ComProM-QSRR: Integration of Comparative Protein Modeling with Quantitative Structure-Retention Relationship for Prediction of the Chromatographic Behavior of Peptides
Posted: 5 Feb 2020
Date Written: February 3, 2020
In the biological world, peptides are key components which play significant roles. Peptides and proteins are used in many disease conditions like diabetes, cancer, bacterial infections etc. To synthesize and optimize the library of peptides is a time consuming and expensive chore. QSAR is being used for several decades for activity prediction, lead optimization etc. Peptide QSAR is a daunting task because of uncertainties in the 3-D structures of peptides. Here, we utilized our validated peptide QSAR methodology HomoSAR to predict the retention time of new peptide sequences and chromatographic behavior of peptides, named as ComProM-QSRR. It is a union of the principles of Comparative Protein Modeling and the QSRR (Quantitative Structure Retention Relationship) formalism to predict retention time of new peptide sequences. The first step in this methodology is multiple sequence alignment which is followed by scoring every position in the peptide sequence against a reference peptide (the most active) in the alignment, through calculation of similarity indices. The similarity indices obtained for each position (amino acid residue) in the peptide form the “descriptors” values “X” (independent variables) which are correlated to Mean Retention Time “Y” (dependent variable) of the peptides by G/PLS technique. In the present study, we found that this methodology can be applied to predict the retention time to all classes of peptides irrespective of size or sequence. The ComProM-QSRR study has been able to recognize amino acids and their allied physicochemical properties at specific positions in the peptide sequences that contribute to their retention time. The use of ComProM-QSRR methodology is thus expected to be applicable to a wide research field like proteomics for the retention time (RT) prediction of peptides.
Keywords: QSRR, Peptide QSAR, RT
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