Biomedical subjects

C Landès

Publications and source records attributed to C Landès.

4 recordsLinked to original sources

PubMed1995

A structure-based multiple sequence alignment of all class I aminoacyl-tRNA synthetases.

The superimposable dinucleotide fold domains of MetRS, GlnRS and TyrRS define structurally equivalent amino acids which have been used to constrain the sequence alignments of the 10 class I aminoacyl-tRNA synthetases (aaRS). The conservation of those residues which have been shown to be critical in some aaRS enables to predict their location and function in the other synthetases, particularly: i) a conserved negatively-charged residue which binds the alpha-amino group of the amino acid substrate; ii) conserved residues within the inserted domain bridging the two halves of the dinucleotide-binding fold; and iii) conserved residues in the second half of the fold which bind the amino acid and ATP substrate. The alignments also indicate that the class I synthetases may be partitioned into two subgroups: a) MetRS, IleRS, LeuRS, ValRS, CysRS and ArgRS; b) GlnRS, GluRS, TyrRS and TrpRS.

Amino Acid Sequence↗

PubMed1994

Fast databank searching with a reduced amino-acid alphabet.

Fast sequence databanks search algorithms generally make use of hash tables and look for exactly matching words. An increased sensitivity--at the expense of a decreased selectivity--can be attained in the case of proteins by using a reduced amino acid alphabet. We propose here an alphabet reduced to 10 symbols, that we used in modified versions of the FASTP and SCAN programs. An application to the aminoacyl-tRNA synthetases shows that this technique may be useful in detecting distant relationships between proteins.

Algorithms↗

PubMed1993

Dot-plot comparisons by multivariate analysis (DOCMA): a tool for classifying protein sequences.

A method aimed at classifying protein sequences without resorting to pairwise alignment is presented. Called DOCMA (DOt-plot Comparisons by Multivariate Analysis), it is based on a multivariate analysis of the pairwise dot-plots between all the sequences in the set. The dot-plots are first simplified by considering only the projections of the 'diagonal' segments of similarity onto the axes. From these projections a data matrix is built, in which each column is representative of the comparisons of one given sequence with all the other ones. This data matrix is then transformed into a distance matrix by a chi-squared analysis, from which the coordinates of the sequences in an orthonormal Euclidean space are obtained. The sequences are finally classified by a dynamic clustering procedure followed by a search for strong clusters. Application of this method to protein families such as the globins, the cytochromes c and the aminoacyl-tRNA synthetases shows that it is quite effective in delineating subgroups that contain even distantly related sequences.

Algorithms↗

PubMed1992

A comparison of several similarity indices used in the classification of protein sequences: a multivariate analysis.

The present work describes an attempt to identify reliable criteria which could be used as distance indices between protein sequences. Seven different criteria have been tested: i and ii) the scores of the alignments as given by the BESTFIT and the FASTA programs; iii) the ratio parameter, i.e. the BESTFIT score divided by the length of the aligned peptides; iv and v) the statistical significance (Z-scores) of the scores calculated by BESTFIT and FASTA, as obtained by comparison with shuffled sequences; vi) the Z-scores provided by the program RELATE which performs a segment-by-segment comparison of 2 sequences, and vii) an original distance index calculated by the program DOCMA from all the pairwise dotplots between the sequences. These 7 criteria have been tested against the aminoacid sequences of 39 globins and those of the 20 aminoacyl-tRNA synthetases from E. coli. The distances between the sequences were analyzed by the multivariate analysis techniques. The results show that the distances calculated from the scores of the pairwise alignments are not adequately sensitive. The Z-score from RELATE is not selective enough and too demanding in computer time. Three criteria gave a classification consistent with the known similarities between the sequences in the sets, namely the Z-scores from BESTFIT and FASTA and the multiple dotplot comparison distance index from DOCMA.

Algorithms↗