Effect Factors on Secondary Structure of Protein Sequence Pattern

2011 ◽  
Author(s):  
Tao Liu ◽  
Minghui Li
Keyword(s):  
Secondary Structure ◽  
Protein Sequence ◽  
Effect Factors ◽  
Sequence Pattern

Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

2019 ◽  
Vol 16 (4) ◽  
pp. 317-324
Author(s):  
Liang Kong ◽  
Lichao Zhang ◽  
Xiaodong Han ◽  
Jinfeng Lv
Keyword(s):  
Feature Extraction ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Function Analysis ◽  
Superior Performance ◽  
Support Vector ◽  
Chaos Game Representation ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

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.

Thermostable alanine racemase from Bacillus stearothermophilus: DNA and protein sequence determination and secondary structure prediction

Biochemistry ◽  
1988 ◽  
Vol 27 (4) ◽  
pp. 1311-1316 ◽  
Author(s):  
Katsuyuki Tanizawa ◽  
Atsushi Ohshima ◽  
Alfred Scheidegger ◽  
Kenji Inagaki ◽  
Hidehiko Tanaka ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Bacillus Stearothermophilus ◽  
Secondary Structure Prediction ◽  
Alanine Racemase ◽  
Sequence Determination

Protein Sequence and Structure of N-terminal Amino Acids of Subunit Delta of Spinach Photosynthetic ATP-Synthase CF1

1987 ◽  
Vol 42 (11-12) ◽  
pp. 1231-1238 ◽  
Author(s):  
Richard J. Berzborn ◽  
Werner Finke ◽  
Joachim Otto ◽  
Helmut E . Meyer
Keyword(s):  
Secondary Structure ◽  
Atp Synthase ◽  
Protein Sequence ◽  
Alpha Helix ◽  
Amino Acid Residues ◽  
E Coli ◽  
Helical Wheel ◽  
Terminal Amino ◽  
Amphipathic Alpha Helix ◽  
Structure Calculations

Chloroplast ATP-synthase (CF1) subunit delta (δ) has been isolated from spinach thylakoids in the presence of SDS. By automated Edman degradation and online analysis of PTH derivatives the 35 N-terminal amino acid residues were sequenced. The mature protein starts with: NH2-Val-Asp-Ser-Thr-Ala-Ser-Arg-Tyr-Ala-. This protein sequence allows alignment of spinach δ with the sequences of Z. mays 25 kDa polypeptide, the δ subunit of Rps. blastica, Rsp. rubrum and E. coli F1, and of bovine OSCP, but not with mitochondrial δ. Secondary structure calculations and helical wheel plots reveal a conserved secondary structure. The analyzed N-terminal sequences probably build a short amphipathic alpha helix with two adjacent turns. The such aligned polar residues around Tyr8 of subunit δ are suitable to channel protons.

scholarly journals Improved computational methods of protein sequence alignment, model selection and tertiary structure prediction

2013 ◽  
Author(s):  
◽  
Xin Deng
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Model Selection ◽  
Sequence Alignment ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Solvent Accessibility ◽  
Relative Solvent Accessibility ◽  
Tertiary Structure Prediction

Protein sequence and profile alignment has been used essentially in most bioinformatics tasks such as protein structure modeling, function prediction, and phylogenetic analysis. We designed a new algorithm MSACompro to incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into multiple protein sequence alignment. Our experiments showed that it improved multiple sequence alignment accuracy over most existing methods without using the structural information and performed comparably to the method using structural features and additional homologous sequences by slightly lower scores. We also developed HHpacom, a new profile-profile pairwise alignment by integrating secondary structure, solvent accessibility, torsion angle and inferred residue pair coupling information. The evaluation showed that the secondary structure, relative solvent accessibility and torsion angle information significantly improved the alignment accuracy in comparison with the state of the art methods HHsearch and HHsuite. The evolutionary constraint information did help in some cases, especially the alignments of the proteins which are of short lengths, typically 100 to 500 residues. Protein Model selection is also a key step in protein tertiary structure prediction. We developed two SVM model quality assessment methods taking query-template alignment as input. The assessment results illustrated that this could help improve the model selection, protein structure prediction and many other bioinformatics problems. Moreover, we also developed a protein tertiary structure prediction pipeline, of which many components were built in our group’s MULTICOM system. The MULTICOM performed well in the CASP10 (Critical Assessment of Techniques for Protein Structure Prediction) competition.

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