scholarly journals Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins

2020 ◽  
Vol 10 (17) ◽  
pp. 5918
Author(s):  
Azin Sadat ◽  
Iris J. Joye
Keyword(s):  
Secondary Structure ◽  
Raman Spectra ◽  
Protein Secondary Structure ◽  
Structural Data ◽  
Accurate Method ◽  
Globular Proteins ◽  
Raman Spectroscopic ◽  
Peak Separation ◽  
Secondary Structure Of Proteins ◽  
Ft Raman

FTIR and Raman spectroscopy are often used to investigate the secondary structure of proteins. Focus is then often laid on the different features that can be distinguished in the Amide I band (1600–1700 cm−1) and, to a lesser extent, the Amide II band (1510–1580 cm−1), signature regions for C=O stretching/N-H bending, and N-H bending/C-N stretching vibrations, respectively. Proper investigation of all hidden and overlapping features/peaks is a necessary step to achieve reliable analysis of FTIR and FT-Raman spectra of proteins. This paper discusses a method to identify, separate, and quantify the hidden peaks in the amide I band region of infrared and Raman spectra of four globular proteins in aqueous solution as well as hydrated zein and gluten proteins. The globular proteins studied, which differ widely in terms of their secondary structures, include immunoglobulin G, concanavalin A, lysozyme, and trypsin. Peak finding was done by analysis of the second derivative of the original spectra. Peak separation and quantification was achieved by curve fitting using the Voigt function. Structural data derived from the FT-Raman and FTIR analyses were compared to literature reports on protein structure. This manuscript proposes an accurate method to analyze protein secondary structure based on the amide I band in vibrational spectra.

Applications of a New 206.5-nm Continuous-Wave Laser Source: UV Raman Determination of Protein Secondary Structure and CVD Diamond Material Properties

1996 ◽  
Vol 50 (11) ◽  
pp. 1459-1468 ◽  
Author(s):  
Janet S. W. Holtz ◽  
Richard W. Bormett ◽  
Zhenhuan Chi ◽  
Namjun Cho ◽  
X. G. Chen ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Raman Spectra ◽  
Continuous Wave ◽  
Broad Band ◽  
Protein Secondary Structure ◽  
Cvd Diamond ◽  
Spectroscopic Studies ◽  
Laser Source ◽  
High Signal ◽  
Signal To Noise

We demonstrate the utility of a new 206.5-nm continuous-wave UV laser excitation source for spectroscopic studies of proteins and CVD diamond. Excitation at 206.5 nm is obtained by intracavity frequency doubling the 413-nm line of a krypton-ion laser. We use this excitation to excite resonance Raman spectra within the π → π amide transition of the protein peptide backbone. The 206.5-nm excitation resonance enhances the protein amide vibrational modes. We use these high signal-to-noise spectral data to determine protein secondary structure. We also demonstrate the utility of this source to excite CVD and gem-quality diamond within its electronic bandgap. The diamond Raman spectra have very high signal-to-noise ratios and show no interfering broad-band luminescence. Excitation within the diamond bandgap also gives rise to narrow photoluminescence peaks from diamond defects. These features have previously been observed only by cathodoluminescence measurements. This new continuous-wave UV source is superior to the previous pulsed sources, because it avoids nonlinear optical phenomena and thermal sample damage; Photoluminescence.

scholarly journals Molecular characterization of high-molecular weight glutenin subunits 1Bx6.1 and 1By22.1 from Triticum spelta К1731 accession

2019 ◽  
Vol 25 ◽  
pp. 147-153
Author(s):  
O. A. Orlovskaya ◽  
K. K. Yatsevich ◽  
S. I. Vakula ◽  
L. V. Khotyleva ◽  
A. V. Kilchevsky
Keyword(s):  
Secondary Structure ◽  
Molecular Analysis ◽  
Protein Secondary Structure ◽  
Pcr Analysis ◽  
Glutenin Subunits ◽  
Bread Making ◽  
Triticum Spelta ◽  
On Line ◽  
Secondary Structure Of Proteins ◽  
Sds Electrophoresis

Aim. Some spelt varieties, along with alleles of gliadins and high-molecular glutenin subunits (HMW-GS), identical to common wheat, contain specific alleles, that are source of Triticum aestivum gene pool enrichment. The aim of this work is the identification, molecular analysis of HMW – GS from T. spelta K1731 and evaluation of their effect on the elastic properties of gluten. Methods. Identification of HMW-GS was carried out by SDS-electrophoresis and PCR analysis. Nucleotide gene sequences were determined by Sanger sequencing. The secondary structure of proteins was predicted on the on-line CFSSP server. Results. Subunits 6.1 + 22.1 of the Glu-B1 locus encoded by the Glu-B1be allele were detected in the T. spelta K1731. The nucleotide sequences of the 1Bx6.1, 1By22.1 genes from spelt were determined, the amino acid sequence and the protein secondary structure of 6.1 + 22.1 subunits were analyzed. Conclusions. Molecular analysis of HMW-GS 1Bх6.1 and 1By22.1 from T. spelta К1731 established a low contribution to the bread-making quality of these subunits. Keywords: Triticum spelta K1731, HMW-GS, SDS electrophoresis, sequencing, secondary protein structure, gluten quality.

scholarly journals ATP-induced asymmetric pre-protein folding: a driver of protein translocation?

2017 ◽  
Author(s):  
Robin A. Corey ◽  
William J. Allen ◽  
Ian Collinson
Keyword(s):  
Secondary Structure ◽  
Structure Formation ◽  
Protein Transport ◽  
Protein Translocation ◽  
Protein Secondary Structure ◽  
Structural Data ◽  
Underlying Mechanism ◽  
Dynamics Simulation ◽  
Secondary Structure Formation ◽  
Atp Binding And Hydrolysis

AbstractThe transport of proteins across membranes is a fundamental and essential process, achieved in every cell by the ‘Sec’ translocon. In prokaryotes, SecYEG associates with the motor protein SecA to carry out ATP-driven pre-protein secretion – a vital step in the biogenesis of most periplasmic, outer membrane and secreted proteins. Structural data of the SecA-SecYEG complex has provided considerable insight into underlying mechanism of this process. Previously, we have proposed a Brownian ratchet model for protein translocation, whereby the free energy of ATP binding and hydrolysis favours the progression of pre-protein across the membrane from the cytosol toward the outside [Allen, Corey et al. eLife 2016]. Here, we use atomistic molecular dynamics simulation of a SecA-SecYEG complex engaged with preprotein to further address the mechanism underlying this process. The data describe pre-protein secondary structure formation within the channel, which exhibits a nucleotide-dependent asymmetry between the cytoplasmic and exterior cavities. The results suggest ATP-dependent pre-protein transport is partly driven by pre-protein secondary structure formation. The model previously described, and refined here, could easily be adapted for the transport of proteins across various other membranes, such as the endoplasmic reticular and mitochondrial inner membranes.

“Soft”-cuticle protein secondary structure as revealed by FT-Raman, ATR FT-IR and CD spectroscopy

2001 ◽  
Vol 31 (9) ◽  
pp. 877-885 ◽  
Author(s):  
Vassiliki A Iconomidou ◽  
Georgios D Chryssikos ◽  
Vassilis Gionis ◽  
Judith H Willis ◽  
Stavros J Hamodrakas
Keyword(s):  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Cd Spectroscopy ◽  
Ft Ir ◽  
Cuticle Protein ◽  
Ft Raman

scholarly journals Effect of High Blood Cortisol Concentration on the Spectroscopically Determined Secondary Structure of Proteins and Lipid Balance on the Example of Elite Women Volleyball Players

2021 ◽  
Author(s):  
Joanna Depciuch ◽  
Wojciech Czarny ◽  
Wojciech Szuszkiewicz ◽  
Adam Reich ◽  
Bartosz Klebowski ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Raman Spectra ◽  
Pearson Correlation ◽  
Cortisol Concentration ◽  
Transmission Spectra ◽  
Volleyball Players ◽  
Pearson Correlation Test ◽  
Α Helix ◽  
Secondary Structure Of Proteins ◽  
Structure Of Proteins

Abstract Cortisol is a stress hormone plays a crucial role in the balance between phospholipids and lipids level. In consequence, it affects the secondary structure of proteins. Currently cortisol concentration in plasma is determined by biochemical analysis. A new, optical method to estimate stress level is proposed in this work. Infrared and Raman spectroscopies were used to determine quantitative and qualitative changes in the lipids and proteins fraction in function of cortisol concentration in 49 samples of plasma collected from volleyball players at various stages of preparation for the competition. With the cortisol level increase, a decrease of structures related to PO2- phospholipids groups and amides III, II and I bonds was noticed in the transmission spectra. Changes in the secondary structure of protein were indicated as a frequency shift of α-helix and β-sheet vibrations observed in Raman spectra and second derivative of transmission spectra. Pearson correlation test presented positive correlations between phospholipids and proteins level and between cortisol concentration and phospholipids in transmittance spectra. Negative correlations between cortisol concentration and proteins and phospholipids level was observed in the Raman spectra. Both optical techniques are considered to become effective tools for estimating the concentration of cortisol in the plasma.

Protein Secondary Structure Prediction Approaches

2020 ◽  
pp. 251-273
Author(s):  
Fawaz H. H. Mahyoub ◽  
Rosni Abdullah
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Computational Techniques ◽  
Protein Secondary Structure Prediction ◽  
Prediction Systems ◽  
Secondary Structure Of Proteins ◽  
Structure Of Proteins

The prediction of protein secondary structure from a protein sequence provides useful information for predicting the three-dimensional structure and function of the protein. In recent decades, protein secondary structure prediction systems have been improved benefiting from the advances in computational techniques as well as the growth and increased availability of solved protein structures in protein data banks. Existing methods for predicting the secondary structure of proteins can be roughly subdivided into statistical, nearest-neighbor, machine learning, meta-predictors, and deep learning approaches. This chapter provides an overview of these computational approaches to predict the secondary structure of proteins, focusing on deep learning techniques, with highlights on key aspects in each approach.

EXERCISING MULTI-LAYERED NETWORKS ON PROTEIN SECONDARY STRUCTURE

1992 ◽  
Vol 03 (supp01) ◽  
pp. 209-220 ◽  
Author(s):  
Burkhard Rost ◽  
Chris Sander
Keyword(s):  
Secondary Structure ◽  
Level Structure ◽  
Protein Secondary Structure ◽  
Percentage Points ◽  
Layered Networks ◽  
Evolutionarily Conserved ◽  
Secondary Structure Of Proteins ◽  
Conserved Amino Acids ◽  
Structure Of Proteins

The quality of a multi-layered network predicting the secondary structure of proteins is improved substantially by: (i) using information about evolutionarily conserved amino acids (increase of overall accuracy by six percentage points), (ii) balancing the training dynamics (increase of accuracy for strand), and (iii) combining uncorrelated networks in a jury (increase two percentage points). In addition, appending a second level structure-to-structure network results in better reproduction of the length of secondary structure segments.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

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