Characterization of Cu2+-binding modes in the prion protein by visible circular dichroism and multivariate curve resolution.

Visible circular dichroism (CD) spectra from the copper(II) titration of the metal-binding region of the prion protein, residues 57-98, were analyzed using the self-modeling curve resolution method multivariate curve resolution-alternating least squares (MCR-ALS). MCR-ALS is a set of mathematical tools for estimating pure component spectra and composition profiles from mixture spectra. Model-free solutions (e.g., soft models) are produced under the assumption that pure component profiles should be nonnegative and unimodal. Optionally, equality constraints can be used when the concentration or spectrum of one or more species is known. MCR-ALS is well suited to complex biochemical systems such as the prion protein which binds multiple copper ions and thus gives rise to titration data consisting of several pure component spectra with overlapped or superimposed absorption bands. Our study reveals the number of binding modes used in the uptake of Cu(2+) by the full metal-binding region of the prion protein and their relative concentration profiles throughout the titration. The presence of a non-CD active binding mode can also be inferred. We show that MCR-ALS analysis can be initialized using empirically generated or mathematically generated pure component spectra. The use of small model peptides allows us to correlate specific Cu(2+)-binding structures to the pure component spectra.

Identification of single bacterial cells in aqueous solution using confocal laser tweezers Raman spectroscopy.

We report on a rapid method for reagentless identification and discrimination of single bacterial cells in aqueous solutions using a combination of laser tweezers and confocal Raman spectroscopy (LTRS). The optical trapping enables capturing of individual bacteria in aqueous solution in the focus of the laser beam and levitating the captured cell well off the cover plate, thus maximizing the excitation and collection of Raman scattering from the cell and minimizing the unwanted background from the cover plate and environment. Raman spectral patterns excited by a near-infrared laser beam provide intrinsic molecular information for reagentless analysis of the optically isolated bacterium. In our experiments, six species of bacteria were used to demonstrate the capability of the confocal LTRS in the identification and discrimination between the diverse bacterial species at various growth conditions. We show that synchronized bacterial cells can be well-discriminated among the six species using principal component analyses (PCA). Unsynchronized bacterial cells that are cultured at stationary phases can also be well-discriminated by the PCA, as well as by a hierarchical cluster analysis (HCA) of their Raman spectra. We also show that unsynchronized bacteria selected from random growth phases can be classified with the help of a generalized discriminant analysis (GDA). These findings demonstrate that the LTRS may find valuable applications in rapid sensing of microbial cells in diverse aqueous media.

Quantitative analysis of low-field NMR signals in the time domain.

Two novel methods are described for direct quantitative analysis of NMR free induction decay (FID) signals. The methods use adaptations of the generalized rank annihilation method (GRAM) and the direct exponential curve resolution algorithm (DECRA). With FID-GRAM, the Hankel matrix of the sample signal is compared with that of a reference mixture to obtain quantitative data about the components. With FID-DECRA, a single-sample FID matrix is split into two matrices, allowing quantitative recovery of decay constants and the individual signals in the FID. Inaccurate results were obtained with FID-GRAM when there were differences between the frequency or transverse relaxation time of signals for the reference and test samples. This problem does not arise with FID-DECRA, because comparison with a reference signal is unnecessary. Application of FID-DECRA to 19F NMR data, which contained overlapping signals from three components, gave concentrations comparable to those derived from partial least squares (PLS) analysis of the Fourier transformed spectra. However, the main advantage of FID-DECRA was that accurate (<5% error) and precise (2.3% RSD) results were obtained using only one calibration sample, whereas with PLS, a training set of 10 standard mixtures was used to give comparable accuracy and precision.

Computation of the range of feasible solutions in self-modeling curve resolution algorithms.

Self-modeling curve resolution (SMCR) describes a set of mathematical tools for estimating pure-component spectra and composition profiles from mixture spectra. The source of mixture spectra may be overlapped chromatography peaks, composition profiles from equilibrium studies, kinetic profiles from chemical reactions and batch industrial processes, depth profiles of treated surfaces, and many other types of materials and processes. Mathematical solutions are produced under the assumption that pure-component profiles and spectra should be nonnegative and composition profiles should be unimodal. In many cases, SMCR may be the only method available for resolving the composition profiles and pure-component spectra from these measurements. Under ideal circumstances, the SMCR results are accurate quantitative estimates of the true underlying profiles. Although SMCR tools are finding wider use, it is not widely known or appreciated that, in most circumstances, SMCR techniques produce a family of solutions that obey nonnegativity constraints. In this paper, we present a new method for computation of the range of feasible solutions and use it to study the effect of chromatographic resolution, peak height, spectral dissimilarity, and signal-to-noise ratios on the magnitude of feasible solutions. An illustration of its use in resolving composition profiles from a batch reaction is also given.

Appearance of discontinuities in spectra transformed by the piecewise direct instrument standardization procedure.

Several years ago, we noted that spectra transformed by the piecewise direct standardization (PDS) method may contain discontinuities. Having noticed that the problem was a recurring one, we studied it and recently diagnosed its source. Our investigations suggest that this problem also occurs in applications of window factor analysis, evolving factor analysis, and any other procedure that uses piecewise principal component models. In this work, we report the source of the problem and illustrate it with one example. A procedure is presented for eliminating the problem that is effective in PDS pattern recognition applications. Further work is needed to develop modified algorithms suitable for calibration applications.

Identification and quantitation of drugs of abuse in urine using the generalized rank annihilation method of curve resolution.

A rapid analytical method which is of practical use for the identification and quantitation of drugs of abuse in urine using HPLC with a diode-array detection is described. Because the method utilizes mathematical resolution of partially resolved peaks, greatly simplified sample preparation procedures and very short run times can be used. The generalized rank annihilation method (GRAM) is used to eliminate response due to unknown background peaks and separate partially resolved peaks. An optimized gradient elution program was found for which morphine, phenylpropanolamine, ephedrine, benzoylecgonine, lidocaine, cocaine, diphenhydramine, nortriptyline, norpropoxyphene, nordiazepam, codeine, D-amphetamine, meperidine, and amitriptyline elute from the HPLC column in less than 8.5 min. A commercially available system for HPLC analysis of drugs of abuse is currently available, however, the commercially available system takes 21 min to complete its analysis. Two modified sample pre-treatment methods were also developed to simplify sample treatment procedures substantially. In this paper, The GRAM technique is shown to be extremely powerful in identifying drugs of abuse from large overlapping peaks.

Generalized rank annihilation method using similarity transformations.

Recently several papers have described the generalized rank annihilation method; however, in some cases complex eigenvalues and eigenvectors may appear when the generalized eigenproblem is solved. When complex eigenvalues and eigenvectors are encountered, the results cannot be used to estimate pure component profiles (e.g. spectra or chromatograms). In this paper, a similarity transformation is used to transform complex eigenvalues and eigenvectors into real eigenvalues and eigenvectors, thereby permitting spectra and profiles of pure constituents to be estimated. The modified GRAM method is illustrated with simulated and real data.