scholarly journals The predicted secondary structures of class I fructose-bisphosphate aldolases

1988 ◽  
Vol 249 (3) ◽  
pp. 789-793 ◽  
Author(s):  
L Sawyer ◽  
L A Fothergill-Gilmore ◽  
P S Freemont
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Alpha Helix ◽  
Rabbit Muscle ◽  
X Ray Diffraction ◽  
Fructose Bisphosphate ◽  
Compact Structure ◽  
Alpha Beta ◽  
Surface Loops

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turns for fructose-bisphosphate aldolases from human and rat muscle and liver, from Trypanosoma brucei and from Drosophila melanogaster. All the aldolase sequences gave essentially the same pattern of secondary-structure predictions despite having sequences up to 50% different. One exception to this pattern was an additional strongly predicted helix in the rat liver and Drosophila enzymes. Regions of relatively high sequence variation generally were predicted as reverse turns, and probably occur as surface loops. Most of the positions corresponding to exon boundaries are located between regions predicted to have secondary-structural elements consistent with a compact structure. The predominantly alternating alpha/beta structure predicted is consistent with the alpha/beta-barrel structure indicated by preliminary high-resolution X-ray diffraction studies on rabbit muscle aldolase [Sygusch, Beaudry & Allaire (1986) Biophys. J. 49, 287a].

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.

scholarly journals First determination of the secondary structure of purified factor VIII light chain

1992 ◽  
Vol 288 (1) ◽  
pp. 35-40 ◽  
Author(s):  
N Bihoreau ◽  
M P Fontaine-Aupart ◽  
A Lehegarat ◽  
M Desmadril ◽  
J M Yon
Keyword(s):  
Secondary Structure ◽  
Factor Viii ◽  
Light Chain ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Guanidine Hydrochloride ◽  
Alpha Helix ◽  
Light Chains ◽  
Regular Secondary Structure ◽  
Human Factor Viii

The first analysis of the secondary structure of human factor VIII light chain was performed by c.d. spectroscopy. The purification process described in this paper allowed us to obtain the large amounts of purified factor VIII light chains required for c.d. experiments. Since this 80 kDa protein is non-covalently associated with a heavy chain to form the active molecule, isolated factor VIII light chains were obtained after immunoadsorption and dissociation of the immobilized active complexes by EDTA. Furthermore, factor VIII light chains were discriminated from the residual active complexes and the free heavy chains by a final ion-exchange-chromatography step. This f.p.l.c. analysis showed that factor VIII light chains were less electronegative than the active complexes. The results of conformational analysis by c.d. show that the protein possesses a high degree of regular secondary structure (58%) with approx. 22% of alpha-helix and 36% of beta-strand structures. The protein was completely unfolded by 3 M-guanidine hydrochloride. The results obtained from the analysis of c.d. spectra were compared with those predicted from three different statistical methods based on amino-acid sequence. The secondary structure information obtained from these methods was in good agreement with the c.d. results. These results were comparable with the secondary structure prediction of ceruloplasmin, a protein known to show sequence identity to factor VIII.

scholarly journals The predicted secondary structure of enolase

1986 ◽  
Vol 236 (1) ◽  
pp. 127-130 ◽  
Author(s):  
L Sawyer ◽  
L A Fothergill-Gilmore ◽  
G A Russell
Keyword(s):  
Skeletal Muscle ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Alpha Helix ◽  
Beta Sheet ◽  
Secondary Structure Content ◽  
Reverse Turn ◽  
Yeast Enolase ◽  
Chicken Skeletal Muscle

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turn for both chicken skeletal-muscle and yeast enolase sequences. The predicted secondary-structure content of the chicken enzyme is 27% alpha-helix and less than 10% beta-sheet, whereas in the yeast enolase a similar helix content but virtually no sheet are predicted. These results are in fair agreement with published experimental estimates of the amount of secondary structure in the yeast enzyme. The enzyme appears to be formed from three domains.

scholarly journals DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures

2019 ◽  
Author(s):  
Jie Hou ◽  
Zhiye Guo ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Ab Initio ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Alpha Helix ◽  
Protein Secondary Structure Prediction ◽  
Learning Architectures

AbstractMotivationAccurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction.ResultsThe major improvements over the DNSS1 method include (i) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict secondary structure, and (ii) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on two independent test datasets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the 82 protein targets of 2018 CASP13 experiment and achieved the best Q3 score of 83.74% and SOV score of 72.46%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

scholarly journals Circular-dichroism studies on two murine serum amyloid A proteins

1988 ◽  
Vol 256 (3) ◽  
pp. 775-783 ◽  
Author(s):  
W D McCubbin ◽  
C M Kay ◽  
S Narindrasorasak ◽  
R Kisilevsky
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Amyloid Fibrils ◽  
Secondary Structure Prediction ◽  
Sequence Data ◽  
Heparan Sulphate ◽  
Alpha Helix ◽  
Serum Amyloid ◽  
Amyloid A ◽  
Helical Content

C.d. studies have shown that mouse SAA2 (serum amyloid A2) protein has about one-half of the alpha-helix content of the SAA1 (serum amyloid A1) analogue (15 as against 32%), although secondary-structure prediction analyses based on sequence data do not suggest such a large difference between the forms. The decreased helical content may be a reflection or indication of a stronger propensity to aggregation of the SAA2 form compared with SAA1. The main elements of secondary structure in both proteins are beta-sheets/turns. Interactions with Ca2+ are accompanied by small losses in alpha-helix content, whereas binding to chondroitin-6-sulphate in the presence of millimolar Ca2+ also decreases the amount of secondary structure. However, SAA2 binding to heparan sulphate increases its beta-sheet structure, whereas with SAA1 secondary structure is not apparently altered by its interaction with heparan sulphate. Computer-generated surface profiles show slight differences in accessibility, hydrophilicity and flexibility between the proteins. Understanding these differences may help to explain why SAA2 is found in amyloid fibrils whereas SAA1 is not. In particular, a stronger tendency to aggregation might be the reason why SAA2 is deposited exclusively in these fibrils.

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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