FTIR Analysis of Conformational Changes in the Secondary Structure of Ovalbumin: Effect of pH and Cosolvent Sugar-free Natura

2020 ◽  
Vol 8 (1) ◽  
pp. 78-83
Author(s):  
P. Agalya ◽  
◽  
V. Velusamy
Keyword(s):  
Secondary Structure ◽  
Conformational Changes ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Structural Elements ◽  
Random Coils ◽  
Protein Secondary Structures ◽  
Derivative Analysis ◽  
Influence Of Ph

a-helix, þ-sheet, þ-turns, and random coils are the three-dimensional local segments that constitute a protein secondary structure. Molecular vibrations of proteins are sensitive to structural organizations of peptide chains hence Fourier Transform infrared (FTIR) spectroscopy is one of the recognized techniques for the identification of protein secondary structures. However, the lower frequency region of FTIR especially the amide VI bands (in the region 590-490cm-1) is little studied for proteins. Further, the effect of sugar-free natura on ovalbumin stability is not yet studied to our knowledge. The present study examines the conformational changes in the secondary structure of ovalbumin (OVA) protein under the influence of pH variations (2, 5, 7, 9, and 12) and also cosolvent sugar-free Natura (SFN) inclusion. From the primary absorption spectra of the amide VI bands, the second derivative analysis is furnished to quantify the secondary structural elements of protein thereby conformational changes are analyzed. From obtained results, it is found that conformational changes occur between two major secondary structures of a-helix and þ-sheet of OVA due to variation of pH and inclusion of cosolvent. Also, the results confirm that the denaturation of OVA in the presence of SFN irrespective of pH.

In Silico Study of Secondary Structure of Hemoglobin Protein

2021 ◽  
pp. 6245-6249
Author(s):  
Roma Chandra
Keyword(s):  
Secondary Structure ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Alpha Helix ◽  
Prediction Methods ◽  
Protein Secondary Structures

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

Use of synchrotron-based FTIR microspectroscopy to determine protein secondary structures of raw and heat-treated brown and golden flaxseeds: A novel approach

2005 ◽  
Vol 85 (4) ◽  
pp. 437-448 ◽  
Author(s):  
P. Yu ◽  
J. J. McKinnon ◽  
H. W. Soita ◽  
C. R. Christensen ◽  
D. A. Christensen
Keyword(s):  
Secondary Structure ◽  
Matrix Protein ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Heat Processing ◽  
Lower Percentage ◽  
Protein Secondary Structures ◽  
Novel Approach ◽  
Α Helix ◽  
Β Sheet

The objectives of the study were to use synchrotron Fourier transform infrared microspectroscopy (S-FTIR) as a novel approach to: (1) reveal ultra-structural chemical features of protein secondary structures of flaxseed tissues affected by variety (golden and brown) and heat processing (raw and roasted), and (2) quantify protein secondary structures using Gaussian and Lorentzian methods of multi-component peak modeling. By using multi-component peak modeling at protein amide I region of 1700–1620 cm-1, the results showed that the golden flaxseed contained relatively higher percentage of α-helix (47.1 vs. 36.9%), lower percentage of β-sheet (37.2 vs. 46.3%) and higher (P < 0.05) ratio of α-helix to β-sheet than the brown flaxseed (1.3 vs. 0.8). The roasting reduced (P < 0.05) percentage of α-helix (from 47.1 to 36.1%), increased percentage of β-sheet (from 37.2 to 49.8%) and reduced α-helix to β-sheet ratio (1.3 to 0.7) of the golden flaxseed tissues. However, the roasting did not affect percentage and ratio of α-helix and β-sheet in the brown flaxseed tissue. No significant differences were found in quantification of protein secondary structures between Gaussian and Lorentzian methods. These results demonstrate the potential of highly spatially resolved S-FTIR to localize relatively pure protein in the tissue and reveal protein secondary structures at a cellular level. The results indicated relative differences in protein secondary structures between flaxseed varieties and differences in sensitivities of protein secondary structure to the heat processing. Further study is needed to understand the relationship between protein secondary structure and protein digestion and utilization of flaxseed and to investigate whether the changes in the relative amounts of protein secondary structures are primarily responsible for differences in protein availability. Key words: Synchrotron, FTIR microspectrosopy, flaxseeds, intrinsic structural matrix, protein secondary structures, protein nutritive value

scholarly journals Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 7049
Author(s):  
Maytha Alshammari ◽  
Jing He
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Dimensional Space ◽  
Protein A ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Sources Of Information ◽  
Density Map

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

Using long-range contact number information for protein secondary structure prediction

2014 ◽  
Vol 07 (05) ◽  
pp. 1450052 ◽  
Author(s):  
Yonge Feng ◽  
Liaofu Luo
Keyword(s):  
Secondary Structure ◽  
Long Range ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Negative Influence ◽  
Protein Secondary Structure Prediction ◽  
Contact Number ◽  
Protein Secondary Structures

In this paper, we first combine tetra-peptide structural words with contact number for protein secondary structure prediction. We used the method of increment of diversity combined with quadratic discriminant analysis to predict the structure of central residue for a sequence fragment. The method is used tetra-peptide structural words and long-range contact number as information resources. The accuracy of Q3 is over 83% in 194 proteins. The accuracies of predicted secondary structures for 20 amino acid residues are ranged from 81% to 88%. Moreover, we have introduced the residue long-range contact, which directly indicates the separation of contacting residue in terms of the position in the sequence, and examined the negative influence of long-range residue interactions on predicting secondary structure in a protein. The method is also compared with existing prediction methods. The results show that our method is more effective in protein secondary structures prediction.

scholarly journals Protein Secondary Structure Prediction With a Reductive Deep Learning Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiliang Lyu ◽  
Zhijin Wang ◽  
Fangfang Luo ◽  
Jianwei Shuai ◽  
Yandong Huang
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Resonance Spectroscopy ◽  
Data Set ◽  
X Ray Crystallography ◽  
Protein Secondary Structures

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.

scholarly journals Statistical dependence of protein secondary structure on amino acid bigrams

2006 ◽  
Vol 12 (1) ◽  
pp. 82-85
Author(s):  
Miodrag Zivkovic ◽  
Sasa Malkov ◽  
Snezana Zaric ◽  
Milena Vujosevic-Janicic ◽  
Jelena Tomasevic ◽  
...  
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Protein Secondary Structure ◽  
Data Bank ◽  
Secondary Structures ◽  
Statistical Dependence ◽  
Conditional Probabilities ◽  
Protein Secondary Structures ◽  
Protein Data Bank Database

The statistical dependence of protein secondary structure on amino acid bigram frequencies was studied. Proteins in the PDBSELECT subset of the Protein Data Bank database were investigated. Protein secondary structures were determined using DSSP software. The conditional probabilities of protein secondary structures were calculated and presented. The results on bigrams show the frequencies of all the possible bigrams in all secondary structure types. These results elucidate some factors important for the prediction of the secondary structures of proteins based on the amino acid sequence.

Searching techniques for databases of protein secondary structures

1989 ◽  
Vol 15 (4-5) ◽  
pp. 287-298 ◽  
Author(s):  
Peter J. Artymiuk ◽  
David W. Rice ◽  
Eleanor M. Mitchell ◽  
Peter Willett
Keyword(s):  
Secondary Structure ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Secondary Structures ◽  
British Library ◽  
Geometric Information ◽  
Protein Secondary Structures ◽  
Funded Research Project

This paper summarizes the findings of a recent, British Library-funded research project into computer techniques for searching the three-dimensional protein structures that occur in the Protein Data Bank. The work focuses on the secondary structures of proteins and utilizes both angular and distance geometric information. Algorithms are presented for the auto matic identification of secondary structure elements, of sec ondary structure motifs and of proteins with similar secondary structures.

scholarly journals Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michela Quadrini
Keyword(s):  
Hydrogen Bonding ◽  
Secondary Structure ◽  
Nucleotide Sequence ◽  
Thermus Thermophilus ◽  
Three Dimensional ◽  
Secondary Structures ◽  
Structural Effect ◽  
Biological Processes ◽  
Structural Pattern ◽  
Rna Molecules

Abstract RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors.

scholarly journals Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1773
Author(s):  
Bahareh Behkamal ◽  
Mahmoud Naghibzadeh ◽  
Mohammad Reza Saberi ◽  
Zeinab Amiri Tehranizadeh ◽  
Andrea Pagnani ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Graph Matching ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Secondary Structures ◽  
Computational Method ◽  
Target Sequence ◽  
Matching Problem ◽  
Medium Resolution

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images.

CSSP(Consensus Secondary Structure Prediction): a web-based server for structural biologists

2009 ◽  
Vol 42 (2) ◽  
pp. 336-338 ◽  
Author(s):  
Ankit Gupta ◽  
Avnish Deshpande ◽  
Janardhan Kumar Amburi ◽  
Radhakrishnan Sabarinathan ◽  
Ramaswamy Senthilkumar ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Web Server ◽  
Dimensional Structure ◽  
Structural Elements ◽  
Web Based ◽  
Consensus Secondary Structure ◽  
Helical Wheel

Sequence–structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

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