Reversed-phase high-performance liquid chromatography (RP-HPLC) is the most popular chromatographic mode, accounting for more than 90% of all separations. HPLC itself owes its immense popularity to it being relatively simple and inexpensive, with the equipment being reliable and easy to operate. Due to extensive automation, it can be run virtually unattended with multiple samples at various separation conditions, even by relatively low-skilled personnel. Currently, there are >600 RP-HPLC columns available to end users for purchase, some of which exhibit very large differences in selectivity and production quality. Often, two similar RP-HPLC columns are not equally suitable for the requisite separation, and to date, there is no universal RP-HPLC column covering a variety of analytes. This forces analytical laboratories to keep a multitude of diverse columns. Therefore, column selection is a crucial segment of RP-HPLC method development, especially since sample complexity is constantly increasing. Rationally choosing an appropriate column is complicated. In addition to the differences in the primary intermolecular interactions with analytes of the dispersive (London) type, individual columns can also exhibit a unique character owing to specific polar, hydrogen bond, and electron pair donor–acceptor interactions. They can also vary depending on the type of packing, amount and type of residual silanols, “end-capping”, bonding density of ligands, and pore size, among others. Consequently, the chromatographic performance of RP-HPLC systems is often considerably altered depending on the selected column. Although a wide spectrum of knowledge is available on this important subject, there is still a lack of a comprehensive review for an objective comparison and/or selection of chromatographic columns. We aim for this review to be a comprehensive, authoritative, critical, and easily readable monograph of the most relevant publications regarding column selection and characterization in RP-HPLC covering the past four decades. Future perspectives, which involve the integration of state-of-the-art molecular simulations (molecular dynamics or Monte Carlo) with minimal experiments, aimed at nearly “experiment-free” column selection methodology, are proposed.

To quantitatively characterize the structure of a peptide and to predict its gradient retention time at given HPLC conditions three structural descriptors are used: (i) logarithm of the sum of retention times of the amino acids composing the peptide, log SumAA, (ii) logarithm of the van der Waals volume of the peptide, log VDW(Vol), (iii) and the logarithm of the peptide's calculated n-octanol-water partition coefficient, clog P. The log SumAA descriptor is obtained from empirical data for 20 natural amino acids, determined in a given HPLC system. The two other descriptors are calculated from the peptides' structural formulas using molecular modeling methods. The quantitative structure-retention relationships (QSRR), build by multiple linear regression, describe HPLC retention of peptide on a given chromatographic system on which the retention of the 20 amino acids was predetermined. A structurally diversified series of 98 peptides was employed. The predicted gradient retention times on several chromatographic systems were in good agreement with the experimental data. The QSRR equations, derived for a given system operated at variable gradient times and temperatures allowed for the prediction of peptide retention in that system. Matching the experimental HPLC retention to the theoretically predicted for a presumed peptide could facilitate original protein identification in proteomics. In conjunction with MS data, prediction of the retention time for a given peptide might be used to improve the confidence of peptide identifications and to increase the number of correctly identified peptides.

No abstract

Quantitative structure retention relationships (QSRR) were derived allowing prediction of reversed-phase high-performance liquid chromatography (HPLC) retention of peptides. To quantitatively characterize the structure of a peptide, and then to predict its gradient retention time under given HPLC conditions, the following descriptors are employed: logarithm of the sum of retention times of the amino acids composing the peptide, log Sum(AA), logarithm of Van der Waals volume of the peptide, log VDW(Vol), and logarithm of its calculated n-octanol-water partition coefficient, clog P. The first descriptor is based on a set of empirical data for 20 natural amino acids. The next two descriptors are easily calculated from a structural formula. The predicted gradient retention times are in excellent agreement with the experimental data, determined for a structurally diversified series of 101 peptides. The QSRR equation obtained predicts in a convenient and reliable manner the retention times for any peptide in a once characterized HPLC system.