Evaluation of peptide electropherograms by multivariate mathematical-statistical methods I. Principal component analysis

Depository effects in slowly metabolised proteins, typically glycation or t he estimation of products arising from the reaction of unsaturated long-cha in-fatty acid metabolites (possessing aldehydic groups) are very difficult to assess owing to their extremely low concentration in the protein matrix. In order to reveal such alterations we applied deep enzymatic fragmentatio n resulting in a set of small peptides, which, if modified, are likely to c hange their electrophoretic properties and can be visualised on the resulti ng profile. Peptide maps of collagen (a mixture of collagen types I and III digested by bacterial collagenase) were applied as the model protein struc ture for detecting the nonenzymatic posttranslational changes originating d uring various physiological conditions like high fructose diet and hypertri glyceridemic state. Capillary electrophoresis in acidic media (sodium phosp hate buffer, pH 2.5) was used as the separation method capable of (partial) separation of over 60 peptide peaks. Two to 13 changes were revealed in th e profiles obtained reflecting the physiological conditions of the animals tested. Combination of peptide profiling with subsequent t-test evaluation of individual peak areas and principal component analysis based on cumulati ve peak areas of individual sections of the electropherograms allowed to de termine in which section (part) of the electropherogram the physiological s tate indicating changes occurred. Simultaneously it was possible to reveal the qualitative differences between the four physiological regimes investig ated (i.e., which regime affects the collagen molecules most and which affe cts them least). The approach can be used as guidance for targeted presepar ation of the very complex peptide mixture. (C) 2001 Elsevier Science B.V. A ll rights reserved.