Showing papers by "R. Rajasekaran published in 2011"

In Silico Searching for Disease-Associated Functional DNA Variants

Abstract: Experimental analyses of disease-associated DNA variants have provided significant insights into the functional implications of sequence variation. However, such experiment-based approaches for identifying functional DNA variants from a pool with a large number of neutral variants are challenging. Computational biology has the opportunity to play an important role in the identification of functional DNA variants in large-scale genotyping studies, ultimately yielding new drug targets and biomarkers. This chapter outlines in silico methods to predict disease-associated functional DNA variants so that the number of DNA variants screened for association with disease can be reduced to those that are most likely to alter gene function. To explore possible relationships between genetic mutations and phenotypic variation, different computational methods like Sorting Intolerant from Tolerant (SIFT, an evolutionary-based approach), Polymorphism Phenotyping (PolyPhen, a structure-based approach) and PupaSuite are discussed for prioritization of high-risk DNA variants. The PupaSuite tool aims to predict the phenotypic effect of DNA variants on the structure and function of the affected protein as well as the effect of variants in the non-coding regions of the same genes. To further investigate the possible causes of disease at the molecular level, deleterious nonsynonymous variants can be mapped to 3D protein structures. An analysis of solvent accessibility and secondary structure can also be performed to understand the impact of a mutation on protein function and stability. This chapter demonstrates a 'real-world' application of some existing bioinformatics tools for DNA variant analysis.

Predicting Therapeutic Template by Evaluating the Structural Stability of Anti-Cancer Peptides—A Computational Approach

Abstract: Clinically significant antibiotic resistance has evolved against virtually every antibiotic deployed. Yet the development of new classes of antibiotics has lagged far behind our growing need for such drugs. Antimicrobial peptides (AMPs) have emerged as novel therapeutics hailed for their bactericidal and immunomodulatory properties. However, the process of optimizing antimicrobial peptide stability, using large peptide libraries is both tedious and expensive. The intent of this study is to analyze computationally the stability of anti-cancer peptides (ACPs) and to discover a potential template from a pool of ACPs for therapeutic use. Consequently we highlighted that ACP, NK-Lysin appears advantageous over the other ACPs with respect to stability, and may provide a convenient platform for the development of anticancer therapeutic peptide.