atelosteogenesis type I (AOI), atelosteogenesis type III (AOIII), and boomerang dysplasia (BD). Despite the intense clustering of FLNB variants contributing to the LS-AO-BD disorders, the genotype-phenotype correlation is still enigmatic. In silico prediction tools and molecular dynamics simulation (MDS) approaches have offered the potential for variant classification and pathogenicity predictions. We retrieved 285 FLNB missense variants from the UniProt, ClinVar, and HGMD databases in the current study. Of these, five and 39 variants were located in the CH1 and CH2 domains, respectively. These variants were subjected to various pathogenicity and stability prediction tools, evolutionary and conservation analyses, and biophysical and physicochemical properties analyses. Molecular dynamics simulation (MDS) was performed on the three candidate variants in the CH2 domain (W148R, F161C, and L171R) that were predicted to be the most pathogenic. The MDS analysis results showed that these three variants are highly compact compared to the native protein, suggesting that they could affect the protein on the structural and functional levels. The computational approach demonstrates the differences between the FLNB mutants and the wild type in a structural and functional context. Our findings expand our knowledge on the genotype-phenotype correlation in FLNB-related LS-AO-BD disorders on the molecular level, which may pave the way for optimizing drug therapy by integrating precision medicine.

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

Effect of Litsea lancifolia Leaf Extract on Glucose Transporter 4 Translocation and Glucose Uptake in 3T3L1 Cell Line.

BACKGROUND: Numerous synthetic drugs have been recommended as a remedy for diabetes, but their role in hypoglycemic effects are diverse. The side effects associated with these drugs due to their extended use led scientists to find unconventional medicines with no or little side effects. AIM: This study was aimed at assessment of in vitro antidiabetic activities of methanolic extract of Litsea lancifolia leaves by using 3T3L1 cell line. MATERIALS AND METHODS: The cytotoxic effect of the leaf extract was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The glucose uptake-inducing capabilities and its correlation with glucose transporter 4 (GLUT4) translocation were measured by flow cytometry in 3T3L1 cells. In addition, the inhibitory effect of L. lancifolia leaf extract on α-amylase activity and α-glucosidase activity was determined by colorimetric methods. RESULTS: Different concentrations of L. lancifolia leaf extract did not show any toxicity on 3T3L1 cells, after the treatment for 24h. On stimulation with leaf extract, 60.22% and 86.26% of 3T3L1 cells showed glucose uptake and GLUT4 expression, respectively. The colorimetric assays showed that the methanolic leaf extract of L. lancifolia has a significant inhibitory effect on the activity of α-amylase enzyme and α-glucosidase enzyme with inhibitory concentration (IC50) value of 248.65 µg/mL and 229.61 µg/mL, respectively. CONCLUSION: On the basis of the results of this study, it is evident that L. lancifolia leaf extract showed promising anti-diabetic effect when compared to the standard drugs metformin and acarbose and was nontoxic to 3T3L1 cells. Thus, it can be further investigated to recommend as a possible alternative treatment in antidiabetic applications.

Prioritization of SNPs in y+LAT-1 culpable of Lysinuric protein intolerance and their mutational impacts using protein-protein docking and molecular dynamics simulation studies.

Lysinuric protein intolerance (LPI) is a rare, yet inimical, genetic disorder characterized by the paucity of essential dibasic amino acids in the cells. Amino acid transporter y+LAT-1 interacts with 4F2 cell-surface antigen heavy chain to transport the required dibasic amino acids. Mutation in y+LAT-1 is rumored to cause LPI. However, the underlying pathological mechanism is unknown, and, in this analysis, we investigate the impact of point mutation in y+LAT-1's interaction with 4F2 cell-surface antigen heavy chain in causing LPI. Using an efficient and extensive computational pipeline, we have isolated M50K and L334R single-nucleotide polymorphisms to be the most deleterious mutations in y+LAT-1s. Docking of mutant y+LAT-1 with 4F2 cell-surface antigen heavy chain showed decreased interaction compared with native y+LAT-1. Further, molecular dynamic simulation analysis reveals that the protein molecules increase in size, become more flexible, and alter their secondary structure upon mutation. We believe that these conformational changes because of mutation could be the reason for decreased interaction with 4F2 cell-surface antigen heavy chain causing LPI. Our analysis gives pathological insights about LPI and helps researchers to better understand the disease mechanism and develop an effective treatment strategy.