A New Insight into Structural and Functional Impact of Single-Nucleotide Polymorphisms in PTEN Gene

Phosphatase and tensin homolog (PTEN) plays essential roles in cellular processes including survival, proliferation, energy metabolism, and cellular architecture. Activating the mutations of PTEN has long been known to produce a variety of disorders, mainly diabetes and cancer in humans. Owing to the importance of PTEN gene, a functional analysis using different in silico approaches was undertaken to explore the possible associations between genetic mutations and phenotypic variation. SIFT, PolyPhen, I-Mutant 3.0, SNP&GO, and PHD-SNP were used for initial screening of functional nsSNPs. From the observed results, three mutations R47G, H61D, and V343E were selected based on their surface accessibility and total energy change. By molecular dynamics approach, H61D showed increase in flexibility, radius of gyration, solvent accessibility, and deviated more from the native structure which was supported by the decrease in the number of hydrogen bonds. Further from principal component analysis and interaction analysis, we identified significant structural changes that can reasonably explain the involvement of deviations in stability caused by mutations. Our analysis also predicts the involvement of SNPs that could potentially influence post-translational modifications in PTEN gene. These in silico predictions could provide a new insight into structural and functional impact of PTEN polymorphisms.     

In Silico Analysis of Prion Protein Mutants: A Comparative Study by Molecular Dynamics Approach

Polymorphisms in the human prion proteins lead to amino acid substitutions by the conversion of PrPC to PrPSc and amyloid formation, resulting in prion diseases such as familial Creutzfeldt–Jakob disease, Gerstmann–Straussler–Scheinker disease and fatal familial insomnia. Cation–π interaction is a non-covalent binding force that plays a significant role in protein stability. Here, we employ a novel approach by combining various in silico tools along with molecular dynamics simulation to provide structural and functional insight into the effect of mutation on the stability and activity of mutant prion proteins. We have investigated impressions of prevalent mutations including 1E1S, 1E1P, 1E1U, 1E1P, 1FKC and 2K1D on the human prion proteins and compared them with wild type. Structural analyses of the models were performed with the aid of molecular dynamics simulation methods. According to our results, frequently occurred mutations were observed in conserved sequences of human prion proteins and the most fluctuation values appear in the 2K1D mutant model at around helix 4 with residues ranging from 190 to 194. Our observations in this study could help to further understand the structural stability of prion proteins.     

In silico discrimination of nsSNPs in hTERT gene by means of local DNA sequence context and regularity

Understanding and predicting the significance of novel genetic variants revealed by DNA sequencing is a major challenge to integrate and interpret in medical genetics with medical practice. Recent studies have afforded significant advances in characterization and predicting the association of single nucleotide polymorphisms in human TERT with various disorders, but the results remain inconclusive. In this context, a comparative study between disease causing and novel mutations in hTERT gene was performed computationally. Out of 59 missense mutations, five variants were predicted to be less stable with the most deleterious effect on hTERT gene by in silico tools, in which two mutations (L584W and M970T) were not previously reported to be involved in any of the human disorders. To get insight into the structural and functional impact due to the mutation, docking study and interaction analysis was performed followed by 6 ns molecular dynamics simulation. These results may provide new perspectives for the targeted drug discovery in the coming future.     

Computational refinement of functional single nucleotide polymorphisms associated with ATM gene

Background

Understanding and predicting molecular basis of disease is one of the major challenges in modern biology and medicine. SNPs associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the ATM gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown.

Principal findings

We analyzed the deleterious nsSNPs associated with ATM gene based on different computational methods. An integrative scoring system and sequence conservation of amino acid residues was adapted for a priori nsSNP analysis of variants associated with cancer. We further extended our approach on SNPs that could potentially influence protein Post Translational Modifications in ATM gene.

Significance

In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in both coding and non coding region that can alter the expression and function of ATM gene. In silico characterization of nsSNPs affecting ATM gene function can aid in better understanding of genetic differences in disease susceptibility.     

Low Band Gap Benzimidazole COF Supported Ni3N as Highly Active OER Catalyst

Covalent organic frameworks (COFs) have structures and morphologies closely resembling graphenes, whose modular construction permits atomic‐level manipulations. This, combined with their porous structure, makes them excellent catalyst supports. Here, the high electrocatalytic activity of a composite, formed by supporting Ni₃N nanoparticles on a benzimidazole COF, for oxygen evolution reaction is shown. The composite oxidizes alkaline water with a near‐record low overpotential of 230 mV @ 10 mA cm^?2 (η ₁₀). This high activity is attributed to the ability of the COF to confine the Ni₃N nanoparticles to size regimes otherwise difficult to obtain and to its low band gap character (1.49 eV) arising from the synergy between the conducting Ni₃N nanoparticles and the π‐conjugated COF. The COF itself, as a metal‐free self‐standing framework, has an oxygen evolution reaction activity with η ₁₀ of 400 mV. The periodic structure of the COF makes it serve as a matrix to disperse the catalytically active Ni₃N nanoparticles favoring their high accessibility and thereby good charge‐transport within the composite. This is evident from the amount of O₂ evolved (230 mmol h^?1 g^?1), which, to the best of our knowledge, is the highest reported. The work reveals the emergence of COF as supports for electrocatalysts.     

IMPARO: inferring microbial interactions through parameter optimisation

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route. Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis. We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors. Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.