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Alignment-free Prediction of Ribonucleases using a Computational Chemistry approach: Comparison with HMM model and Isolation from Schizosaccharomyces pombe, Prediction, and Experimental assay of a new sequence
* 1, 2 , 1, 3, 4 , 5 , 6 , 2 , 1 , 7
1  Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, 09124, Italy
2  CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara, 54830, Cuba
3  Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy, Faculty of Pharmacy, USC, Santiago de Compostela, 15782, Spain
4  Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela, 15782, Spain
5  CINVESTAV–LANGEBIO, Irapuato, Guanajuato, 36500, México
6  Caribbean vitroplants, Santo Domingo, 1464, Dominican Republic
7  Vascular Biology Institute, School of Medicine, University of Miami, Florida, 33136, USA

Abstract: 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 having a double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail when there is a low amino acidic identity percentage between query sequence and others with similar functions (remote homologues) or a similar sequence is not recorded in the database. Quantitative Structure-Activity Relationships (QSAR) applied to protein sequences may allow an alignment-independent prediction of protein function. These sequences QSAR like methods often use 1D sequence numerical parameters as the input to seek sequence-function relationships. However, previous 2D representation of sequences may uncover useful higher-order information. In the work described here we calculated for the first time the Spectral Moments of a Markov Matrix (MMM) associated with a 2D-HP-map of a protein sequence. We used MMMs values to characterize numerically 81 sequences of type III RNases and 133 proteins of a control group. We subsequently developed one MMM-QSAR and one classic Hidden Markov Model (HMM) based on the same 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 for the first time the isolation of a new Pac1 protein (DQ647826) from Schizosaccharomyces pombe, strain 428-4-1. The MMM-QSAR model predicts the new RNase III with the same accuracy as other classical alignment methods. Experimental assay of this protein confirms the predicted activity. The present results suggest that MMM-QSAR models may be used for protein function annotation avoiding sequence alignment with the same accuracy of classic HMM models.
Keywords: Spectral graph theory / Hidden Markov Model / Ribonucleases / Pac1 / Protein 2D representations