Hidden Markov models and the Viterbi algorithm applied to integrated bioinformatics analyses of putative flagellar actin-interacting proteins in Leishmania spp.

The tetratricopeptide repeats (TPR)-like superfamily of proteins in Leishmania spp., as revealed by multi-relational data mining

“…Recently we have considered various approaches to recognizing functional elements in flagellar proteins from Leishmania genomes. In our recent works (Pacheco et al, 2007(Pacheco et al, , 2009Girão et al, 2008), we have employed probabilistic language models to simultaneously predict TPR, PPR and HAT motifs, whereas now we extend this research to a preliminary benchmarking with existing HMM methods. Our model is, for the most part, an HMM, although a component of it is actually stochastic context free grammar (SCFG) (Dyrka and Nebel, 2009).…”

“…Our model is, for the most part, an HMM, although a component of it is actually stochastic context free grammar (SCFG) (Dyrka and Nebel, 2009). The Viterbi path (Pacheco et al, 2007(Pacheco et al, , 2009) is the most likely derivation (parse) of the sequence, by the given SCFG, to compute the total probability of all derivations that are consistent with a given sequence, based on some SCFGs. This is equivalent to the probability of the SCFG generating the sequence, and is intuitively a measure of how consistent the sequence is with the given grammar (Rivas and Eddy, 2001).…”

“…A PPR motif is a degenerate 35-residue sequence, closely related to the 34-residue TPR motif. As reported in our earlier studies (Pacheco et al, 2007;Girão et al, 2008;Pacheco et al, 2009), we systematically solved inconsistencies in the motif annotation by manual expertise. Since motif occurrences are adjacent in sequences, we could define the motif sequence of a protein as the succession of motifs read from the N toward the C terminus.…”

“…In a first validation step we used the feature of trained HMMs to emit domain-specific sequences according to their model parameters. Sequences were compared with generated state paths in the same way as described earlier (Girão et al, 2008;Pacheco et al, 2009). The process of generation was repeated 10 times for every TPR-like motif.…”

Role of remote sensing, geographic bioinformatics system and bioinformatics in kala-azar epidemiology

“…Consensus epitope predictions from more than 8,271 annotated protein sequences of L. major with 5-8 different algorithms including some machine learning algorithms allowed the identification of 78 class I CD8 + epitopes and have opened opportunities for the identification of targets for vaccine development[83]. Hidden Markov models and the Viterbi algorithm are also applied to integrated bioinformatics analyses of putative flagellar actin-interacting proteins in Leishmania species[84]. These are some bright examples of how machine learning approach and bionformatics analysis move forward the functional genomics of Leishmania species.…”