Protein secondary structure from fourier transform infrared spectroscopy: A data base analysis

Sarver, Ronald W.; Krueger, William C.

doi:10.1016/0003-2697(91)90155-m

Cited by 151 publications

(105 citation statements)

References 23 publications

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“…Em primeiro lugar, é interessante salientar a extrema semelhança com espectros de queijo Cheddar, apresentados por Chen et al 27 Na região média (MID), observam-se bandas de absorção entre 3760 e 1520 cm -1 , principalmente relacionadas com presença de água (vibração de grupos hidroxila por deformação axial simétrica e assimétrica entre 3600 e 3000 cm -1 ), gordura (vibrações de estiramentos de grupos C-H, associadas aos grupos metil e metileno em 2930 e 2850 e sinal característico de estiramentos de grupos carbonila entre 1730 e 1765 cm ). [28][29][30][31] Na região próxima (NIR) observam-se bandas de absorção entre 7500 a 4000 cm -1 , associadas a sobretons e combinação de ligações de grupos C-H, N-H e O-H.…”

Section: Caracterização Espectroscópica De Queijo Pratounclassified

Caracterização físico-química de queijo prato por espectroscopia no infravermelho e regressão de mínimos quadrados parciais

Sauer¹,

Okada²,

Peralta‐Zamora³

2008

Quím. Nova

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CaraCterização físiCo-químiCa de queijo prato por espeCtrosCopia no infravermelho e regressão de mínimos quadrados parCiais elenise sauer-leal e fernanda martins okada Departamento de Alimentos, Universidade Tecnológica Federal do Paraná, Campus Ponta Grossa, Av. Monteiro Lobato, s/n -km 04, 84016-210 Ponta Grossa -PR, Brasil patricio peralta-zamora* Departamento de Química, Universidade Federal do Paraná, CP 19081, 81531-990 Curitiba -PR, Brasil Recebido em 11/6/07; aceito em 24/4/08; publicado na web em 22/9/08 PHYSICO-CHEMICAL CHARACTERIZATION OF PRATO CHEESE BY INFRARED SPECTROSCOPY AND PARTIAL LEAST SQUARES REGRESSION. In this work an analytical methodology for the determination of relevant physicochemical parameters of prato cheese is reported, using infrared spectroscopy (DRIFT) and partial least squares regression (PLS). Several multivariate models were developed, using different spectral regions and preprocessing routines. In general, good precision and accuracy was observed for all studied parameters (fat, protein, moisture, total solids, ashes and pH) with standard deviations comparable with those provided by the conventional methodologies. The implantation of this multivariate routine involves significant analytical advantages, including reduction of cost and time of analysis, minimization of human errors, and elimination of chemical residues.Keywords: prato cheese; DRIFTS; multivariate calibration. introduçãoPara priorizar o fornecimento de produtos alimentícios seguros, uniformes e com qualidade, o sistema produtivo precisa manter um rigoroso controle de qualidade, atendendo critérios de órgãos fiscalizadores e demandas de mercado.1 Em função desta necessidade, a disponibilização de métodos analíticos eficientes e rápidos torna-se prioritária, principalmente em casos em que as metodologias tradicionais se mostram demoradas, dispendiosas, inseguras e incompatíveis com as novas diretrizes ambientais. 2Dentro do contexto dos produtos lácteos, controles que visam o cumprimento de padrões relacionados com identidade e qualidade são previstos pelo Ministério da Agricultura, 3 sendo as normas de rotulagem previstas pela Agência Nacional de Vigilância Sanitária. 4 No caso particular do queijo prato, a caracterização físico-química envolve determinações de extrato seco, umidade, proteína, gordura, cinzas e pH, parâmetros determinados por metodologias oficiais, reconhecidamente demoradas e geradoras de resíduos.Nos últimos anos, metodologias que utilizam técnicas instrumentais e ferramentas de calibração multivariada têm sido abundantemente descritas na literatura, com o objetivo de reduzir o tempo de análise e o gasto de reagentes, mantendo a eficiência e a confiabilidade das metodologias convencionais.Dentro deste contexto, a espectroscopia no infravermelho aparece como ferramenta de primeira importância, permitindo o estabelecimento de inúmeras metodologias analíticas orientadas ao controle de qualidade de produtos alimentícios.5-8 Além de versátil, permitindo a análise de uma grande diversidade de ...

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Section: Caracterização Espectroscópica De Queijo Pratounclassified

Caracterização físico-química de queijo prato por espectroscopia no infravermelho e regressão de mínimos quadrados parciais

Sauer¹,

Okada²,

Peralta‐Zamora³

2008

Quím. Nova

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“…Most approaches for the interpretation of ECD now use band shape or pattern recognition analyses to derive structural correlations. A number of protein structural studies have utilized vibrational spectroscopic techniques, mostly Fourier transform (FT) IR but also Raman, which have a resolution and signal-to-noise ratio (S/N) advantage (for FTIR) over ECD (Byler & S u i , 1986;Mantsch et al, 1986;Williams, 1986;Berjot et al, 1987;Bussian & Sander, 1989;Dousseau & Pezolet , 1990;Lee et al, 1990;Venyaminov & Kalnin, 1990;Sarver & Krueger, 1991a, 1991bPribic et al, 1993). The analyses of these latter spectroscopies in terms of structure has primarily depended on assignment of transition frequencies that we have earlier shown to be inherently ambiguous and potentially misleading if used in isolation without reference to data from other techniques (Dukor et al, 1992).…”

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confidence: 99%

“…The analyses of these latter spectroscopies in terms of structure has primarily depended on assignment of transition frequencies that we have earlier shown to be inherently ambiguous and potentially misleading if used in isolation without reference to data from other techniques (Dukor et al, 1992). Some more recent studies have utilized band-shape methods for FTIR spectra (Dousseau & Pezolet, 1990;Leeet al, 1990;Sarver & Krueger, 1991a, 1991bPribic et al, 1993). Somewhat between these two approaches is the use of vibrational C D (VCD), a hybrid of CD and IR, to predict protein conformation (Keiderling, 1993(Keiderling, , 1994.…”

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confidence: 99%

Comparison of and limits of accuracy for statistical analyses of vibrational and electronic circular dichroism spectra in terms of correlations to and predictions of protein secondary structure

et al. 1995

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This work provides a systematic comparison of vibrational CD (VCD) and electronic CD (ECD) methods for spectral prediction of secondary structure. The VCD and ECD data are simplified to a small set of spectral parameters using the principal component method of factor analysis (PC/FA). Regression fits of these parameters are made to the X-ray-determined fractional components (FC) of secondary structure. Predictive capability is determined by computing structures for proteins sequentially left out of the regression. All possible combinations of PC/FA spectral parameters (coefficients) were used to form a full set of restricted multiple regressions with the FC values, both independently for each spectral data set as well as for the two VCD sets and all the data grouped together. The complete search over all possible combinations of spectral parameters for different types of spectral data is a new feature of this study, and the focus on prediction is the strength of this approach. The PC/FA method was found to be stable in detail to expansion of the training set. Coupling amide I1 to amide I' parameters reduced the standard deviations of the VCD regression relationships, and combining VCD and ECD data led to the best fits. Prediction results had a minimum error when dependent on relatively few spectral coefficients. Such a limited dependence on spectral variation is the key finding of this work, which has ramifications for previous studies as well as suggests future directions for spectral analysis of structure. The best ECD prediction for helix and sheet uses only one parameter, the coefficient of the first subspectrum. With VCD, the best predictions sample coefficients of both the amide I' and I1 bands, but error is optimized using only a few coefficients. In this respect, ECD is more accurate than VCD for a-helix, and the combined VCD (amide I'+II) predicts the P-sheet component better than does ECD. Combining VCD and ECD data sets yields exceptionally good predictions by utilizing the strengths of each. However, the residual error, its distribution, and, most importantly, the lack of dependence of the method on many of the significant components derived from the spectra leads to the conclusion that the heterogeneity of protein structure is a fundamental limitation to the use of such spectral analysis methods. The underutilization of these data for prediction of secondary structure suggests spectral data could predict a more detailed descriptor.

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“…In addition, the intensity of the signals corresponding to amide III at 1238 cm -1 were markedly weakened. In the amide I band, several signals corresponding to the secondary structure of proteins overlapped with each other; the approximate composition ratios of α helices, β sheets, and random coils can be estimated by performing a linear analysis of the signals 30,31) . In the case of collagen, the amide I band originates from the C=O stretching mode in the Gly-X-Y sequence (X and Y represent proline and hydroxyproline, respectively 32,33) ), and it is formed from three main components, namely [1] the carbonyl group of proline located on X, [2] its interactions with the amine group of Gly in other repeated units, and its [3] interactions with water (~1690 cm -1 , 1630 cm -1 ).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Demineralized Mammalian Bone Tissue by Fourier Transform Infrared Microscopy

Yamaguchi

Yamamoto

Tsujigiwa

2018

J. Hard Tissue Biology.

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The amounts and properties of cartilage matrix components such as collagen and proteoglycans change in joint and bone related diseases, and spectroscopic studies of these components are important for developing new diagnostic and therapeutic methods. Infrared spectroscopy provides information on chromophores and vibrations related to characteristic molecules present in cells and tissues. In addition, infrared spectroscopy is a non-invasive and useful spectroscopy based analytical technique that does not use antibody labeling. In this study, we used decalcifi ed unstained specimens of canine mandibular bone and rat and rabbit tibia. The specimens were subjected to microscopic infrared spectroscopy, and analyzed by determining the infrared spectrum, with a focus on fi ber proteins (collagen) and carbohydrates (proteoglycans) present in the specimens. Normally, a large signal originating from the PO stretching mode of phosphate (PO 4 3-) is observed during measurements of the infrared absorption spectrum of bone sections; however, the above mentioned signal was not observed in our study as the samples used in our study were decalcifi ed. Instead, we observed signals from proteoglycans that are usually masked by signals originating from PO stretching and are diffi cult to observe. Measurements were performed on the same types of bone sections, and the results showed that the infrared spectrum varied greatly depending on the portion subjected to the measurements. However, in contrast, the results of the measurements conducted on the cartilage regions of rat and rabbit tibia coincided in terms of the amide I, amide II, and amide III bands, as well as the signal intensity ratio of the proteoglycan signal. Our fi ndings indicated that infrared spectrum patterns allowed the identifi cation of cartilaginous tissues in bone sections. By using this characteristic signal as a marker and carrying out two dimensional mapping on the cartilage part, for example, it is suggested that it can be applied to analysis of collagen distribution in demineralized fossil samples.

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Protein secondary structure from fourier transform infrared spectroscopy: A data base analysis

Cited by 151 publications

References 23 publications

Caracterização físico-química de queijo prato por espectroscopia no infravermelho e regressão de mínimos quadrados parciais

Caracterização físico-química de queijo prato por espectroscopia no infravermelho e regressão de mínimos quadrados parciais

Comparison of and limits of accuracy for statistical analyses of vibrational and electronic circular dichroism spectra in terms of correlations to and predictions of protein secondary structure

Analysis of Demineralized Mammalian Bone Tissue by Fourier Transform Infrared Microscopy

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