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BMC Bioinformatics. 2008 May 16;9:236. doi: 10.1186/1471-2105-9-236.

A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins.

Author information

1
Dipartimento di Malattie Infettive, Parassitarie ed Immunomediate - Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Roma, Italy. kevbrick@gmail.com

Abstract

BACKGROUND:

The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent a fully accurate substitution model for proteins characterized by a biased amino acid composition. This problem has been addressed recently by adjusting existing matrices, however, to date, no empirical approach has been taken to build matrices which offer a substitution model for comparing proteins sharing an amino acid compositional bias. Here, we present a novel procedure to construct series of symmetrical substitution matrices to align proteins from similarly biased Plasmodium proteomes.

RESULTS:

We generated substitution matrices by selecting from the BLOCKS database those multiple alignments with a compositional bias similar to that of P. falciparum and P. yoelii proteins. A novel 'fuzzy' clustering method was adopted to group sequences within these alignments, showing that this method retains more complete information on the amino acid substitutions when compared to hierarchical clustering. We assessed the performance against the BLOSUM62 series and showed that the usage of our matrices results in an improvement in the performance of BLAST database searches, greatly reducing the number of false positive hits. We then demonstrated applications of the use of novel matrices to improve the annotation of homologs between the two Plasmodium species and to classify members of the P. falciparum RIFIN/STEVOR family.

CONCLUSION:

We confirmed that in the case of compositionally biased proteins, standard BLOSUM matrices are not suited for optimal alignments, and specific substitution matrices are required. In addition, we showed that the usage of these matrices leads to a reduction of false positive hits, facilitating the automatic annotation process.

PMID:
18485187
PMCID:
PMC2408606
DOI:
10.1186/1471-2105-9-236
[Indexed for MEDLINE]
Free PMC Article

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