title: Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum.
abstract: BACKGROUND
  Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.
  
  
  RESULTS
  Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.
  
  
  CONCLUSIONS
  We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/~terrapon/HMMﬁt/
authors.affiliation: Méthodes et Algorithmes pour la Bioinformatique, LIRMM, Université Montpellier 2, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Terrapon
authors.forename: Olivier
authors.initials: O
authors.lastname: Gascuel
authors.forename: Eric
authors.initials: E
authors.lastname: Maréchal
authors.forename: Laurent
authors.initials: L
authors.lastname: Bréhélin
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D030562:Databases, Protein; D008390:Markov Chains; D058977:Molecular Sequence Annotation; D010963:Plasmodium falciparum; D011506:Proteins / Q000032:analysis; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D016415:Sequence Alignment; D012984:Software
chemical_list: D011506:Proteins; D020543:Proteome