MMM-QSAR Recognition of Ribonucleases without Alignment: Comparison with an HMM Model and Isolation from Schizosaccharomyces pombe, Prediction, and Experimental Assay of a New Sequence
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文摘
The study of type III RNases constitutes an important area in molecular biology. It is known that the pac1+gene encodes a particular RNase III that shares low amino acid similarity with other genes despite havinga double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail whenthere is a low amino acidic identity percentage between a query sequence and others with similar functions(remote homologues) or a similar sequence is not recorded in the database. Quantitative structure-activityrelationships (QSAR) applied to protein sequences may allow an alignment-independent prediction of proteinfunction. These sequences of QSAR-like methods often use 1D sequence numerical parameters as the inputto seek sequence-function relationships. However, previous 2D representation of sequences may uncoveruseful higher-order information. In the work described here we calculated for the first time the spectralmoments of a Markov matrix (MMM) associated with a 2D-HP-map of a protein sequence. We used MMMsvalues to characterize numerically 81 sequences of type III RNases and 133 proteins of a control group. Wesubsequently developed one MMM-QSAR and one classic hidden Markov model (HMM) based on thesame data. The MMM-QSAR showed a discrimination power of RNAses from other proteins of 97.35%without using alignment, which is a result as good as for the known HMM techniques. We also report forthe first time the isolation of a new Pac1 protein (DQ647826) from Schizosaccharomyces pombe strain428-4-1. The MMM-QSAR model predicts the new RNase III with the same accuracy as other classicalalignment methods. Experimental assay of this protein confirms the predicted activity. The present resultssuggest that MMM-QSAR models may be used for protein function annotation avoiding sequence alignmentwith the same accuracy of classic HMM models.
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