A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm

ProbFAST: Probabilistic Functional Analysis System Tool

<p>Abstract</p> <p>Background</p> <p>The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis.</p> <p>Results</p> <p>We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes.</p> <p>Conclusions</p> <p>ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at <url>http://gdm.fmrp.usp.br/probfast</url>.</p

Effects of BDNF polymorphism and physical activity on episodic memory in the elderly: a cross sectional study

International audienceBackground:The brain-derived neurotrophic factor (BDNF) concentration is highest in the hippocampus comparedwith that in other brain structures and affects episodic memory, a cognitive function that is impaired in olderadults. According to the neurotrophic hypothesis, BDNF released during physical activity enhances brain plasticity andconsequently brain health. However, even if the physical activity level is involved in the secretion of neurotrophin, thisprotein is also under the control of a specific gene. The aim of the present study was to examine the effect of theinteraction between physical activity and BDNF Val66Met(rs6265), a genetic polymorphism, on episodic memory.Methods:Two hundred and five volunteers aged 55 and older with a Mini Mental State Examination score≥24participated in this study. Four groups of participants were established according to their physical activity level andpolymorphism BDNF profile (Active Val homozygous, Inactive Val homozygous, Active Met carriers, Inactive Met carriers).Episodic memory was evaluated based on the delayed recall of the Logical Memory test of the MEM III battery.Results:As expected, the physical activity level interacted with BDNF polymorphism to affect episodic memoryperformance (p< .05). The active Val homozygous participants significantly outperformed the active Met carriers andinactive Val homozygous participants.Conclusion:This study clearly demonstrates an interaction between physical activity andBDNF Val66Metpolymorphismthat affects episodic memory in the elderly and confirms that physical activity contributes to the neurotrophic mechanismimplicated in cognitive health. The interaction shows that only participants with Val/Val polymorphism benefited fromphysical activit