Amalgamation of comparative protein modeling with quantitative structure-retention relationship for prediction of the chromatographic behavior of peptides

• Published in: Journal of Chromatography A 2022-04, Vol.1669, p.462967, Article 462967
• Main Authors: Borkar, Maheshkumar R., Coutinho, Evans C.
• Format: Article
• Language: English
• Subjects: Genetic algorithms (GA); Liquid chromatography; Molecular descriptors; Partial least squares (PLS); Peptides; Quantitative structure retention relationships (QSRR); Retention time
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2022.462967

•ComProM-QSRR methodology has been developed for the prediction of retention time of peptides in liquid chromatography.•It identifies physicochemical properties at specific positions in the peptide sequences that modulate the retention time.•The prediction works well for all classes of peptides irrespective of size or sequence.•It is simple and straightforward approach to execute for retention time prediction. Peptide therapeutics plays a prominent role in medical practice. Both peptides and proteins have been used in several disease conditions like diabetes, cancer, bacterial infections etc. The optimization of a peptide library is a time consuming and expensive chore. The tools of computational chemistry offer a way to optimize the properties of peptides. Quantitative Structure Retention (Chromatographic) Relationships (QSRR) is a powerful tool which statistically derives relationships between chromatographic parameters and descriptors that characterize the molecular structure of analytes. In this paper, we show how Comparative Protein ModelingQuantitative Structure Retention Relationship (acronym ComProM-QSRR) can be used to predict the retention time of peptide sequences. This formalism is founded on our earlier published QSAR methodology HomoSAR. ComProM-QSRR can recognize and distinguish the contribution of amino acids at specific positions in the peptide sequences to the retention phenomena through their related physicochemical properties. This study firmly establishes the fact that this approach can be pragmatically used to predict the retention time to all classes of peptides regardless of size or sequence.