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.

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 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].

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 Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

2015 ◽  
Vol 112 (24) ◽  
pp. E3095-E3103 ◽  
Author(s):  
András Micsonai ◽  
Frank Wien ◽  
Linda Kernya ◽  
Young-Ho Lee ◽  
Yuji Goto ◽  
...  
Keyword(s):  
Circular Dichroism ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Amyloid Fibrils ◽  
Secondary Structure Prediction ◽  
Science Research ◽  
Cd Spectroscopy ◽  
Structure Selection ◽  
Β Sheets ◽  
Circular Dichroïsm

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides.

scholarly journals bpRNA: Large-scale Automated Annotation and Analysis of RNA Secondary Structure

2018 ◽  
Author(s):  
Padideh Danaee ◽  
Mason Rouches ◽  
Michelle Wiley ◽  
Dezhong Deng ◽  
Liang Huang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Single Molecule ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Large Scale ◽  
Secondary Structure Prediction ◽  
Sequence Data ◽  
Secondary Structures ◽  
Base Pairs ◽  
Statistical Trends

ABSTRACTWhile RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, “bpRNA-1m”, of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

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