How to Minimize Certain Artifacts in Fourier Self-Deconvolution

Smeller, László; Goossens, K.; Heremans, Karel

doi:10.1366/0003702953965533

Cited by 47 publications

(36 citation statements)

References 10 publications

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“…The overlapping components of the amide I/IЈ band were resolved by Fourier self-deconvolution (45), which decreases the width of the component lines of the amide band. The optimal parameters were determined from the analysis of the power spectrum (46). A resolution enhancement factor (45) of 1.5 was reached by using a Lorentzian band shape of 20 cm Ϫ1 bandwidth.…”

Section: Methodsmentioning

confidence: 99%

Allosteric Effectors Influence the Tetramer Stability of Both R- and T-states of Hemoglobin A

Schay

Smeller

Tsuneshige

et al. 2006

Journal of Biological Chemistry

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The contribution of heterotropic effectors to hemoglobin allostery is still not completely understood. With the recently proposed global allostery model, this question acquires crucial significance, because it relates tertiary conformational changes to effector binding in both the R-and T-states. In this context, an important question is how far the induced conformational changes propagate from the binding site(s) of the allosteric effectors. We present a study in which we monitored the interdimeric interface when the effectors such as Cl ؊ , 2,3-diphosphoglycerate, inositol hexaphosphate, and bezafibrate were bound. We studied oxy-Hb and a hybrid form (␣FeO 2 ) 2 -(␤Zn) 2 as the T-state analogue by monitoring heme absorption and Trp intrinsic fluorescence under hydrostatic pressure. We observed a pressure-dependent change in the intrinsic fluorescence, which we attribute to a pressure-induced tetramer to dimer transition with characteristic pressures in the 70 -200-megapascal range. The transition is sensitive to the binding of allosteric effectors. We fitted the data with a simple model for the tetramer-dimer transition and determined the dissociation constants at atmospheric pressure. In the R-state, we observed a stabilizing effect by the allosteric effectors, although in the T-analogue a stronger destabilizing effect was seen. The order of efficiency was the same in both states, but with the opposite trend as inositol hexaphosphate > 2,3-diphosphoglycerate > Cl ؊ . We detected intrinsic fluorescence from bound bezafibrate that introduced uncertainty in the comparison with other effectors. The results support the global allostery model by showing that conformational changes propagate from the effector binding site to the interdimeric interfaces in both quaternary states.Hemoglobin (1) is a tetrameric protein, which plays a vital role in the transport of oxygen. It consists of two dimers of ␣ and ␤ subunits that reversibly bind and release oxygen (1). The description of this cooperative phenomenon has been most frequently derived from the Monod-Wyman-Changeux (MWC) 2 two-state allosteric model (2) that attributes cooperativity to a rapid equilibrium between two conformations of distinct oxygen affinity of the whole tetramer. These distinct states are the fully unliganded T-state and the fully ligated R-state. Szabo and Karplus (3) modified the two-state model incorporating the stereochemical mechanism suggested by Perutz (4) for the T to R switch, and introduced ligation-induced tertiary changes within the T-state. In this extended model (MWC-SK), it was proposed that cooperativity still works through a ligation-induced shift in the equilibrium of states T and R, but the model attributed importance in the conformational switch to certain changes at the inter-and intrasubunit interfaces.

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Section: Methodsmentioning

confidence: 99%

Allosteric Effectors Influence the Tetramer Stability of Both R- and T-states of Hemoglobin A

Schay

Smeller

Tsuneshige

et al. 2006

Journal of Biological Chemistry

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“…To this end, a program was developed in which the Fourier self-deconvolution is done by visual inspection of the power spectrum (Smeller et al, 1995b). A resolution enhancement factor of 1.6 was obtained with a Lorentzian line with 19 cm-' full width at half height.…”

Section: Methodsmentioning

confidence: 99%

Pressure‐Tuning the Conformation of Bovine Pancreatic Trypsin Inhibitor Studied by Fourier‐Transform Infrared Spectroscopy

Goossens

Smeller

Frank

et al. 1996

European Journal of Biochemistry

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A hydrostatic pressure of 1 .5 GPa induces changes in the secondary structure of bovine pancreatic tryspin inhibitor (BPTI) as revealed by the analysis of the amide I' band with Fourier-transform infrared (FTIR) spectroscopy in the diamond anvil cell. The features of the secondary structure remain distinct at high pressure suggesting that the protein does not unfold. The fitted percentages of the secondary structure elements during compression and decompression strongly suggest that the pressure-induced changes are reversible. The pressure-induced changes in the tyrosine side chain band are also reversible. The results demonstrate that the infrared technique explores different aspects of the behaviour of proteins in comparison with two published molecular dynamics studies performed up to 1

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“…Furthermore, FSD filter functions determine the shape of deconvolved bands and the SNR degradation in the FSD spectra [38]. Inadequate FSD filter parameters may result in under-or over-deconvolution, with the latter one characterized by noise amplification and the appearance of large negative side-lobes (see [31,38,39] for details].…”

Section: Spectral Filtering (Smoothing/derivatives Fourier-self Decomentioning

confidence: 99%

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

291

203

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IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient data and has generated an urgent need for new and automated methods of spectral data analysis. Spectral data pre-processing is an important first step in the workflow of IR and Raman spectra analysis which involves specific processing procedures performed on the raw data. Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy. The main goals of data pre-processing can be summarized as follows: (i) Improvement of the robustness and accuracy of subsequent quantitative or classification analyses (ii) Improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines (iii) Detection and removal of outliers and trends (iv) Reduction of the dimensionality of the data mining task. Removal of irrelevant and redundant information by feature selection. For systematic reasons it is useful to subdivide data pre-processing procedures into at least eight different categories. These groups can be used to perform the following operations: 1. Exclusion (cleaning) -this class of data pre-processing methods is used to detect and eliminate spectral outliers. Tests for spectral quality are important examples and aim at the detection of outlier spectra prior to further data mining tasks. Removal of outlier spectra can be achieved by labeling them as NaNs (Not a Number), for example as "bad" pixel spectra in hyperspectral imaging applications. Another way of outlier removal in hyperspectral imaging applications is the interpolation of bad pixel spectra by using spectral data from neighboring locations. 2. Normalization -normalization is used to scale the spectra within a similar range. In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3. Filtering -in frequency analyses filtering is known as a group of methods that removes unwanted frequencies from the signals to be analyzed. Examples for...

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How to Minimize Certain Artifacts in Fourier Self-Deconvolution

Cited by 47 publications

References 10 publications

Allosteric Effectors Influence the Tetramer Stability of Both R- and T-states of Hemoglobin A

Allosteric Effectors Influence the Tetramer Stability of Both R- and T-states of Hemoglobin A

Pressure‐Tuning the Conformation of Bovine Pancreatic Trypsin Inhibitor Studied by Fourier‐Transform Infrared Spectroscopy

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

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