SearcharxivSearch

arXiv subjects

Sona Vasudevan

Publications and source records attributed to Sona Vasudevan.

3 recordsLinked to original sources

In Search of Newer Targets for IBD: A Systems and a Network Medicine Approach

Introduction: Crohn's disease and ulcerative colitis, both under the umbrella of inflammatory bowel diseases (IBD), involve many distinct molecular processes. The difference in their molecular processes is studied by using the different genes involved in each disease, and it is explored further for drug targeting and drug repurposing. Methods: The initial set of genes was obtained by mining published literature and several curated databases. The identified genes were then subject to Systems and Network analysis to reveal their molecular processes and shed some light on their pathogenesis. Such methodologies have identified newer targets and drugs that can be repurposed. Results: We use a Systems and Network Medicine approach to understand the mechanism of actions of genes involved in IBD. From an initial set of genes mined from literature and curated databases, we used the Multi-Steiner Tree algorithm implemented within the CoVex systems medicine platform to expand each disease module by incorporating candidate genes with significant connections to the disease-related seed genes. Such expanded disease modules will identify a larger set of potential targets and drugs. We used the Closeness Centrality algorithm implemented within CoVex to search for newer targets and repurposable drugs. Through a network medicine approach, we provide a mechanistic view of the diseases and point to newer drugs and targets. Conclusion: We demonstrate that the Systems and Network Medicine approach is a powerful way to understand diseases and understand their mechanisms of action.

q-bio.MN

Ridge Regression Estimated Linear Probability Model Predictions of O-glycosylation in Proteins with Structural and Sequence Data

The likelihood of O-GlcNAc glycosylation in human proteins is predicted using the ridge regression estimated linear probability model (LPM). To achieve this, sequences from three similar post-translational modifications (PTMs) of proteins occurring at, or very near, the S or T site are analyzed: N-glycosylation, O-mucin type (O-GalNAc) glycosylation, and phosphorylation. Results found include: 1) The consensus composite sequon for O-glycosylation does NOT have W on either side of the glycosylation site. 2) The same holds for the consensus sequon for phosphorylation. 3) For LPM estimation, N-glycosylated sequences are found to be good approximations to non-O-glycosylatable sequences. 4) The selective positioning of an amino acid along the sequence, differentiates the PTMs of proteins. 5) Some N-glycosylated sequences are also phosphorylated at the S or T site. 6) ASA values for N-glycosylated sequences are stochastically larger than those for O-GlcNAc glycosylated sequences. 7) Structural attributes (beta turn II, II', helix, beta bridges, beta hairpin, and the phi angle) are significant LPM predictors of O-GlcNAc glycosylation. The LPM with sequence and structural data as explanatory variables yields a Kolmogorov-Smirnov (KS) statistic value of 99%. 8) With only sequence data, the KS statistic erodes to 80%, underscoring the germaneness of structural information, which is sparse on O-glycosylated sequences. With 50% as the cutoff probability for predicting O-GlcNAc glycosylation, this LPM mispredicts 21% of out-of-sample O-GlcNAc glycosylated sequences as not being glycosylated. The 95% confidence interval around this mispredictions rate is 16% to 26%

q-bio.QM

Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data

Absent experimental evidence, a robust methodology to predict the likelihood of N-glycosylation in human proteins is essential for guiding experimental work. Based on the distribution of amino acids in the neighborhood of the NxS/T sequon (N-site); the structural attributes of the N-site that include Accessible Surface Area, secondary structural elements, main-chain phi-psi, turn types; the relative location of the N-site in the primary sequence; and the nature of the glycan bound, the ridge regression estimated linear probability model is used to predict this likelihood. This model yields a Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which is reasonable.

q-bio.QM