scholarly journals MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns

2019 ◽  
Author(s):  
Chao Fang ◽  
Zhaoyu Li ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Sequence Similarity ◽  
Protein Secondary Structure ◽  
Web Server ◽  
Experimental Tests ◽  
Secondary Structures ◽  
Supplementary Information ◽  
Beta Turns ◽  
Torsion Angles ◽  
Protein Secondary Structures ◽  
Protein Functions

Abstract Motivation Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, developed based on advanced deep neural networks, achieved state-of-the-art performance for predicting secondary structures, backbone torsion angles, beta-turns and gamma-turns, respectively. An easy-to-use web service will provide the community a convenient way to use these methods for research and development. Results MUFold-SSW, a new web server, is presented. It provides predictions of protein secondary structures, torsion angles, beta-turns and gamma-turns for a given protein sequence. This server implements MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, which performed well for both easy targets (proteins with weak sequence similarity in PDB) and hard targets (proteins without detectable similarity in PDB) in various experimental tests, achieving results better than or comparable with those of existing methods. Availability and implementation MUFold-SSW is accessible at http://mufold.org/mufold-ss-angle. Supplementary information Supplementary data are available at Bioinformatics online.

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

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.

scholarly journals DeepGOPlus: improved protein function prediction from sequence

2019 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Protein Function ◽  
Drug Targets ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Supplementary Information ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Protein Functions ◽  
Novel Method

Abstract Motivation Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein–protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time. Results We developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins (if available). We evaluate the performance of DeepGOPlus using the CAFA3 evaluation measures and achieve an Fmax of 0.390, 0.557 and 0.614 for BPO, MFO and CCO evaluations, respectively. These results would have made DeepGOPlus one of the three best predictors in CCO and the second best performing method in the BPO and MFO evaluations. We also compare DeepGOPlus with state-of-the-art methods such as DeepText2GO and GOLabeler on another dataset. DeepGOPlus can annotate around 40 protein sequences per second on common hardware, thereby making fast and accurate function predictions available for a wide range of proteins. Availability and implementation http://deepgoplus.bio2vec.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction

2021 ◽  
Vol 22 (21) ◽  
pp. 11449
Author(s):  
Gabriel Bianchin de Oliveira ◽  
Helio Pedrini ◽  
Zanoni Dias
Keyword(s):  
Computational Methods ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Machine Learning Techniques ◽  
Laboratory Methods ◽  
Protein Secondary Structures ◽  
Learning Techniques ◽  
Template Free

Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—i) template-free classifiers, based on machine learning techniques; and ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.

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 IGLOSS: iterative gapless local similarity search

2019 ◽  
Vol 35 (18) ◽  
pp. 3491-3492
Author(s):  
Braslav Rabar ◽  
Maja Zagorščak ◽  
Strahil Ristov ◽  
Martin Rosenzweig ◽  
Pavle Goldstein
Keyword(s):  
Parameter Estimation ◽  
Similarity Search ◽  
Sequence Similarity ◽  
Web Server ◽  
Supplementary Information ◽  
Local Similarity ◽  
Supplementary Data ◽  
Matching Algorithm ◽  
Local Sequence ◽  
Sequence Patterns

Abstract Summary Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a new method for iterative motif scanning that will look for ungapped sequence patterns similar to a submitted query. Using careful parameter estimation and an adaptation of a fast string-matching algorithm, the method performs significantly better in this context than the existing software. Availability and implementation The IGLOSS web server is available at http://compbioserv.math.hr/igloss/. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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