Liquid phase separations (eg. liquid chromatography and electrophoretic separations) have long been used as investigative tools by scientists and researchers seeking to identify the structure of molecules, particularly peptides (as used herein the term “peptides” refers to polymers having more than one amino acid, and includes, without limitation, dipeptides, tripeptides, oligopeptides, and polypeptides. The term “protein” refers to molecules containing one or more polypeptide chains).
Proteomics involves the broad and systematic analysis of proteins, which includes their identification, quantification, and ultimately the attribution of one or more biological functions. Proteomic analyses are challenging due to the high complexity and dynamic range of protein abundances. The industrialisation of biology requires that the systematic analysis of expressed proteins be conducted in a high-throughput manner and with high sensitivity, further increasing the challenge. Recent technological advances in instrumentation, bio-informatics and automation have contributed to progress towards this goal. Specifically, in the area of proteomic identification, it is evident that greater specificity benefits the ability to deal with the high complexity of proteomes. As a result, recent efforts have focused on improvements in separation speed, resolving power and dynamic range, and these methods have generally been based on the combination of separations with mass spectrometry (MS), using correlation of tandem mass spectra with established protein databases or predictions from genome sequence data for identifications.
Additionally, modern proteomics research has increasingly taken advantage of the ability of liquid chromatography to identify proteins from their elution time from a chromatographic column. The information gleaned from a liquid chromatograph can be enhanced by identifying the molecule's mass, or mass to charge, by coupling the liquid chromatograph either on line or off line, with a mass spectrometer. Common methods include offline tryptic digestion and subsequent electrophoretic or chromatographic separation with matrix-assisted laser desorption/ionization or electrospray time-of-flight or ion trap mass spectrometry. Capillary electrophoresis, mass spectrometry or liquid chromatography/mass spectrometry coupled online via electrospray interfaces have also been used to analyze tryptic and other digests of complex biological samples such as whole cell lysates and human body fluids. The dynamic range of the mass spectrometer in these methods may be limited when a sample is directly infused by ion suppression in the electrospray and the detector. Further, the dynamic range of Fourier transform ion cyclotron resonance (FTICR) and ion trap mass spectrometers can be limited by the storage capacity within the instrument, although it has been shown that the use of a mass selective quadrupole to selectively load the FTICR cell.
Researchers attempting to enhance the accuracy of these methods have devised a number of schemes to increase their accuracy. For example, in the paper “Prediction of Chromatographic Retention and Protein Identification in Liquid Chromatography/Mass Spectrometry” Magnus Palmblad, Margareta Ramstrom, Karin E. Markides, Per Hakansson, and Jonas Bergquist, Analytic Chemistry p. 4–9, 2002, the authors describe a method for using the information from liquid separation schemes such as chromatography and electrophoretic methods, to improve peptide mass fingerprinting based on accurate mass measurement. The author's concede that the resolving power and accuracy in chromatographic separations are several orders of magnitude lower than in mass spectrometry, but they contend that the information is complementary in nature and available at negligible computational cost and at no additional experimental cost. Briefly, the method described in the Palmblad paper assigns “retention coefficients” for the 20 amino acids, as well as the number of each amino acid, a term that compensates for void volumes and a delay between sample injection and acquisition of mass spectra. The parameters are then fitted by the least squares method to experimental data from ˜70 BSA peptides of ˜100 HAS and transferrin peptides putatively identified by accurate mass measurement and high relative intensities in the mass spectra. The authors found that “the accuracy of the predictor was found to be 8–10% when “trained” by each of the six BSA and CSF data sets.” While approaches such as that described in the Palmblad paper provide some useful information, their utility is limited by the accuracy of the predictions.
Thus, at the present, there are two major approaches for proteomic analyses. The first one consists of the off-line combination of two-dimensional polyacrylamide electrophoresis (2D-PAGE) with MS. The proteins are first separated in a gel by their pI and mass and then the protein “spots” are enzymatically hydrolysed resulting in peptide mixtures which are analysed by matrix assisted laser desorption ionisation-time of flight (MALDI-TOF) or electrospray (ESI)-MS. Another rapid evolving approach consists of a global proteome-wide enzymatic digestion followed by analysis using on-line 1-D or 2-D liquid chromatography (LC) coupled with ESI-MS. The detection of the peptides is achieved by tandem MS (7,13) or more recently by single stage Fourier transform ion cyclotron resonance (FTICR)-MS, which provides high sensitivity, large dynamic range and high throughput in routine applications by circumventing the need for tandem MS.
An aspect of proteomic analysis that has not yet been exploited involves use of the information available from the separations (eg. LC elution time). Indeed, retention time in LC is unique and structurally dependent for a defined experiment (mobile phase composition, stationary phase etc.). If there is a way to predict the LC retention time for a given peptide structure, then this could be used in conjunction with either MS/MS data to improve the confidence of peptide identifications and/or increase the number of peptide identifications, or, with sufficiently high accuracy MS, to reduce the need for MS/MS data (i.e. if the prediction is reliable enough).
The idea that chromatographic behaviour of peptides could be predicted based on the amino acid composition is not new. In 1951, Knight and Pardee showed that synthetic peptides retention factor (Rf) values on paper chromatography could be predicted with some accuracy. In 1952, Sanger introduced the problem of isomers by demonstrating that the relationship between Rf and composition was not absolutely accurate since peptides containing the same amino acids but having difference sequences could frequently be separated. More recently, there have been several reports on the prediction of peptide elution times in reversed-phase (RP) or normal phase liquid chromatography. These methods used quantitative structure-chromatographic retention relationships (QSRR's) (e.g. partial least square or multiple linear regression) for the peptide elution time prediction. Casal et al. demonstrated that partial least squares regression provides a better predictive ability with these models using a mixture of 25 small standard peptides. One limitation of these models is that they are most effective for peptides with less than 15–20 amino acid residues.
Another approach, based on artificial neural networks (ANNs), has demonstrated better predictive capabilities in several areas of chemistry including: (i) conformational states for small peptides (31), (ii) carbon-13 nuclear magnetic resonance chemical shifts and (iii) the retardation factor or retention time of small molecules in thin layer chromatography, GC and LC. While these advances are significant, until the present invention, those having skill in the art have not yet used ANNs for peptide elution time prediction.
Accordingly, there remains a need for improved methods for predicting the identity of peptides and proteins.