Application of Support Vector Machines in Virtual Screening

Traditionally drug discovery has been a labor intensive effort, since it is difficult to identify a possible drug candidate from an extremely large small molecule library for any given target. Most of the small molecules fail to show any activity against the target because of electrochemical, structural and other incompatibilities. Virtual screening is an in-silico approach to identify drug candidates which are unlikely to show any activity against a given target, thus reducing an enormous amount of experimentation which is most likely to end up as failures. Important approaches in virtual screening have been through docking studies and using classification techniques. Support vector machines based classifiers, based on the principles of statistical learning theory have found several applications in virtual screening. In this paper, first the theory and main principles of SVM are briefly outlined. Thereafter a few successful applications of SVM in virtual screening have been discussed. It further underlines the pitfalls of the existing approaches and highlights the area which needs further contribution to improve the state of the art for application of SVM in virtual screening

Analysis of Optimizers to Regulate Occupant's Actions for Building Energy Management

International audienceOccupants and their actions play major roles in building energy management as reported by previous studies, which involves finding the optimal schedule of user actions, under a given physical context, in order to minimize their dissatisfaction. However, comparison and performance analysis of various optimizers, for the concerned problem, have not been studied previously, which is essential to gain insight into the underlying characteristics of the problem. In this work, the performance of four popular and contemporary multi-objective optimization algorithms viz. DEMO, NSGA-II, NSGA-III, and θ-DEA, for estimating the optimal schedule has been analyzed in terms of their abilities to find minimal average indoor conditions, to discover more number of alternative trade-off solutions (flexibility) and to promptly converge to a smaller minimal net dissatisfaction value (speed of convergence). Results show that NSGA-II has slightly better capabilities than NSGA-III and θ-DEA, but it clearly outperforms DEMO. The recently developed population dynamics indicators are also applied to support the observed features of the optimizers. The proposed analyzing paradigm can also be used when the optimization problem is extended to include several other objectives

DNA sequence variation and haplotype structure of the ICAM1 and TNF genes in 12 ethnic groups of India reveal patterns of importance in designing association studies

We have examined the patterns of DNA sequence variation in and around the genes coding for ICAM1 and TNF, which play functional and correlated roles in inflammatory processes and immune cell responses, in 12 diverse ethnic groups of India. We aimed to (a) quantify the nature and extent of the variation, and (b) analyse the observed patterns of variation in relation to population history and ethnic background. At the ICAM1 and TNF loci, respectively, the total numbers of SNPs that were detected were 28 and 12. Many of these SNPs are not shared across ethnic groups and are unreported in the dbSNP or TSC databases, including two fairly common non-synonymous SNPs at positions 13487 and 13542 in the ICAM1 gene. Conversely, the TNF-376A SNP that is reported to be associated with susceptibility to malaria was not found in our study populations, even though some of the populations inhabit malaria endemic areas. Wide between-population variation in the frequencies of shared SNPs and coefficients of linkage disequilibrium have been observed. These findings have profound implications in case-control association studies

Genetic and Functional Diversities of Bacterial Communities in the Rhizosphere of Arachis Hypogaea

Bioinoculants are environmentally friendly, energy efficient and economically viable resources in sustainable agriculture. Knowledge of the structure and activities of microbial population in the rhizosphere of a plant is essential to formulate an effective bioinoculant. In this study, the bacterial community present in the rhizosphere of an important oilseed legume, Arachis hypogaea (L.) was described with respect to adjoining bulk soil as a baseline control using a 16S rDNA based metagenomic approach. Significantly higher abundance of Gamma-proteobacteria, a prevalence of Bacillus and the Cytophaga-Flavobacteria group of Bacteroidetes and absence of the Rhizobiaceae family of Alphaproteobacteria were the major features observed in the matured Arachis-rhizosphere. The functional characterization of the rhizosphere-competent bacteria was performed using culture-dependent determination of phenotypes.Most bacterial isolates from the groundnutrhizosphere exhibited multiple biochemical activities associated with plant growth and disease control. Validation of the beneficial traits in candidate bioinoculants in pot-cultures and field trials is necessary before their targeted application in the groundnut production system

Identifying Eigen-Like Hydrated Protons at Negatively Charged Interfaces

Despite the importance of the hydrogen ion in a wide range of biological, chemical, and physical processes, its molecular structure in solution remains lively debated. Progress has been primarily hampered by the extreme diffuse nature of the vibrational signatures of hydrated protons in bulk solution. Using the inherently surface-specific vibrational sum frequency spectroscopy, we show that at selected negatively charged interfaces, a resolved spectral feature directly linked to the H3O+ core in an Eigen-like species can be readily identified in a biologically compatible pH range. The results offer a new molecular perspective for tracking and understanding the behaviour of hydrated protons at interfaces