Selective functionalisation of Re6 cluster anionic units:from hexa-hydroxo [Re6Q8(OH)6]4− (Q = S, Se) to neutral trans-[Re6Q8L4L′2] hybrid building blocks

The reaction of [Re6Q8(OH)6]4- (Q = S, Se) with p-tertbutylpyridine (TBP) in water leads to neutral trans-[Re6Q8(TBP)4(OH)2] whose hydroxyl reactivity with carboxylic acid and TBP exchange reaction with functional pyridine have been investigated.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.

Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.