Maturity onset diabetes of the young (MODY) is a metabolic and genetic disorder. It is different from type 1 and type 2 diabetes with low occurrence level (1–2 %) among all diabetes. This disorder is a consequence of β-cell dysfunction. Till date, 11 subtypes of MODY have been identified, and all of them can cause gene mutations. However, very little is known about the gene mapping, molecular phylogenetics, and co-expression among MODY genes and networking between cascades. This study has used latest servers and software such as VarioWatch, ClustalW, MUSCLE, G Blocks, Phylogeny.fr, iTOL, WebLogo, STRING, and KEGG PATHWAY to perform comprehensive analyses of gene mapping, multiple sequences alignment, molecular phylogenetics, protein–protein network design, co-expression analysis of MODY genes, and pathway development. The MODY genes are located in chromosomes-2, 7, 8, 9, 11, 12, 13, 17, and 20. Highly aligned block shows Pro, Gly, Leu, Arg, and Pro residues are highly aligned in the positions of 296, 386, 437, 455, 456 and 598, respectively. Alignment scores inform us that HNF1A and HNF1B proteins have shown high sequence similarity among MODY proteins. Protein–protein network design shows that HNF1A, HNF1B, HNF4A, NEUROD1, PDX1, PAX4, INS, and GCK are strongly connected, and the co-expression analyses between MODY genes also show distinct association between HNF1A and HNF4A genes. This study has used latest tools of bioinformatics to develop a rapid method to assess the evolutionary relationship, the network development, and the associations among eleven MODY genes and cascades. The prediction of sequence conservation, molecular phylogenetics, protein–protein network and the association between the MODY cascades enhances opportunities to get more insights into the less-known MODY disease. © 2014, Springer Science+Business Media New York.

George Priya Doss C

Department of Integrative Biology

School of Bio Sciences and Technology

Vellore Campus

Chakraborty C

Bandyopadhyay S

Agoramoorthy G.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Cell Biochemistry and Biophysics

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that isbased on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missensemutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&amp;GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K,A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics(MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP.We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases suchas diabetes. © Ivyspring International Publisher.

Fulltext

Theranostics

Evolution- and Structure-Based Computational Strategy Reveals the Impact of Deleterious Missense Mutations on MODY 2 (Maturity-Onset Diabetes of the Young, Type 2)

Cluster of differentiation (CD) is a group of proteins with highly immunological and medical importance, and some are established therapeutics. These membrane proteins are used to investigate of cell surface molecules of blood cells especially WBC. We selected a population of fifteen members with most medical importance, which includes CD2, CD4, CD5, CD6, CD7, CD9, CD14, CD16, CD19, CD22, CD28, CD33, CD36, CD38, and CD44 and performed in silico analysis using algorithm analysis and mathematical models. The results suggest that LEU (L) is well aligned. CD16 is rooted with CD22 and likewise, CD4 is closely related to CD44. Notably, highest number of highly conserved amino acids is recorded in CD22. WebLogo were formed up to 350 amino acid position and Met (M) is found to be tallest logo. Our results would be useful for upcoming researchers to obtain fundamental idea about the particular regions CD proteins which is having the structural and functional significance related to the evolutionary biology. © 2013 Springer Science+Business Media New York.

The Protein Journal

Does Computational Biology Help us to Understand the Molecular Phylogenetics and Evolution of Cluster of Differentiation (CD) Proteins?

Using computational biology, we have depicted the insulin phylogenetics. We have also analyzed the sequence alignment and sequence logos formation for both the insulin chain A and B for three groups namely, the mammalian group, vertebrates group and fish group. We have also analyzed cladograms of insulin for the mammalian group. In accordance with that path lengths, matrix for distance analysis, matching representation of nodes of the cladogram and dissimilarity between two nodes have been performed for both of the A and B chains of the mammalian group. Our results show that 12 amino acid residues (GlyA1, IleA2, ValA3, TyrA19, CysA20, AsnA21, LeuB6, GlyB8, LeuB11, ValB12, GlyB23 and PheB24) are highly conserved for all groups and among them some (GlyA1, IleA2, ValA3);(TyrA19, CysA20, AsnA21) are continuous. This study shows a rapid method to calculate the amino acid sequences in terms of evolutionary conservation rates as well as molecular phylogenetics. © 2011 Elsevier Ireland Ltd.

Computer Methods and Programs in Biomedicine

Can computational biology improve the phylogenetic analysis of insulin?

The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins. © 2011 Chakraborty et al.

Journal	Data powered by TypesetCell Biochemistry and Biophysics
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1085-9195
Open Access	No