Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography

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Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography Magdalena Skoczylas, Szymon Bocian, Bogusław Buszewski∗ ´ , Gagarina 7 St., 87-100 Torun, Poland Chair of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun

a r t i c l e

i n f o

Article history: Received 9 May 2019 Revised 20 August 2019 Accepted 3 September 2019 Available online xxx Keywords: Amino acids Retention mechanism Structural similarity HPLC ELSD Stationary phases

a b s t r a c t Quantitative structure – retention relationships analysis was applied to investigate the molecular retention mechanism of proteinogenic and non-proteinogenic amino acids on the amino acid- and peptidesilica stationary phases. Twelve stationary phases with chemically bonded amino acids of different types (glycine, alanine, phenylalanine, leucine, methionine, aspartic acid, and N-(9-Fluorenylmethoxycarbonyl)O-tert-butyl-l-tyrosine) and chains lengths (amino acid, dipeptide, and tripeptide) were tested. In order to compare chromatographic properties of the prepared materials with the conventional columns, the amino-bonded phases (laboratory-prepared and commercial one) were also studied. For each of analyte, the molecular descriptors were calculated using quantum mechanics method. The QSRR models were determined using 13 molecular descriptors mainly related to the surface area, hydrophobicity, polarity, ion-exchange and hydrogen bonding capabilities of the analytes. Finally, the prediction potency of the molecular modeling descriptors-based models was also independently studied for the tested stationary phases using 15 training set and 6 test set of amino acids. © 2019 Published by Elsevier B.V.

1. Introduction The chemical modification of silica surface with amino acids, peptides, or proteins molecules represents a convenient approach to obtain biologically inspired chromatographic stationary phases [1]. This type of materials reveals diversified application targets due to a wide range of amino acids side chain chemistries (e.g. linear, branched, or aromatic alkyl groups, thiols, amines, etc.). Depending on the length and the type of amino acids sequences, they have been applied in the separation of d- and lamino acids, isomeric dipeptides, phenylthiohydantoin-amino acids derivatives [2,3], unmodified α -amino acids racemates [4], proteinogenic and non-proteinogenic amino acids [5], enantiomers of amino acids, glycyl and diastereomeric dipeptides, tripeptides [6], drugs in blood serum or plasma [7,8], bioactive polar compounds [9–11], steroids, sulfur-based drug molecules [12], a traditional Chinese medicine (TCM)-Rheum Palmatum L. [13], protein digest [14], inorganic and organic ions [15,16], carbohydrates [17], and in a molecular-shape selective HPLC for polycyclic aromatic hydrocarbons (PAHs) [18]. In consequence, the amino acid-based stationary phases revealed applicability in the following HPLC ∗

Corresponding author. E-mail addresses: [email protected], [email protected] (B. Buszewski).

modes: reversed-phase liquid chromatography (RP LC) [12,18], ligand exchange chromatography [4–6], hydrophilic interaction liquid chromatography (HILIC) [9–11,14,17], off-line two dimensional liquid chromatography (2D-RP/RP LC) [13], ion chromatography (IC) [15,16], as well as biochromatography as templates for the preparation of molecularly imprinted stationary phases for chiral separations [19–23]. This relatively wide application range is reflected in the diversified retention mechanism occurring on the amino acid- and peptide-silica stationary phases which certainly depends on the chemically bonded amino acids sequence, solutes structure, and mobile phase components. Quantitative structure-retention relationship (QSRR) constitutes a convenient approach to investigate the retention mechanism at the molecular level [24]. QSRRs introduced by Kaliszan are a statistically derived relationships modeling chromatographic parameter (retention) as a function of the descriptors characterizing the molecular structure of analytes. Therefore, the impact of the solutes molecular properties on the retention in particular chromatographic mode (i.e. stationary phase) can be performed [25]. In the literature and work practice, three types of QSRR approaches are prominent. The simplest one regressed the logarithm of retention factor with the theoretically calculated logarithm of n-octanol/water partition coefficient (log P) [26–29]. The second QSRR approach assumes the regression of the logarithm of

https://doi.org/10.1016/j.chroma.2019.460514 0021-9673/© 2019 Published by Elsevier B.V.

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

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retention factor – log k (retention factor – k or retention time – tR ) against structural descriptors of analytes which are determined by quantum chemical calculations. This type of QSRR equations assists in distinguishing stationary phases in terms of the chemical properties of bonded ligands and/or organic or inorganic material support, as well as indicating the interactions between analytes and stationary phases [30]. One of the descriptor-based model which exhibits a good performance in quantitative comparison of stationary phases was developed by Kaliszan and coworkers [26,31,32] and has the form:

tR = k1 + k2 μ + k3 δMin + k4 AWAS

(1)

where: k1 –k4 are regression coefficients, μ – total dipole moment, δ Min – electron excess charge of the most negatively charged atom, AWAS – water-accessible molecular surface area. However, the application of other structural descriptors in QSRR analysis also enables the investigation of retention mechanism, retentive differences between HPLC columns, as well as retention prediction of a new set of solutes. For instance, Quiming et al. [33] developed descriptor-based models to describe and predict the retention behavior of adrenoreceptor agonists and antagonists on bare silica column using 36 constitutional, geometric, electronic, topological, and physicochemical descriptors [33]. A research group of Haddad extensively used QSRR approach, among other to speed up the screening phase of chromatographic method development using two thousand molecular descriptors consisting of constitutional, topological, geometrical, electrostatic, physical, shape, and quantum chemical descriptors [34–36]. The QSRR approach with an extended number of molecular descriptors was also applied and reported by [33,37–44]. Three-dimensional quantitative structure – retention relationship (3D-QSRR) is another useful tool within above approach. 3DQSRR model was constructed based on a three-dimensional quantitative structure – biological activity relationships method (3DQSAR) known as comparative molecular field analysis (CoMFA). This approach assumes that the interactions between analytes and stationary and mobile phases have a non-covalent character. Moreover, in this method the chromatographic retention variations correlate with the changes in the steric and electrostatic fields surrounding a set of analyte molecules. The molecular structure and stereochemical information are coded in the 3D descriptors which constitute a prospective option for modeling chiral separation, predicting retention times, understanding the retention mechanism, and comparing different stationary phases [45–49]. The linear solvation energy relationships (LSER) constitute a third type of the most studied QSRR which was introduced to chromatography by Abraham and co-workers [43] based on the solvatochromic method developed by Kamlet and Taft [50,51]. The full LSER model is represented of the equation:

log k = log k0 + r R2 + vVx + sπ2H + a



α2H + b



β2H

(2)

where: R2 – excess molar refraction, Vx – characteristic McGowan  H  volume, π2H – dipolarity/polarizability, α2 and β2H – effective hydrogen bonding acidity and basicity, respectively, and r, v, s, a, b – regression coefficients characterizing a particular properties of a given chromatographic system, i.e. mobile phase and stationary phase [25,43,44,52–54]. Overall, the QSRR approach, in the form of three the most studied models, allows (i) the investigation of retention mechanism in a given system, and thus optimize its working conditions, (ii) the evaluation of differences and similarities of the HPLC columns, (iii) the identification of the most instructive analyte descriptors, (iv) the retention prediction for a new solutes, (v) the prediction of the relative biological activity within a set of analytes, and (vi) the evaluation of non-chromatographic properties of analytes such as lipophilicity, dissociation constant, etc. [25,44,55]

In these studies, the QSRR approach was used to investigate the molecular retention mechanism of proteinogenic and nonproteinogenic amino acids on the amino acid- and peptide-silica stationary phases. The research was also aimed to evaluate differences and similarities between the prepared columns. This type of chromatographic stationary phases was studied for the first time using QSRR analysis and amino acids as a test analytes. The investigations were performed on the twelve stationary phases with chemically bonded amino acids of different types – glycine (Gly), alanine (Ala), phenylalanine (Phe), leucine (Leu), methionine (Met), aspartic acid (Asp), and N-(9-Fluorenylmethoxycarbonyl)-Otert-butyl-l-tyrosine ([Fmoc-Tyr(tBu)]) and chain lengths (amino acid, dipeptides, and tripeptides). In order to compare chromatographic properties of the prepared materials with the conventional columns, the amino-bonded phases (laboratory prepared and commercial one) were also studied. The amino-bonded phase comprised a support for the synthesis of the amino acid- and peptidesilica stationary phases. A group of 15 amino acids as a training analytes and 6 amino acids as test (validation) solutes were tested during the research. For each of analyte the molecular descriptors were calculated using quantum mechanics method or taken from experimental data. The QSRR models were determined using 13 molecular descriptors mainly related to the surface area, hydrophobicity, polarity, ion-exchange and hydrogen bonding capabilities of the analytes. Finally, the prediction potency of the molecular modeling descriptors-based models was also independently studied for the tested stationary phases.

2. Materials and methods 2.1. Instrumentation and chromatographic conditions Elemental analysis was done using a Perkin-Elmer CHN 240 analyzer (Palo Alto, USA). Solid state 13 C NMR measurements were performed on a Bruker Avance III 700 MHz (Karlsruhe, Germany) after placing ∼300 mg samples in the double-bearing rotors of zirconia. The 13 C cross-polarization magic-angle spinning (CP/MAS) NMR spectra were received with rotation frequency 8 kHz, pulse time 2 ms, acquisition time 0.01643 s, and relaxation time 6 s. The synthesized adsorbents were packed into 125 mm × 4.6 mm i.d. stainless steel tubes (Sigma Aldrich, Germany), using the slurry method. About 1.5 g of the modified material was prepared as a slurry with 15 ml of methanol (amino-(Met)1 , amino-(Met)2 , and amino-[Fmoc-Tyr(tBu)]) and put into the packing apparatus. Acetonitrile was used as a packing pressurizing solvent during the filling process. Columns were packed using a DSF 122 packing pump (Haskel INC., USA) under a pressure of a 40 MPa. The chromatographic experiments were performed on the Shimadzu Prominence LC (Kyoto, Japan) equipped with quaternary gradient pump (LC-20AD), an autosampler (SIL-20C), a column thermostat (CTO-10AS), and low temperature-evaporative light scattering detector (ELSD-LT, Shimadzu). Instrument control, data acquisitions, and processing were performed with LabSolutions software for HPLC. The chromatographic measurements were carried out on twelve laboratory prepared amino acid- and peptide-silica stationary phases and one aminopropyl column (silica-Amino), as well as R a commercial TSKgel NH2 -100 amino-bonded column (particle diameter 3 μm, Col. no. 504NP0 0 02, Tosoh Bioscience GmbH, Kaisei-cho, Shunan-shi, Yamaguchi, Japan). The structures of the tested stationary phases, including their heterogeneous structure of chemically bonded ligands, are presented in Fig. 1. The physicochemical properties (e.g. carbon content, coverage density, and column dimension) of the materials are listed in

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

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Fig. 1. Schematic structure of chemically bonded stationary phases (in non-ionized state) used in the study: A – amino-(Leu)1 , B – amino-(Leu)2 , C – amino-(Leu)3 , D – amino-(Gly)1 , E – amino-(Gly)3 , F – amino-(Met)1 , G – amino-(Met)2 , H – amino-(Phe)1 , I – amino-(Phe)2 , J – amino-(Asp)1 , K – amino-(Ala)2 , L – silica-Amino, M – TSKgel-NH2 , N – amino-[Fmoc-Tyr(tBu)]. A detailed heterogeneous structure of amino-(Met)2 is presented additionally as an example.

Table 1 General parameters and characteristics of stationary phases used in the study.

Column

Bonded moiety

Carbon content [%]

The primary coverage density of aminopropyl groups [μmol m−2 ]

silica-Amino TSKgel – NH2 amino-(Gly)1 amino-(Gly)3 amino-(Asp)1 amino-(Ala)2 amino-[Fmoc-Tyr(tBu)] amino-(Met)1 amino-(Met)2 amino-(Leu)1 amino-(Leu)2 amino-(Leu)3 amino-(Phe)1 amino-(Phe)2

aminopropyl amino alkyl glycine tripeptide of glycine aspartic acid dipeptide of alanine N-(9-fluorenylmethoxy carbonyl)-O-tert-butyl-l-tyrosine methionine dipeptide of methionine leucine dipeptide of leucine tripeptide of leucine phenylalanine dipeptide of phenylalanine

3.31 – 4.61 5.29 6.61 6.15 9.71

3.13 – 3.24 3.00 2.95 3.00 3.13

– – 1.67 3.13 2.59 2.90 0.75

125 × 4.6 150 × 4.6

5.08 7.88 7.39 10.37 12.13 8.15 11.57

3.13 3.13 3.24 3.24 3.24 2.95 2.94

0.99 1.61 1.92 3.33 4.14 1.63 2.88

125 × 4.6

Table 1. The following amino acid- and peptide-silica stationary phases were employed: amino-(Gly)1 , amino-(Gly)3 , amino-(Asp)1 , amino-(Ala)2 , amino-(Leu)1 , amino-(Leu)2 , amino-(Leu)3 , amino(Phe)1 , amino-(Phe)2 , amino-(Met)1 , amino-(Met)2 and amino[Fmoc-Tyr(tBu)]. The solid support of the stationary phases was ˚ Kromasil 100, with particle diameter 5 μm, pore diameter 100 A, pore volume 0.9 mL/g, surface area 310 m2 /g (Akzo Nobel Bohus, Sweden).

The amount of bonded amino acids ligands [μmol m−2 ]

Column dimensions [mm]

According to the chemical properties of the analytes (i.e. amino acids), the stationary phases were tested using binary acetonitrile and water mobile phase. Isocratic elution was used with an acetonitrile range between 65 and 85% v/v. Depending on the stationary phase polarity, the volumetric ratio of the mobile phase components varied, in order to obtain comparable retention factor for all materials. The amino-(Met)2 stationary phase was investigated using 88% of ACN v/v; amino-(Met)1 , amino-[Fmoc-Tyr(tBu)],

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

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amino-(Leu)2 , amino-(Leu)3 , and amino-(Phe)2 were tested using 85% of ACN v/v; amino-(Asp)1 , amino-(Gly)3 , amino-(Ala)2 , amino(Leu)1 , and amino-(Phe)1 using 80% of ACN v/v; amino-(Gly)1 and TSKgel-NH2 using 77% of ACN v/v, and silica-Amino using 65% of ACN v/v. Flow rate and injection volume were scaled for 1 mL/min and 5 μL (10 μL for l-homoserine and l-tyrosine), respectively. The retention data were collected from the three replicate injections of the particular standard. The temperature of the column was set as 30 °C. The ELSD parameters were as follows: the evaporation temperature of the chromatographic eluent was 50 °C, the gas (nitrogen) flow rate was set at 3.27 bar, and the gain was 1. 2.2. Chemicals and materials The following reagents were used for chemical modification of the silica support: Fmoc-Met-OH, Fmoc-Tyr(tBu)OH, N,N -dicyclohexylcarbodiimide (DCC), piperidine, anhydrous dichloromethane (DCM), anhydrous N,N-dimethylformamide (DMF) (Merck, KGaA, Darmstadt, Germany). Organic solvents used during synthesis: methanol and toluene were purchased from J.T. Baker (Deventer, The Netherlands). HPLC-grade acetonitrile was purchased from J.T. Baker, Deventer, The Netherlands. Water was purified using the Milli-Q system (Milli-pore, El Paso, TX, USA) in our laboratory. The amino acids standards (l-valine, l-proline, l-methionine, l-leucine, l-threonine, l-tryptophan, l-alanine, l–serine, l-asparagine, l-isoleucine, lphenylalanine, l-tyrosine, glycine, l-homoserine, l-citrulline, lcarnitine, taurine, creatine, betaine, β -alanine, 4-aminobutyric acid) were purchased from Merck, KGaA, Darmstadt, Germany. The stock solutions of these compounds were prepared by dissolving a weighed amount of each amino acid in deionized water to concentration 1 mg/mL (0.4 mg/mL for l-tyrosine and l-homoserine).

2.3. Stationary phase synthesis The phases with covalently bonded methionine (amino-(Met)1 ) and dipeptide of methionine (amino-(Met)2 ) were prepared in compliance with procedure described in the previous work [11]. In the case of the third material, the synthesis was carried out according to the reported process, however the protecting groups i.e. tert-butyl (tBu) and 9-fluorenylmethyloxycarbonyl (Fmoc) moieties were not removed. As a result, amino acid-based stationary phase with modified structure was prepared and called amino-[FmocTyr(tBu)]. 2.4. QSRR analysis A group of 15 proteinogenic and non-proteinogenic amino acids as training analytes and 6 proteinogenic amino acids as test (validation) solutes were tested during the research. The chemical structures of the analytes and their grouping into two sets are presented in Fig. 2. For each of analyte the molecular descripR tors were calculated using HyperChem Release 7 package with the extension ChemPlus (Hyper-Cube, Waterloo, Canada). Before molecular modeling all amino acids structures were drawn using ACD/ChemSketch Freeware (Toronto, Ontario, Canada). The energy minimization process of amino acids structures was performed by the semi-empirical AM1 method. The Polak–Ribiere algorithm was used for calculations. The molecular descriptors were calculated based on the geometry-optimized structures of the analytes. The following molecular descriptors were determined: van der Waals (VWS) and solvent-accessible surface areas (SAS), hydration energy

(HE), solvent-accessible surface-bounded molecular volume (V), total energy (TE), molar refractivity (R), polarizability (PR), mass (M), dipole moment (μ), heat of formation (HF), point charge (Pch ), and electron excess charge of the most negatively charged atom (δ Min ). In addition, hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), polar surface area (P), and the effective partition coefficient for dissociative systems (log D) were taken from the ChemSpider database. The log of the octanol-water partition coefficient (log P) of l-threonine, glycine, l-alanine, l-lysine, l-histidine, l-valine, l-methionine, l-tyrosine, l-isoleucine, l-leucine, l-phenylalanine, and l-tryptophan was taken from experimental data [56]. The log P values of l-proline, l–serine, l-asparagine, and l-arginine were also extracted from experimental data [57]. For other analytes the log P values were taken from DrugBank database. In order to avoid information overlap in the descriptors and cross-correlation of predictors in regression analysis, the descriptors were correlated with each other. In a group of original descriptors, 6 descriptors were highly correlated (the correlation coefficient was higher than 0.9). Further, the descriptor with the highest correlation with log k of each tested column was used for the regression analysis [33]. Finally, the QSRR models were determined using 13 molecular descriptors listed in Table 2. The multiple linear regression analysis (MLR) was ultimately used to find the quantitative relationship between the retention data (Table 3) and the calculated structural descriptors (Table 2). This stage was performed with the application of Statistica 7.1. DataMiner software (Statsoft, Cracow, Poland) running on the Windows platform. The retention data (log k) for 15 training set of amino acids were regressed with their 13 molecular descriptors. In consequence, the QSRR models were elucidated. The QSRR equations included the regression coefficients with the standard deviation, and have been statistically characterized with the use of the following parameters: the standard error of the estimate (s), the value of the Fisher test (F), the square of the multiple correlation coefficient (R2 ), and the level of significance of equations (p). The best derived two- and three-parameter QSRR models for each of the tested columns were then used to calculate retention times (tR ) for training and validation sets of amino acids. This stage was performed in order to investigate the predictive ability of the obtained descriptor-based models. 3. Results and discussion 3.1. Instrumental characterization The prepared amino acid- and peptide-silica stationary phases, i.e. amino-(Met)1 , amino-(Met)2 , and amino-[Fmoc-Tyr(tBu)] were characterized by elemental analysis. The results of these investigations are listed in Table 1. It should be emphasized that the description of the synthesis and characterization of other amino acid- and peptide-silica stationary phases used in this study were reported elsewhere [11,58]. In the wake of bonding subsequent methionine molecules to the peptide chain an increase of carbon content was observed. As a result, the coverage density increased approximately two times. In the case of amino-[Fmoc-Tyr(tBu)] stationary phase, the coverage density of bonded ligand was equal 0.75 μmol/m2 . Due to the steric hindrance, the coverage of bonded Fmoc-Tyr(tBu)-OH ligands is lower than the primary coverage density of amino groups. It should also be noted that the prepared stationary phases are characterized by a heterogeneous surface of chemically bonded ligands. This is related with the fact that the bonding of amino acids could occur via two active centers. One of them was an amino group embedded from already bonded amino acid, which resulted in the formation of a layer of dipeptide. The bonding of amino acids could also occur on the unbonded amino groups of the support [11].

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

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Fig. 2. Structures of the tested amino acids with side chains charge at pH 7.4 (in the bracket the full names, the three- and one-letter abbreviations of amino acids names are included). The pKa values of α -amino and α -carboxyl groups are equal approximately 9 and 2.2, respectively. The pKa values were taken from the DrugBank database.

The synthesized materials were also characterized by solid state CP/MAS NMR measurements. Obtained NMR spectra for the synthesized stationary phases are shown in Figs. 3 and 4. As is displayed in Figs. 3 and 4 for all stationary phases, the signal at about δ = +170 ppm results as a consequence of the peptide bonds among the amino acids moiety. The signal in this range of chemical shift scale is distinctive for the amino acid- and peptide-silica columns. This can be also confirmed by the comparison of silicaamino and amino acid-silica columns spectra (Fig. 3). In the range 13 C

of chemical shift (δ ) values from +9 to +60 ppm, peaks corresponding to the carbon atoms derived from an aliphatic system (alkyl bonded ligands) can be observed. The chemical shifts were modified by the presence of different substituents (e.g. amino, amide, and ester groups). According to Fig. 4, the resonance in the range 115–130 ppm was assigned to the carbon atoms of the aromatic groups that are characteristic for the bonded tyrosine molecules and Fmoc moiety. On the other hand, the signal at about δ = +27 ppm is assigned to the carbon atom on tert-butyl group

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

VWS [A˚ 2 ]

HE [kcal/mol]

log P

HBA

HBD

log D (pH∼7)

P [A˚ 2 ]

μ

TE [a.u.]

HF [kcal/mol]

Pch [D]

δ Min [e− ]

274.37 243.25 304.17 250.21 295.14 248.30 201.14 348.04 394.70 264.81 280.07 271.32 346.97 242.06 276.74

155.73 134.68 176.38 147.64 187.75 143.09 92.86 206.42 218.58 142.69 136.13 159.93 171.23 113.04 133.70

−8.32 −7.89 −8.14 −15.58 −11.35 −14.58 −11.56 −17.40 −7.78 −15.82 −14.85 −17.16 −4.98 −11.93 −11.58

−2.10 −2.62 −1.57 −3.48 −1.43 −3.50 −3.00 −3.30 −2.90 −3.30 −2.20 −2.90 −2.70 −3.30 −3.00

3 3 3 5 3 4 3 6 4 4 4 5 3 3 3

3 2 3 5 3 4 3 6 5 4 3 4 1 3 3

−2.16 −2.76 −1.86 −4.00 −1.46 −3.33 −3.20 −3.70 −3.61 −3.48 −5.20 −3.98 −3.12 −3.27 −3.22

63 49 63 106 63 84 63 118 60 84 89 90 37 63 63

3.088 2.034 3.031 4.022 2.813 1.650 2.713 4.178 9.357 2.350 2.231 1.603 5.348 1.592 2.696

−60.30 −59.28 −66.03 −73.46 −79.09 −66.37 −43.13 −93.02 −83.76 −66.37 −63.05 −69.73 −60.51 −48.86 −54.58

−114.31 −100.46 −119.80 −142.73 −75.66 −155.42 −98.10 −147.50 7.59 −156.05 −128.11 −54.36 73.34 −105.03 −111.74

2.495 2.572 2.377 3.344 2.163 1.264 2.282 4.418 10.136 2.727 1.545 0.957 5.646 1.904 2.361

−0.3549 −0.3643 −0.3547 −0.4209 −0.3563 −0.3515 −0.3523 −0.4020 −0.3651 −0.3534 −0.9496 −0.4209 −0.3307 −0.3629 −0.3579

322.69 275.44 222.66 290.60 227.07 309.44

178.65 214.66 121.86 173.37 114.32 196.61

– −13.17 −16.73 −7.65 −9.95 −18.24

−1.87 −1.04 −3.00 −1.69 −2.83 −1.80

3 4 4 3 3 4

3 4 4 3 3 4

−1.93 −1.06 −3.65 −1.86 −2.89 −2.24

89 79 84 63 63 84

1.489 2.950 2.552 2.835 2.933 1.755

−67.45 −96.59 −60.64 −66.03 −48.86 −90.87

−107.31 −46.24 −149.14 −119.42 −103.30 −120.12

2.310 3.080 2.431 2.271 2.382 1.777

−0.3569 −0.3275 −0.3702 −0.3596 −0.3329 −0.3599

Training dataset l-Valine l-Proline l-Leucine l-Asparagine l-Phenylalanine l-Threonine Glycine l-Citrulline l-Carnitine l-Homoserine Taurine Creatine Betaine β -Alanine GABA Test dataset l-Methionine l-Tryptophan l-Serine l-Isoleucine l-Alanine l-Tyrosine

SAS – solvent accessible surface area, VWS – van der Waals surface area, HE – hydration energy, log P - logarithms of octanol/water partition coefficient, HBA – number of hydrogen bond acceptors, HBD – number of hydrogen bond donors, log D – distribution coefficient at pH = 7.4, P – polar surface area, μ – dipole moment, TE – total energy, HF – heat of formation, Pch – point charge, δ Min – electron excess charge of the most negatively charged atom.

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Amino acids

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Table 2 Molecular descriptors of the tested amino acids.

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log k Amino acid

0.382 0.530 0.185 0.668 0.128 0.558 0.670 0.752 0.711 0.603 0.375 0.580 0.397 0.731 0.709

(±0.0003) (±0.0003) (±0.0004) (±0.0004) (±0.0001) (±0.0001) (±0.0004) (±0.0004) (±0.001) (±0.001) (±0.0002) (±0.0002) (±0.001) (±0.001) (±0.005)

ACN/H2 O (80/20v/v ) amino-(Gly)3 0.448 0.608 0.275 0.796 0.300 0.677 0.738 0.845 0.846 0.675 0.486 0.666 0.462 0.834 0.893

(±0.0003) (±0.002) (±0.001) (±0.002) (±0.001) (±0.002) (±0.002) (±0.001) (±0.0004) (±0.001) (±0.001) (±0.001) (±0.003) (±0.001) (±0.003)

ACN/H2 O (80/20v/v ) amino-(Asp)1 0.404 0.520 0.255 0.759 0.275 0.651 0.741 0.884 0.803 0.730 0.481 0.687 0.413 0.868 0.939

(±0.001) (±0.001) (±0.001) (±0.001) (±0.0004) (±0.002) (±0.001) (±0.001) (±0.001) (±0.002) (±0.0003) (±0.0003) (±0.0002) (±0.001) (±0.001)

ACN/H2 O (80/20v/v ) amino-(Ala)2 0.271 0.465 0.080 0.568 0.086 0.464 0.555 0.661 0.793 0.498 0.283 0.489 0.348 0.700 0.799

(±0.0001) (±0.001) (±0.0002) (±0.0003) (±0.002) (±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.003) (±0.0003) (±0.001) (±0.001)

ACN/H2 O (85/15v/v ) amino-[Fmoc-Tyr(tBu)] 0.435 0.663 0.322 0.759 0.496 0.673 0.771 0.856 0.595 0.743 0.574 0.547 0.501 0.748 0.767

(±0.001) (±0.001) (±0.001) (±0.002) (±0.001) (±0.002) (±0.001) (±0.004) (±0.003) (±0.002) (±0.001) (±0.001) (±0.001) (±0.002) (±0.004)

ACN/H2 O (85/15v/v ) amino-(Met)1 0.233 0.406 0.109 0.542 0.281 0.480 0.564 0.554 0.491 0.465 0.315 0.342 0.058 0.655 0.717

(±0.003) (±0.003) (±0.003) (±0.003) (±0.004) (±0.002) (±0.001) (±0.001) (±0.002) (±0.002) (±0.002) (±0.001) (±0.002) (±0.002) (±0.003)

ACN/H2 O (88/12v/v ) amino-(Met)2 0.290 0.486 0.133 0.592 0.301 0.501 0.680 0.664 0.558 0.561 0.328 0.504 0.107 0.795 0.844

(±0.002) (±0.003) (±0.004) (±0.003) (±0.004) (±0.004) (±0.004) (±0.001) (±0.003) (±0.003) (±0.003) (±0.001) (±0.001) (±0.004) (±0.0003)

log k Amino acid

0.282 0.484 0.123 0.466 0.098 0.412 0.531 0.592 0.745 0.449 0.188 0.488 0.359 0.689 0.739

(±0.001) (±0.0004) (±0.001) (±0.001) (±0.001) (±0.0002) (±0.0004) (±0.0003) (±0.001) (±0.001) (±0.0002) (±0.001) (±0.0002) (±0.001) (±0.003)

ACN/H2 O (85/15v/v ) amino-(Leu)2 0.449 0.628 0.297 0.656 0.277 0.614 0.775 0.824 0.722 0.657 0.366 0.657 0.373 0.877 0.844

(±0.003) (±0.002) (±0.003) (±0.002) (±0.002) (±0.004) (±0.002) (±0.001) (±0.006) (±0.002) (±0.003) (±0.003) (±0.001) (±0.003) (±0.001)

ACN/H2 O (85/15v/v ) amino-(Leu)3 0.409 0.583 0.250 0.612 0.225 0.576 0.738 0.759 0.729 0.618 0.336 0.603 0.302 0.871 0.863

(±0.0004) (±0.0004) (±0.001) (±0.002) (±0.001) (±0.001) (±0.001) (±0.001) (±0.002) (±0.001) (±0.002) (±0.001) (±0.001) (±0.001) (±0.002)

ACN/H2 O (80/20v/v ) amino-(Phe)1 0.285 0.501 0.125 0.481 0.109 0.409 0.517 0.595 0.827 0.424 0.126 0.511 0.428 0.684 0.769

(±0.002) (±0.001) (±0.001) (±0.004) (±0.001) (±0.003) (±0.001) (±0.001) (±0.004) (±0.002) (±0.001) (±0.002) (±0.002) (±0.001) (±0.0004)

ACN/H2 O (85/15v/v ) amino-(Phe)2 0.225 0.473 0.092 0.438 0.225 0.377 0.526 0.549 0.762 0.422 0.105 0.467 0.241 0.719 0.821

(±0.001) (±0.001) (±0.0003) (±0.005) (±0.001) (±0.003) (±0.001) (±0.002) (±0.0002) (±0.002) (0.001) (±0.003) (±0.0003) (±0.001) (±0.002)

ACN/H2 O (77/23v/v ) TSKgel – NH2 0.582 0.674 0.428 0.972 0.524 0.850 0.855 0.813 0.356 0.691 0.619 0.564 0.442 0.581 0.323

(±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.001) (±0.004) (±0.001) (±0.002) (±0.004) (±0.001) (±0.004) (±0.002)

ACN/H2 O (65/25v/v ) silica-Amino 0.378 (±0.001) 0.290 (±0.0004) 0.220 (±0.001) 0.684 (±0.002) 0.354 (±0.001) 0.643 (±0.001) 0.577 (±0.001) 0.602 (±0.005) 0.255 (±0.0004) 0.577 (±0.004) 0.636 (±0.014) 0.149 (±0.001) −0.034 (±0.001) 0.510 (±0.001) 0.550 (±0.001)

7

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l-Valine l-Proline l-Leucine l-Asparagine l-Phenylalanine l-Threonine Glycine l-Citrulline l-Carnitine l-Homoserine Taurine Creatine Betaine β -Alanine GABA

ACN/H2 O (80/20v/v ) amino-(Leu)1

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l-Valine l-Proline l-Leucine l-Asparagine l-Phenylalanine l-Threonine Glycine l-Citrulline l-Carnitine l-Homoserine Taurine Creatine Betaine β -Alanine GABA

ACN/H2 O (77/23v/v ) amino-(Gly)1

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Table 3 The logarithm of retention factor values (±standard deviation) for amino acids analyzed on the tested stationary phases with indicated mobile phase composition.

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Fig. 3.

13

C CP MAS NMR spectra of amino-(Met)1 and amino-(Met)2 stationary phases compared with silica-amino spectrum.

Fig. 4.

13

C CP MAS NMR spectrum of amino-[Fmoc-Tyr(tBu)] stationary phase.

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9

Fig. 5. Chromatograms of amino acids training set: 1 – l-leucine, 2 – l-phenylalanine, 3 – l-valine, 4 – betaine, 5 – taurine, 6 – l-proline, 7 – creatine, 8 – l-threonine, 9 – l-homoserine, 10 – glycine, 11 – l-asparagine, 12 – β -alanine, 13 – l-citrulline, 14 – l-carnitine, 15 – GABA obtained on A – amino-(Gly)3 , B – amino-(Asp)1 , C – amino-(Ala)2 , D – amino-(Leu)1 . Experimental data are contained in Section 2.1.

derived from the second protection moiety. The particular information may be found on the spectra (Figs. 3 and 4). Consequently, the solid state 13 C CP/MAS NMR spectra established the successful immobilization of amino acids and peptide on the silica support [9,13,59,60]. 3.2. Retention relationships The retention of amino acids on the amino acid- and peptidebased stationary phases and amino-bonded phases was investigated. The logarithm values from the retention factor of amino acids together with the standard deviation are listed in Table 3. Depending on the sequence of the bonded amino acids, the polarity of the material was different. In consequence, the mobile phase composition was adjusted individually for each column in order to receive comparable retention factor of the tested solutes. The details of the mobile phase compositions and chromatographic parameters for each tested column were provided in Section 2.1 and Table 3. The chromatograms of amino acids training set for four selected columns are presented in Fig. 5. The elution order of amino acids was predominantly similar on the amino acid-based stationary phases. Minor changes in selectivity, however were observed. Since previous studies have shown that the surface properties of the tested materials vary with the properties of the amino acids side chains, the

tested columns should be considered according to these changes [17,58,61]. Indeed, in the case of most of the studied columns, one of the first eluting solutes comprised phenylalanine, leucine, and valine. This was consistent with the retention mechanism occurring in HILIC, as elution order changed from hydrophobic to hydrophilic solutes [62,63]. One of the parameters describing the hydrophilic/hydrophobic properties is the effective partition coefficient for dissociative systems – log D. This parameter includes the equilibrium concentrations of unionized and ionized species of a compound in both 1-octanol and water phase at 25 °C. A low log D values means high hydrophilicity and low hydrophobicity and vice versa [64]. Despite of relatively strong hydrophilic properties (log D = −3.12), betaine was also included in the group of firsteluting analytes, while compounds with comparable polarity were eluted later. This observation may be related to the repulsion forces between positively charged quaternary amino group in the betaine structure with positively charged amino groups localized on the stationary phase surface. On the other hand, the strongest interactions with the amino acid-and peptide-based columns surfaces revealed β -alanine and GABA. The pKa values of their β - and γ -amino groups are higher (around 10.3 – DrugBank database) in comparison with pKa of α -amino moiety (around 9 – DrugBank database) (Fig. 2). This may lead to a shift in an

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equilibrium constant towards non-protonated form. Thus, the negatively charged carboxylic groups, which comprised a dominant charged in these species, are attracted by positively charged amino groups of amino acids and peptides bonded onto silica surface. It should be also emphasized that the elution order was almost the same for columns with the same type of amino acid, e.g. leucine, dipeptide of leucine, and tripeptide of leucine. This suggests that the elongation of amino acid sequence with the same type of amino acid does not change the retention mechanism. However, it may provide an increase in retention, and thus in selectivity. In the case of amino-bonded columns, the elution order was changed with regard to materials of interest. In the case of commercial TSKgel-NH2 column, GABA was the first to be eluted, whereas the most retentive compound was asparagine. Similarly, laboratory-prepared silica-Amino column provided the highest retention of asparagine. These relations may be an evidence of the residual silanols influence on the retention of basic compounds on the amino-based columns. Since the chromatographic measurements were performed in different mobile phase composition, the direct comparison of the retentiveness of the studied stationary phases cannot be performed. However, it should be kept in mind that this feature had impact on the elution strength changes. Thus, it can be considered that the most retentive materials were stationary phases with chemically bonded tripeptide of glycine – amino-(Gly)3 , aspartic acid molecule – amino-(Asp)1 , and dipeptide of alanine – amino-(Ala)2 . This is in compliance with the previous research that showed the highest excess adsorption of water on those stationary phases and the higher hydrophilic retention than in the case of modifications with leucine and phenylalanine [17,58,65]. In this research, it can also be noted that the stationary phases modified with amino acids containing hydrophobic side chains (e.g. benzene ring, isobutyl group) provide lower retention of hydrophilic solutes. The stationary phases with bonded methionine – amino(Met)1 , dipeptide of methionine – amino-(Met)2 , and N-(9Fluorenylmethoxycarbonyl)-O-tert-butyl-L-tyrosine – amino[Fmoc-Tyr(tBu)] are materials that have not yet been studied in terms of the chromatographic behavior. According to the theoretical considerations and observed results, these newly-synthesized materials could belong to the group together with leucine- and phenylalanine-based materials. In some extent, the similar side chains structures of methionine with isoleucine and tyrosine with phenylalanine can prove above-mentioned statement. However, the additional polar groups (e.g. ester, ether) in the case of amino[Fmoc-Tyr(tBu)] column induces slightly stronger polar surface properties in comparison with the other columns belonging to this group. Moreover, the increase of amino acids retention was observed with the elongation of methionine peptide chain. Despite of the structural similarities of the side chains between columns, the different elution order and selectivity were observed. This confirms that presence of polar groups and heteroatoms have additional influence on the retention mechanism. 3.3. QSRR analysis – retention mechanism The main objective of the research was to study the retention mechanism of proteinogenic and non-proteinogenic amino acids on the amino acid- and peptide-silica stationary phases. The application of this group of analytes was also aimed at investigation of the effect of the structural similarities between the analytes molecules and chemically bonded ligands localized on the stationary phase surface. For these purposes, the QSRR analysis was applied. The chromatographic retention of amino acids represents the dependent variables, while independent variables comprise the structural descriptors of the tested solutes. The analyte-related parameters (descriptors) are listed in Table 2 and represented the follow-

ing properties: solvent accessible surface area (SAS), van der Waals surface area (VWS), hydration energy (HE), log P, number of hydrogen bond acceptors (HBA), number of hydrogen bond donors (HBD), distribution coefficient at pH = 7.4 (log D), polar surface area (P), dipole moment (μ), total energy (TE), heat of formation (HF), point charge (Pch ), and the highest electron excess on a single atom in analyte molecule (δ Min ). The two- and three-parameter QSRR equations employing the structural properties of amino acids were found to describe isocratic retention in respect of the log k. For most of the tested columns, at least 2 (two-parameter) and 1 (three-parameter) statistically significant descriptor-based models were noted. The highest number of statistically significant QSRR equations (2 twoparameter and 14 three-parameter) were observed for the amino(Gly)3 column. On the other hand, no statistically significant two-parameter QSRR equation was reported for amino-(Ala)2 , amino-(Leu)1 , and amino-(Phe)2 stationary phases. This may verify that the amino acids with hydrophobic side chains in the ligand structures reduce selectivity. This effect was smaller in the case of amino-(Leu)2 – 3 and amino-(Phe)1 stationary phases for which more QSRR equations were reported. The methionine-based columns also revealed only few statistically significant descriptorbased models. However, the application of other types of descriptors could be done in order to perform QSRR analysis. The two- and three-parameter QSRR models with the highest determination coefficients for each of the tested column are listed in Table 4. Since, these models describe similarities and differences of intermolecular interactions at 0.05 level of significance and represent the most informative models, the retention mechanism of amino acids on the amino acid-based materials will be described. In the case of amino acid- and peptide-silica stationary phases, the highest electron excess on a single atom in amino acid molecules (δ Min ) has the greatest influence on their retention mechanism. This can be observed mainly for the amino(Gly)1 , amino-(Gly)3 , amino-(Leu)2 , amino-(Leu)3 , and amino(Phe)1 columns (Table 4). The inclusion of this predictor may signify the involvement of ion–ion electrostatic interactions between amino acids and the stationary phases [52]. As was mentioned in Section 3.2 and reported in the previous research [17,61], the tested columns possess positively charged amino groups localized at the end of the amino acid and/or peptide ligands. Moreover, the tested amino acids are ionized under the conditions used during chromatographic analysis. Therefore, the ionic interactions can occur between ionized amino acids molecules and ionized amino groups of amino acid and/or peptide ligands. It should be emphasized that residual ionized silanols can also be involved in these electrostatic interactions. The effect on retention of the particular descriptors is reflected by their regression coefficients in the QSRR models. Thus, the observed regression coefficients prove that the net effect on the retention of δ Min is positive and has the greatest influence, while the regression coefficients for the other descriptors are significantly lower (Table 4). This suggests that in the case of amino-(Gly)1 , amino-(Gly)3 , amino-(Leu)2 , amino-(Leu)3 , and amino-(Phe)1 columns ion–ion electrostatic interactions constitute an overriding importance in the retention of amino acids on their surfaces. Despite the fact that for the other amino acid- and peptide-silica stationary phases (amino-(Ala)2 , amino[Fmoc-Tyr(tBu)], amino-(Met)1 , amino-(Met)2 , amino-(Leu)1 ) δ Min descriptor is not included in the QSRR equations, electrostatic interactions also play a significant role in the retention mechanism of amino acids. The inclusion of the point charge descriptor (Pch ) as a predictor for the retention indicates the aforementioned ion–ion electrostatic interactions as one process of retention in the studied system. Nevertheless, the influence of the Pch descriptor on the retention is significantly lower in comparison with the δ Min

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11

Table 4 Regression coefficients (k1 , k2 , k3 , k4 ) with standard deviations, the standard error of the estimate (s), the value of the Fisher test (F), number of data amino acids used to derive regression (n), the square of the multiple correlation coefficient (R2 ), and the level of significance of equations p < 0.05 for the investigated stationary phases. Equation amino-Gly1 log k = k1 + k2 log D + k3 δ Min log k = k1 + k2 VWS + k3 HBA + k4 Pch amino-Gly3 log k = k1 + k2 log D + k3 δ Min log k = k1 + k2 VWS + k3 HBD + k4 Pch amino-Asp1 log k = k1 + k2 HBD + k3 TE log k = k1 + k2 VWS + k3 HBD + k4 HF amino-Ala2 – log k = k1 + k2 VWS + k3 HBD + k4 Pch amino-[Fmoc-Tyr(tBu)] log k = k1 + k2 VWS + k3 HBD log k = k1 + k2 VWS + k3 TE + k4 Pch amino-Met1 log k = k1 + k2 VWS + k3 HBD log k = k1 + k2 SAS + k3 HE + k4 Pch amino-Met2 log k = k1 + k2 HBD + k3 TE log k = k1 + k2 SAS + k3 HE + k4 Pch amino-Leu1 – log k = k1 + k2 HBD + k3 TE + k4 Pch amino-Leu2 log k = k1 + k2 logD + k3 δ Min log k = k1 + k2 HBD + k3 μ + k4 Pch amino-Leu3 log k = k1 + k2 logD + k3 δ Min log k = k1 + k2 VWS + k3 HBD + k4 P amino-Phe1 log k = k1 + k2 logD + k3 δ Min log k = k1 + k2 HBD + k3 TE + k4 Pch amino-Phe2 – log k = k1 + k2 VWS + k3 HBD + k4 P TSKgel – NH2 log k = k1 + k2 VWS + k3 TE log k = k1 + k2 SAS + k3 VWS + k4 logD silica-Amino log k = k1 + k2 VWS + k3 HBD log k = k1 + k2 VWS + k3 HBD + k4 HF

k1

k2

k3

k4

s

F

n

R2

0.2994 (±0.1049) 0.5456 (±0.1536)

−0.1979 (±0.0387) −0.0055 (±0.0012)

0.9812 (±0.2585) 0.1655 (±0.0320)

– 0.0638 (±0.0157)

0.0135 0.0109

14 12

15 15

0.6916 0.7726

0.3699 (±0.1087) 0.6998 (±0.1371)

−0.1841 (±0.0343) −0.0044 (±0.0011)

0.8673 (±0.2424) 0.1336 (±0.0225)

– 0.0457 (±0.0149)

0.0136 0.0115

15 13

15 15

0.7115 0.7756

0.7861 (±0.2235) 0.9903 (±0.2327)

0.1826 (±0.0468) −0.0056 (±0.0016)

0.0120 (±0.0045) 0.2003 (±0.0451)

– 0.0020 (±0.0009)

0.0253 0.0211

8 7

15 15

0.5591 0.6624

– 0.7397 (±0.2165)

– −0.0056 (±0.0017)

– 0.1050 (±0.0363)

– 0.0746 (±0.0235)

– 0.0240

6

– 15

– 0.6286

0.7653 (±0.1456) 0.7770 (±0.1361)

−0.0028 (±0.0010) −0.0100 (±0.0020)

0.0837 (±0.0265) −0.0187 (±0.0045)

– 0.0428 (±0.0151)

0.0123 0.0089

6 8

15 15

0.5135 0.6869

0.5946 (±0.1698) 0.7613 (±0.3304)

−0.0037 (±0.0012) −0.0033 (±0.0013)

0.1140 (±0.0309) −0.0299 (±0.0112)

– 0.0761 (±0.0296)

0.0174 0.0214

9 4

15 15

0.5887 0.5356

0.8052 (±0.2170) 0.8754 (±0.3936)

0.1820 (±0.0454) −0.0037 (±0.0015)

0.0144 (±0.0044) −0.0341 (±0.0133)

– 0.0842 (±0.0353)

0.0238 0.0304

8 4

15 15

0.5805 0.5100

– 0.7438 (±0.2209)

– 0.1341 (±0.0454)

– 0.0141 (±0.0047)

– 0.0525 (±0.0202)

– 0.0236

– 5

– 15

– 0.5637

0.4187 (±0.1167) 0.4135 (±0.1225)

−0.1893 (±0.0430) 0.0993 (±0.0314)

1.0744 (±0.2875) −0.2901 (±0.0946)

– 0.24442 (±0.0819)

0.0167 0.0198

11 6

15 15

0.6376 0.6066

0.3563 (±0.1529) 1.0661 (±0.2369)

−0.2168 (±0.0604) −0.0048 (±0.0014)

1.2061 (±0.3647) 0.2453 (±0.0648)

– −0.0083 (±0.0034)

0.0244 0.0215

7 6

15 15

0.5498 0.6340

0.3024 (±0.1386) 0.7242 (±0.2423)

−0.2298 (±0.0548) 0.1249 (±0.0498)

1.4525 (±0.3308) 0.0137 (±0.0052)

– 0.0636 (±0.0222)

0.0183 0.0283

11 4

15 15

0.6496 0.5462

– 0.9359 (±0.2774)

– −0.0038 (±0.0016)

– 0.2744 (±0.0759)

– −0.0119 (±0.0040)

– 0.0294

– 4

– 15

– 0.5482

0.6205 (±0.1647) 1.0014 (±0.2517)

−0.0102 (±0.0021) −0.0063 (±0.0019)

−0.0239 (±0.0053) 0.0069 (±0.0028)

– −0.1026 (±0.0438)

0.0142 0.0192

12 5

15 15

0.6727 0.5927

0.6693 (±0.1675) 0.4191 (±0.1764)

−0.0046 (±0.0011) −0.0028 (±0.0012)

−0.1350 (±0.0305) 0.0817 (±0.0342)

– −0.0016 (±0.0007)

0.0169 0.0121

13 14

15 15

0.6814 0.7903

descriptor reported for the amino-(Gly)1 , amino-(Gly)3 , amino(Leu)2 , amino-(Leu)3 , and amino-(Phe)1 columns. The absence in the QSRR equations of δ Min descriptor and slight impact of Pch descriptor on the retention may suggest the occurrence of additional interactions determining the retention mechanism of amino acids on the amino acid- and peptide-silica stationary phases. One of the exceptions to the above mentioned dependencies was the stationary phase with chemically bonded aspartic acid molecule – amino-(Asp)1 . As can be seen in Table 4, the QSRR models derived for this column do not include any descriptor associated with the occurrence of ionic interactions. This stationary phase is the only one that contains both ionic moieties i.e. positively charged amino group and negatively charged carboxylic group. Thus, the interactions between this zwitterionic stationary phase and zwitterionic analytes are govern mainly by hydrophilic/hydrophobic interactions and hydrogen bonding due to the inclusion of the number of hydrogen bond donors (HBD), the total energy (TE), van der Waals surface area (VWS), and the heat of formation (HF) in the QSRR models. The meaning of the particular descriptor will be discussed in the next paragraphs. The presence of log D as retention predictor together with δ Min descriptor constitutes a specific relation that was observed for amino-(Gly)1 , amino-(Gly)3 , amino-(Leu)2 , amino-(Leu)3 , and amino-(Phe)1 columns. The log D descriptor is a measure of the

analytes polarity and its low values indicate high hydrophilic character. Thus, the lower log D, the higher the retention is observed in HILIC mode. The negative sign of regression coefficient corresponding log D descriptor confirms this relation regarding to the tested columns and conditions. The presence of this descriptor solely in combination with ionic interactions-related descriptor (δ Min ) may suggest that its meaning may be associated with both hydrophilic and ion–ion interactions. Data presented in Table 4 also indicate that the number of hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA) in the case of amino-(Gly)1 column have a relevant and comparable influence on the retention mechanism of amino acids on the amino acid- and peptide-silica stationary phases. The inclusion of these descriptors in the derived QSRR models signifying hydrogen bonding as another mechanism of retention for the studied system. According to Fig. 1, the tested stationary phases possess in their structure both acceptors (oxygen atom in carbonyl moiety) and donors (nitrogen atoms in the amide and amine groups) hydrogen bond centers. As a result of structural similarity between analytes and chemically bonded ligands, both hydrogen bond centers appear also in the structure of amino acids (Fig. 2). Therefore, it can be assumed that hydrogen bond creation constitutes also valid importance in the retention of amino acids on the tested stationary phases surfaces. It should be also emphasized that descriptors related to the occurrence of hydrogen bonding was included

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[m5G;September 24, 2019;15:56]

M. Skoczylas, S. Bocian and B. Buszewski / Journal of Chromatography A xxx (xxxx) xxx

in all the mathematical models derived for the amino acid- and peptide-silica stationary phases. It may be conclude that the creation of hydrogen bond with polar analytes is characteristic feature of the amino acid- and peptide-based columns, while the presence of electrostatic interactions is more diversified within this groups of chromatographic packings. The effects on retention of the HBD and HBA are reflected by their regression coefficient in the obtained models. First, HBD and HBA descriptors revealed significantly lower (about 10 times) impact on the retention in comparison with δ Min descriptor. This suggests that the ionic interactions are dominant in the case of amino-(Gly)1 , amino-(Gly)3 , amino-(Leu)2 , amino-(Leu)3 , and amino-(Phe)1 columns. On the other hand, the models derived for amino-(Ala)2 , amino-[Fmoc-Tyr(tBu)], amino-(Met)1 , amino-(Met)2 , amino-(Leu)1 showed that HBD has higher (about 2 times) influence on the retention mechanism of amino acids than Pch descriptor (Table 4). This may suggest that for this group of columns, the combination of both mechanisms i.e. electrostatic interactions and hydrogen bonding is occurred without the dominance of any of them. Second, the positive sign of regression coefficient corresponding HBD and HBA descriptors observed in the case of all tested stationary phases indicates that the higher number of hydrogen bond donors or acceptors the higher retention of amino acids should be observed. Third, the regression coefficient values corresponding descriptors related to the hydrogen bond creation are comparable for the tested columns. This may suggest that the formation of hydrogen bonds is equally preferred for each stationary phase without the influence of the amino acid type in the sequence. For two stationary phases with the highest relative hydrophobicity – amino-(Leu)3 and amino-(Phe)2 the derived QSRR models showed higher impact (about 2 times) of HBD on the retention in comparison with other tested columns (Table 4). This may imply that the greater importance of hydrogen bonds is observed for stationary phases modified with longer hydrophobic amino acid sequences. It should however note that the ion– ion electrostatic interactions are dominant process in the retention mechanism of amino-(Leu)3 column. As can be seen in Table 4, the QSRR models derived for the second mentioned hydrophobic columns – amino-(Phe)2 did not include any descriptors related to the electrostatic interactions, similarly as amino-bonded phases and amino-(Asp)1 column. For this column, the main retention predictor was number of hydrogen bond donors, however the polar surface area (P) is included in the QSRR equation together with VWS. The influence on the retention of these both descriptors is significantly lower in comparison with the other descriptors included in the derived models. The P descriptor gives information about polar surface area of the amino acid molecules and its net effect on retention was negative. Thus, the higher polar surface area of analyte molecules, the lower retention is observed. Similar dependencies was observed for the three-parameter model derived for amino-(Leu)3 column. This may prove that the hydrophobic side chains in the peptide ligands participate in the retention of polar analytes due to the reduction of their retention with increase the polarity of amino acids. Nevertheless, this effect is observed for the longer amino acid sequences – dipeptides and tripeptides. Another predictor that is frequently included in the QSRR models is VWS. The physicochemical meaning of the VWS feature refers to the ability of the solutes molecules to interact with the chromatographic components through the hydrophobic forces. For all tested amino acid- and peptide-silica stationary phases except amino-(Met)1 , amino-(Met)2 , amino-(Leu)1 , amino-(Leu)2 , and amino-(Phe)1 this descriptor was included in two- and/or three-parameter models. The negative input to retention from VWS shows that these nonspecific intermolecular interactions are stronger between amino acids and molecules of the mobile phase

than between amino acids and the chemically bonded ligand of the stationary phases. The replacement of the VWS feature with the solvent accessible surface area (SAS) while retaining the same impact on retention could be reported for amino-(Met)1 and amino-(Met)2 columns. SAS reflects the ability of the analyte molecules to participate in non-specific intermolecular interactions. The increasing size of SAS, which determines the contact area with molecules forming the chromatographic system, indicated decreased retention of amino acids on the surface of those two columns (Table 4). In addition to the reduction of the amino acids retention, the negative sign of the regression coefficient corresponding SAS descriptor reiterated that the non-specific intermolecular interactions are stronger between analytes molecules and molecules of the mobile phase than those with the stationary phase. In the group of methionine-based columns some intra related similarities with inter related differences could be reported. For those two columns – amino-(Met)1 and amino-(Met)2 the derived models included the hydration energy (HE) descriptor which is not observed for any of the columns being tested. This descriptor reflects the energy released when molecules are attracted to water. Thus, in general, HE refers to hydration process of amino acids in the mobile phase and is considered from the point of view of hydrophilic interactions. As can be seen in Table 4, HE is inversely proportional to the retention due to the negative sign of corresponding regression coefficient. This may imply similar relation to that observed for other columns in the case of VWS feature. The higher HE of amino acids which is associated with their greater polarity, the retention is decreased. This may suggest stronger analytes interactions with the mobile phase molecules than with stationary phase ligands. In addition, this exception of presence HE descriptor in the mathematical models derived for methioninebased columns may be induce due to additional atom i.e. sulfur with meaningful electron affinity and electronegativity. The total energy (TE) descriptor constitutes another retention predictor that was included in the QSRR models derived for amino(Asp)1 , amino-[Fmoc-Tyr(tBu)], amino-(Met)2 , amino-(Leu)1 , and amino-(Phe)1 . In most cases, the TE is proportional to retention which means that the higher sum of the energy of electron and the energy of intra-nuclear repulsion of molecules (TE), their higher retention is observed. On the other hand, the negative contribution of TE feature to the retention was observed for amino-[FmocTyr(tBu)] column. The dipole moment (μ) descriptor was included only in the QSRR models calculated for the stationary phase with chemically bonded dipeptide of leucine – amino-(Leu)2 . This parameter refers to dipole – dipole and dipole – induced dipole attractive interactions of the amino acids with the components of the mobile and stationary phases. The contribution of μ in retention was relatively high and comparable to that observed for HBD. This indicates that these secondary interactions have impact on the retention of amino acid on the amino-(Leu)2 column and are competitive for the formation of hydrogen bonds. The tested amino acid- and peptide-silica stationary phases revealed some similarities and differences in the retention mechanism of amino acids which was discussed in the previous paragraphs. The comparison of their properties with amino-bonded phases also provides some overlaps and variations. The QSRR models calculated for laboratory prepared silica-Amino column included similar descriptors as retention predictors in comparison with the amino acid- and peptide-silica stationary phases (Table 4). The HBD and VWS features constituted the main predictors of retention with impact comparable to that for amino acid-based columns; however HBD was inversely proportional to the retention in the case of two-parameter model but proportional in the case of three-parameter model. The negative sign of regression coefficient

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

ARTICLE IN PRESS

JID: CHROMA

M. Skoczylas, S. Bocian and B. Buszewski / Journal of Chromatography A xxx (xxxx) xxx

corresponding HBD differentiates this stationary phase from the amino acid-based columns and indicated that the higher number of hydrogen bond donors in amino acid molecules the lower retention is observed. On the other hand, in the three-parameter model the contribution of HBD in retention was positive and similar to that observed for other tested columns; however with slightly lower impact. In this point it can be conclude that the retention mechanism of amino acids on the silica-Amino and other amino acid- and peptide-based columns was similar in terms of hydrogen bonding and hydrophobic/hydrophilic interactions. Additional descriptor included in the QSRR model which was observed only for silica-Amino column was the heat of formation (HF). This relation signifying the importance of hydrophilic interactions in the system composed of laboratory-prepared amino-bonded column and amino acids as solutes. In the case of commercial column – TSKgel – NH2 more differences in retention mechanism with regard to the amino acidand peptide-silica stationary phases could be observed. Since the descriptors related to the creation of hydrogen bond were not included in the derived models, the hydrogen bonding does not have influence on the retention mechanism of amino acids on the TSKgel – NH2 column. This is a significant difference in relation to other tested stationary phases. Moreover, the ion–ion electrostatic interactions also do not have a significant role in the retention process on both amino-bonded phases, while these interactions are overriding in the retention mechanism which occur on the amino acid- and peptide-silica surfaces. However, similarities between the discussed columns can be seen in the terms of the importance of hydrophilic/hydrophobic interactions which are induced by the presence of VWS, SAS, TE, and log D descriptors in the models calculated for TSKgel – NH2 column. Based on the derived mathematical models it can be conclude that the retention mechanism of amino acids on the amino acidand peptide-silica stationary phases is a complex phenomenon. It should be noted that the retention of ampholyte organic molecules (amino acids) on the stationary phases composed of both the anionic (residual silanols, carboxylic group) and cationic (protonated amino moiety) groups is also governed by multipoint or quadrupole type of analyte - bonded group interactions and formation of internal salts of amino acids in the stationary phases [66–69]. Thus, the investigated process is significantly affected by contributions of ion–ion electrostatic interactions effects which imply the aqueous normal phase (ANP) mechanism with dominating adsorption of solutes. However, the analytes adsorption due to hydrogen bonding also contributed to the retention, depending on the type of amino acids and on the characteristic of the stationary phase. The retention process is also formed by the hydrophilic/hydrophobic interactions between amino acids and the components of the mobile and stationary phases. Hence the investigated retention mechanism was in some extent attributed to liquid – liquid partitioning between the mobile phase and the waterenriched layer at the surface of the stationary phase. The formation of such layer was reported during the investigation of solvation process which occurs on the surface of the amino acid- and peptide-silica stationary phases and described elsewhere [58]. The occurrence of partition phenomenon may indicate on the other hand the mechanism characteristic for HILIC mode. Since the ANP mechanism is attributed rather to adsorption and HILIC to partition, the retention mechanism of amino acids on the amino acidand peptide-based columns is a combination of the two effects but with the predominance of the adsorption process [70]. 3.4. QSRR analysis – retention prediction The predictive properties of the QSRR equations were tested for each of the investigated stationary phases. For this purpose, the

[m5G;September 24, 2019;15:56] 13

derived descriptor-based models (Table 4) were used to predict the retention times of both training and test (validation) datasets. Six proteinogenic amino acids (l-methionine, l-tryptophan, l–serine, l-isoleucine, l-alanine, and l-tyrosine) were applied as testing solutes. The molecular descriptors of those compounds are listed in Table 2. The comparison of the experimental and predicted retention times (tR ) gained from the two- and three-parameter models for each of the tested columns is presented in Table 5. The agreement between experimental and predicted tR values for the training and test sets was quantified by the determination coefficient (R2 ) and the root mean square error of prediction (RMSEP) and listed in Table 5. The best predictive properties of both two-parameter and three-parameter QSRR equations indicated by the highest values of the determination coefficient (R2 ) and the lowest values of the root mean square errors of prediction (RMSEP) were reported for glycine-based columns, i.e. amino-(Gly)1 and amino-(Gly)3 . The values of R2 derived from the correlation of experimental and predicted retention times values were 0.9805 for the two-parameters model (2P-M) and 0.9731 for the three-parameters model (3P-M) 0.9883 observed on amino-(Gly)1 columns and 0.9883 for 2P-M and 0.9510 for 3P-M on amino-(Gly)3 stationary phase. The RMSEP values were in the range 0.23–0.82. These values suggest that the determined QSRR models revealed a very good ability in describing the retention behavior of the test amino acids. Plots of the predicted versus the experimental retention times for the training and test sets of these two the best models are shown in Fig. 6. The correlation between predicted and observed retention times was also relatively high for other amino acid- and peptidesilica stationary phases. The determination coefficient values for test set were in the range of 0.81–0.97. This group does not include amino-(Met)2 , amino-(Phe)2 and amino-(Leu)2 columns. The threeparameters QSRR model derived for the amino-(Leu)2 column showed no predictive abilities (R2 = 0.0931 and RMSEP = 22.93) in contrast to the two-parameters model which had a good ability to predict retention of test analytes (R2 = 0.9673 and RMSEP = 0.87). The QSRR models derived for other two columns – amino-(Met)2 and amino-(Phe)2 exhibited low accuracy in predicting retention of test amino acids. In contrast, for their shorter counterparts i.e. amino-(Met)1 and amino-(Phe)1 the descriptors-based models revealed good prediction properties (Table 5). This may indicate that the longer sequence of amino acids with hydrophobic side chains in the column the weaker ability in accurate describing retention of amino acids. It should be emphasized that this relationship, however, was not valid for leucine-based columns. For this group of columns, an increase in the number of leucine molecules in the amino acid sequence induced QSRR models with better ability in characterizing the amino acids retention. These dependencies are very general because a thorough analysis of e.g. R2 values provides that the best predictive capabilities of the QSRR models were observed for amino-(Leu)1 in the case of three-parameter model, whereas amino-(Leu)3 is characterized by the best two-parameter QSRR model. The descriptor-based models derived for amino-based columns, i.e. TSKgel – NH2 and silica-Amino were less accurate in predicting retention of amino acids with regard to QSRR models of the amino acid- and peptide-silica stationary phases. This is proven by the significantly lower R2 values for the commercial amino-column (0.5736 for 2P-M and 0.1263 for 3P-M). In the case of laboratoryprepared silica-Amino column, relatively good prediction properties were reported for the derived QSRR equations (R2 = 0.6859 for 2P-M and 0.7955 for 3P-M). It should be however emphasized that the reported poor predictive properties of the models could be overcome by using the QSRR equations including higher number of descriptors.

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

JID: CHROMA

14

Column /Amino acid

amino-(Gly)1

Training set

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

l-Valine l-Proline l-Leucine l-Asparagine Glycine l-Phenylalanine creatine GABA β -alanine taurine l-Threonine l-citrulline l-carnitine betaine l-homoserine

6.17 7.94 4.58 10.24 10.28 4.24 8.69 11.07 11.55 6.10 8.82 12.03 11.12 6.32 9.06 R2 RMSEP

6.14 7.38 5.59 10.44 8.81 4.95 10.36 8.78 8.86 6.32 9.25 9.66 10.00 8.89 9.74 0.6520 2.23

5.82 7.10 4.84 12.59 10.42 4.36 8.31 7.00 8.12 8.35 7.56 10.59 9.94 7.04 8.97 0.5561 3.03

6.31 8.37 4.78 12.01 10.72 4.96 9.34 14.61 12.97 6.73 9.53 13.25 13.27 6.45 9.50 R2 RMSEP

6.43 7.70 5.86 10.79 9.12 5.20 10.71 9.10 9.18 6.94 9.55 10.01 10.31 9.19 10.04 0.5645 5.30

7.26 6.80 6.15 13.97 12.02 5.57 7.87 8.57 9.78 7.85 9.27 11.99 13.92 5.27 10.58 0.5791 4.39

5.61 6.83 4.44 10.69 10.32 4.57 9.30 15.38 13.28 6.39 8.69 13.74 11.66 5.69 10.10 R2 RMSEP

8.03 5.94 7.08 11.96 11.95 5.42 9.14 9.13 10.43 7.56 9.88 10.78 9.39 4.35 9.88 0.5166 5.62

6.49 5.91 5.25 13.60 13.48 5.45 11.28 8.18 10.46 7.51 9.17 10.31 11.15 5.36 9.18 0.4400 6.64

5.66 7.73 4.34 9.26 9.06 4.38 8.05 14.39 11.86 5.75 7.72 11.01 14.23 6.37 8.19 R2 RMSEP

– – – – – – – – – – – – – – – – –

6.58 6.79 5.44 11.58 11.97 4.86 6.24 7.96 9.20 7.02 7.57 8.87 14.33 5.97 9.20 0.4914 5.08

4.56 6.86 3.80 8.25 8.46 5.06 5.54 8.38 8.08 5.81 7.00 10.02 6.04 5.11 8.00 R2 RMSEP

5.97 5.69 5.39 8.56 8.29 5.10 6.82 6.68 7.44 6.59 7.45 7.36 5.91 4.15 7.47 0.5512 1.34

4.95 7.00 4.19 9.82 8.41 5.17 5.60 5.95 6.84 7.17 6.92 7.14 6.55 4.86 7.88 0.5478 1.38

5.18 3.94 11.24 5.04 8.67 5.11 R2 RMSEP

5.69 4.60 10.06 5.55 8.16 6.25 0.9805 0.65

4.73 4.84 10.74 4.91 8.47 4.96 0.9731 0.23

5.65 4.78 11.55 5.22 8.97 6.62 R2 RMSEP

5.97 4.82 10.37 5.82 8.46 6.55 0.9883 0.36

6.02 6.12 12.33 6.24 10.09 6.33 0.9510 0.82

5.09 4.34 11.10 4.98 8.31 5.88 R2 RMSEP

6.87 5.18 11.30 7.08 10.43 5.79 0.8604 2.14

5.35 6.55 11.85 5.40 10.39 6.05 0.8919 1.67

5.00 4.07 9.15 4.77 7.69 5.40 R2 RMSEP

– – – – – – – –

5.30 5.01 10.96 5.51 9.69 5.04 0.9429 1.49

4.70 4.33 10.01 4.24 6.45 5.59 R2 RMSEP

5.33 5.18 8.35 5.47 7.39 5.66 0.8817 1.05

4.20 6.24 9.29 4.36 6.95 6.39 0.8112 0.89

amino-(Asp)1

amino-(Met)2

amino-(Ala)2

amino-(Leu)1

amino-[Fmoc-Tyr(tBu)]

amino-(Leu)2

amino-(Leu)3

amino-(Met)1

Training set

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

l-Valine l-Proline l-Leucine l-Asparagine Glycine l-Phenylalanine creatine GABA β -alanine

4.66 6.09 3.93 7.70 8.02 5.00 5.49 10.67 9.49

5.63 5.32 4.99 8.80 8.42 4.69 6.62 6.44 7.35

5.10 5.94 4.33 9.50 8.83 5.08 6.58 5.78 6.72

4.93 6.78 3.94 8.20 9.67 5.01 7.00 13.32 12.10

6.76 5.13 5.88 9.29 10.66 4.40 7.34 7.82 9.11

5.40 6.44 4.45 11.22 10.30 5.38 7.35 6.27 7.49

5.25 7.28 4.19 7.06 7.91 4.05 7.34 11.67 10.60

– – – – – – – – –

6.61 5.49 5.74 8.25 9.99 4.31 5.80 7.50 8.29

6.46 8.90 5.05 9.37 11.80 4.90 9.39 13.54 14.46

6.43 7.70 5.85 10.66 9.19 5.17 10.58 9.16 9.23

6.21 9.28 6.08 7.86 6.84 6.20 8.13 7.14 10.49

5.62 7.60 4.37 8.02 10.19 4.22 7.88 13.06 13.29

tR pred (2P-M) 5.50 6.73 4.96 9.76 8.22 4.33 9.68 8.18 8.26 (continued on

tR pred (3P-M) 6.89 6.56 5.80 9.49 12.26 5.30 6.90 8.36 10.11 next page)

[m5G;September 24, 2019;15:56]

Column /Amino acid

ARTICLE IN PRESS

Test set l-Methionine l-Tryptophan l-Serine l-Isoleucine l-Alanine l-Tyrosine

amino-(Gly)3

M. Skoczylas, S. Bocian and B. Buszewski / Journal of Chromatography A xxx (xxxx) xxx

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

Table 5 Predictive properties of the derived QSRR models.

JID: CHROMA

Column /Amino acid Training set

amino-(Met)1 tR exp tR pred (2P-M)

tR pred (3P-M)

amino-(Met)2 tR exp tR pred (2P-M)

tR pred (3P-M)

amino-(Leu)1 tR exp tR pred (2P-M)

tR pred (3P-M)

amino-(Leu)2 tR exp tR pred (2P-M)

tR pred (3P-M)

amino-(Leu)3 tR exp tR pred (2P-M)

taurine l-Threonine l-citrulline l-carnitine betaine l-homoserine

5.27 6.90 7.87 7.04 3.68 6.73 R2 RMSEP

6.34 7.38 7.28 5.57 3.74 7.40 0.4289 2.15

6.02 6.84 6.78 6.70 4.41 8.07 0.3005 2.75

5.22 6.96 9.38 7.70 3.81 7.75 R2 RMSEP

6.32 8.00 7.73 7.09 3.86 8.00 0.4895 3.81

6.60 7.67 7.54 7.27 4.51 9.29 0.2336 6.02

4.58 6.45 8.84 11.80 5.91 6.86 R2 RMSEP

– – – – – – – –

5.72 6.43 7.10 12.29 5.57 7.33 0.5221 2.89

5.63 8.67 12.99 10.63 5.69 9.40 R2 RMSEP

5.79 9.65 9.94 10.38 9.33 10.14 0.4321 5.51

6.38 9.11 14.47 9.68 5.42 12.28 0.3758 6.69

4.99 7.51 10.62 10.01 4.74 8.11 R2 RMSEP

5.01 8.68 9.00 9.44 8.35 9.19 0.3698 5.26

5.60 8.78 7.34 10.24 3.95 8.81 0.5247 3.95

4.82 4.85 8.82 4.38 6.46 6.09 R2 RMSEP

4.93 4.78 8.51 5.08 7.29 5.30 0.8601 0.32

6.91 10.56 4.47 7.04 6.24 0.8472 1.53

5.00 5.05 9.65 4.33 7.37 6.52 R2 RMSEP

5.69 4.00 9.33 5.88 9.11 4.48 0.5811 2.25

7.72 12.76 4.62 7.88 6.90 0.8277 3.46

4.60 3.85 7.93 4.58 6.89 4.39 R2 RMSEP

– – – – – – – –

5.53 3.96 8.23 5.69 8.68 4.03 0.8679 0.92

5.78 4.75 12.27 5.72 9.48 5.57 R2 RMSEP

5.96 4.83 10.42 5.80 8.57 6.54 0.9673 0.87

13.53 10.34 9.52 6.41 6.39 10.92 0.0931 22.93

5.02 4.07 10.79 4.92 8.08 4.74 R2 RMSEP

5.06 4.02 9.49 4.91 7.58 5.60 0.9686 0.45

4.08 5.15 10.70 5.94 9.99 5.55 0.8675 1.24

TSKgel - NH2

silica-Amino

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

tR exp

tR pred (2P-M)

tR pred (3P-M)

l-Valine l-Proline l-Leucine l-Asparagine Glycine l-Phenylalanine creatine GABA β -alanine taurine l-Threonine l-citrulline l-carnitine betaine l-homoserine

5.27 7.50 4.20 7.25 7.72 4.11 7.63 12.37 10.49 4.21 6.42 8.88 13.87 6.62 6.57 R2 RMSEP

5.26 6.40 4.75 9.14 7.84 4.17 9.06 7.79 7.85 4.16 8.29 8.47 8.99 8.03 8.78 0.4131 4.63

6.66 5.61 5.79 8.26 9.90 4.36 5.64 7.51 8.20 5.68 6.27 7.24 14.42 6.11 7.34 0.5634 3.52

4.15 6.15 3.46 5.79 6.75 4.15 6.09 11.80 9.65 3.52 5.23 7.03 10.50 4.24 5.83 R2 RMSEP

– – – – – – – – – – – – – – – –

5.60 5.35 4.93 6.27 8.55 4.62 5.05 6.46 7.43 3.90 6.33 5.37 10.56 3.59 6.34 0.4838 3.20

7.02 8.35 5.37 15.14 11.91 6.33 6.80 4.52 7.01 7.52 11.78 10.93 4.77 5.49 8.62 R2 RMSEP

5.81 8.20 5.13 12.31 8.85 7.23 8.08 6.79 7.77 9.48 9.63 9.48 5.08 4.52 9.71 0.7259 2.66

6.86 8.46 5.99 11.88 8.82 7.09 10.74 6.19 7.16 8.93 9.97 7.43 5.06 4.48 8.35 0.4504 3.88

5.41 4.71 4.25 9.31 7.62 5.20 3.85 7.26 6.77 8.50 8.61 7.99 4.47 3.07 7.35 R2 RMSEP

5.24 4.93 4.52 8.98 8.68 4.19 6.35 6.20 7.32 6.08 7.28 7.01 5.08 3.26 7.30 0.6546 1.28

5.74 5.33 5.30 8.63 7.42 4.53 5.51 6.31 6.85 6.53 7.88 7.54 4.20 2.90 7.91 0.8062 0.75

4.62 3.93 7.60 4.56 6.82 4.45 R2 RMSEP

4.84 3.92 9.02 4.70 7.27 5.35 0.9594 0.52

5.58 4.05 8.15 5.73 8.66 4.02 0.8687 1.02

4.06 4.09 6.36 4.09 5.53 4.62 R2 RMSEP

– – – – – – –

3.17 4.49 7.30 5.02 7.36 4.55 0.7883 1.01

6.71 5.81 14.68 5.92 9.75 9.44 R2 RMSEP

5.23 9.50 11.27 5.40 7.58 10.43 0.5736 5.57

5.11 11.92 10.96 6.72 8.06 7.80 0.1263 10.01

5.88 4.35 9.81 4.37 6.15 6.75 R2 RMSEP

4.45 4.26 8.71 4.62 7.24 4.82 0.6859 1.37

5.09 4.28 8.63 5.37 6.77 5.53 0.7955 0.82

2P-M – two-parameter model, 3P-M – three-parameter model, R2 – the determination coefficient of tR

pred.

vs. tR

exp

relation, RMSEP – the root mean square errors of prediction.

15

[m5G;September 24, 2019;15:56]

amino-(Phe)1

Training set

Test set l-Methionine l-Tryptophan l-Serine l-Isoleucine l-Alanine l-Tyrosine

∗

amino-(Phe)2

Column /Amino acid

ARTICLE IN PRESS

Test set l-Methionine l-Tryptophan l-Serine l-Isoleucine l-Alanine l-Tyrosine

tR pred (3P-M)

M. Skoczylas, S. Bocian and B. Buszewski / Journal of Chromatography A xxx (xxxx) xxx

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

Table 5 (continued)

JID: CHROMA 16

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Fig. 6. Plots of the experimental vs. predicted retention times using two-parameter models (A - D) and three-parameter model (E – H) for the training set (A, C, E, G) and test set (B, D, F, H) derived for the amino-(Gly)1 (A, B, E, F) and amino-(Gly)3 columns (C, D, G, H).

Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514

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4. Conclusions The application of QSRR analysis allows the investigation of the amino acids retention mechanism on the amino acid- and peptidesilica stationary phases. The two- and three-parameter QSRR equations employing the structural properties of solutes were found to describe isocratic retention in respect of their chromatographic retention. The predictors included in the best statistically significant QSRR models were related to the molecular properties of the amino acids and indicated the presence of dominant ion–ion electrostatic interactions, hydrogen bonding formation, and hydrophobic/hydrophilic interactions as mechanisms of the amino acids retention on the amino acid- and peptide-silica stationary phases. The strength of those secondary interactions was shifted towards interactions with the mobile phases molecules. In general, the retention mechanism of amino acids on the amino acid- and peptide-silica stationary phases is a complex phenomenon, including also the creation of multipoint or quadrupole interactions between amphoteric analytes and the chemically bonded amino acid- and peptide-ligands. The importance of aforementioned interactions induces the occurrence of two phenomena: adsorption (characteristic for ANP) and partition (specific for HILIC) which govern the tested retention process. Despite of the structural difference in the side chains of bonded amino acids and peptides, the elution order of amino acids was predominantly similar on all the studied columns. The retention mechanism was also similar to certain extent for all the tested amino acid- and peptide-silica stationary phases. Some variation was however observed. Similarities were associated with the occurrence of the descriptors related to the formation of hydrogen bonds in all the mathematical models derived for the studied amino acid- and peptide-based columns. This implies that the formation of hydrogen bonds with polar analytes is characteristic feature of this type of chromatographic packings. The importance of ionic interactions in the form of particular descriptors was more diversified within the group of the studied stationary phases. Based on this feature the tested columns can be grouped into set of columns with stronger or weaker electrostatic interactions. The comparison of the retention mechanism occurring on the amino acid- and peptide-based columns with the processes govern retention on the laboratory-prepared amino-bonded phase has shown in general the similarity of the phenomena that took place in both systems. The retention mechanism of amino acids on the commercial TSKgel – NH2 was different with regard to this observed on the amino acid- and peptide-silica stationary phases. The only similarity between commercial amino-based column and the tested ones constitutes the importance of hydrophilic/hydrophobic interactions. The derived QSRR models revealed good prediction potency for the test set of amino acids with some exceptions described in the manuscript. Nevertheless, it can be conclude that the best predictive models may be applied to predict retention of amino acids not investigated in this study with good accuracy. Thus, they may be used to assist in the method development and the optimization of chromatographic conditions. Declaration of Competing Interest The authors have declared no conflict of interest. Acknowledgments This work was financially supported by the National Science Centre of Poland as a part of the PRELUDIUM 12 project no. ´ 2016/23/N/ST4/00369. The authors thank Dr. Sylwia Studzinska for her meaningful suggestions and discussions.

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This publication is dedicated to our faithful friend Professor Roman Kaliszan.

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Please cite this article as: M. Skoczylas, S. Bocian and B. Buszewski, Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography, Journal of Chromatography A, https://doi.org/10.1016/j. chroma.2019.460514