The tetratricopeptide repeats (TPR)-like superfamily of proteins in Leishmania spp., as revealed by multi-relational data mining
Protein sequence analysis tasks are multi-relational problems suitable for multi-relational data mining (MRDM). Proteins containing tetratricopeptide (TPR), pentatricopeptide (PPR) and half-a-TPR (HAT) repeats comprise the TPR-like superfamily in which we have applied MRDM methods (relational association rule discovery and probabilistic relational models) with hidden Markov models (HMMs) and Viterbi algorithm (VA) in genome databases of pathogenic protozoa Leishmania. Such integrated MRDM/HMM/VA approach seeks to capture as much model information as possible in the pattern matching heuristic, without resorting to more standard motif discovery methods ( Pfam, xxxx, SMART, xxxxandSUPERFAMILY, xxxx) and it has the advantage of incorporation of optimized profiles, score offsets and distribution to compute probability, as a more recently reported tool (TPRpred) in order to take in account the tendency of repeats to occur in tandem and to be widely distributed along the sequences. Here we compare such currently available resources with our approach (MRDM/HMM/VA) to highlight that the latter performs best into the TPR-like superfamily assignment and it might be applied to other sequence analysis problems in such a way that it contributes to tight-fit motif discoveries and a better probability that a given target sequence is, indeed, a target motif-containing protein.
multi-relational data mining hidden markov models viterbi algorithm tetratricopeptide repeat motif leishmania proteins