<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.     

A novel reagentless sensing system for measuring glucose based on the galactose/glucose-binding protein.

The galactose/glucose-binding protein (GBP) is synthesized in the cytoplasm of Escherichia coli in a precursor form and exported into the periplasmic space upon cleavage of a 23-amino-acid leader sequence. GBP binds galactose and glucose in a highly specific manner. The ligand induces a hinge motion in GBP and the resultant protein conformational change constitutes the basis of the sensing system. The mglB gene, which codes for GBP, was isolated from the chromosome of E. coli using the polymerase chain reaction (PCR). Since wild-type GBP lacks cysteines in its structure, introducing this amino acid by site-directed mutagenesis ensures single-label attachment at specific sites with a sulfhydro-specific fluorescent probe. Site-directed mutagenesis by overlap extension PCR was performed to prepare three different mutants to introduce a single cysteine residue at positions 148, 152, and 182. Since these residues are not involved in ligand binding and since they are located at the edge of the binding cleft, they experience a significant change in environment upon binding of galactose or glucose. The sensing system strategy is based on the fluorescence changes of the probe as the protein undergoes a structural change on binding. In this work a reagentless sensing system has been rationally designed that can detect submicromolar concentrations of glucose. The calibration plots have a linear working range of three orders of magnitude. Although the system can sense galactose as well, this epimer is not a potential interfering substance since its concentration in blood is negligible.