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Our model is both simple and easy to perform. We hope this model will assist investigation the topology of protein structures in the near future [ 47 — 49 ]. As demonstrated in a series of recent publications [ 50 — 53 ] in developing new prediction methods, user-friendly and publicly accessible web-servers will significantly enhance their impacts [ 54 ], we shall make efforts in our future work to provide a web-server for the prediction method presented in this paper. The authors are grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this paper.

Conceived and designed the experiments: FYE. Analyzed the data: FYE. Wrote the paper: FYE. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: All relevant data are within the paper and its Supporting Information files.

Introduction Protein function is inherently correlated with its structure. Materials and Methods Database All of the CSs data used in this paper were retrieved from the re-referenced protein chemical shift database RefDB [ 28 ]. Statistical distribution Under the normal distribution, the analysis of variance ANOVA can be used to test whether there was a significant difference for two-group or multi-group samples [ 19 , 31 ] in the database.

Quadratic discriminant analysis QDA As mentioned above [ 6 — 10 ], various parameters such as amino acid compositions and dipeptide compositions have been employed in the prediction of beta-hairpin. Performance evaluation In statistical prediction, the jackknife test is considered to be the most rigorous test method [ 36 ] and has been widely used to evaluate the performance of various predictors [ 37 — 41 ].

Results and Discussion Statistical distribution of the average CSs of six nuclei We analyzed the average chemical shifts of six nuclei in beta-hairpin and not beta-hairpin dataset. Download: PPT. Fig 1. Distribution chart of six-nuclei CSs in beta-hairpin and not beta-hairpin motifs. Table 1. Prediction of beta-hairpin based on the CSs of six nuclei Results in Table 1 suggest that the CSs of six nuclei are capable of predicting beta-hairpin.

Table 2. Table 3. Comparison with other feature To test our method and facilitate comparison with other feature, we used 20 amino acid compositions AAC as inputs of the method of QDA. Comparison with other approaches Some approaches have been developed for predicting the beta-hairpin motifs [ 7 — 10 ]. Table 4. The results of different approaches using the same six CSs information.

Conclusion In this paper, we have introduced a model for predicting beta-hairpin motifs based on CSs. Supporting Information. S1 File. S2 File. CSs data of beta-hairpin fragments. S3 File. CSs data of 75 not beta-hairpin fragments. S4 File. Acknowledgments The authors are grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this paper.

References 1. J Mol Biol. Improved protein loop prediction from sequence alone. Protein Eng. Prediction of protein super secondary structures based on the artificial neural network method. Chou KC. Prediction of beta-turns in proteins. J Pept Res. View Article Google Scholar 5. Classification and prediction of beta-turn types. J Protein Chem. Toward predicting protein topology: an approach to identifying beta hairpins. Amino Acids. Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins.

Kumar M, Bhasin M. Nucleic Acids Res. View Article Google Scholar Prediction of the B-hairpins in proteins using support vector machine. The Protein Journal. Chemical shift tensor-the heart of NMR: Insights into biological aspects of proteins. Prog Nucl Magn Reson Spectrosc. J Am Chem Soc. J Phys Chem B. Ab initio study of 13 Ca chemical shift anisotropy tensors in peptides. Case DA. The use of chemical shifts and their anisotropies in biomolecular structure determination.

Curr Opin Struct Biol. Use of chemical shifts in macromolecular structure determination. Methods Enzymol. Protein structure determination from NMR chemical shifts. The predictin of protein structural class using averaged chemical shifts. J Biomolecular Struc and Dynamics.

Martin M, Michael H. A probabilistic model for secondary structure prediction from protein chemical shifts. Protein structural class identification directly from NMR spectra using average chemical shifts. Pastore A, Saudek V. The relationship between chemical shift and secondary structure in proteins. J Magn Reson. Consistent blind protein structure generation from NMR chemical shift data. Wang Y.

Secondary structure effects on protein NMR chemical shifts. J Biomol NMR. Shen Y, Bax A. Identification of helix capping and beta-turn motifs from NMR chemical shifts. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.

RefDB: A database of uniformly referenced protein chemical shifts. Classification of common functional loops of kinase super-families. Wang G, Dunbrack RJ. Sprinthall RC. Basic statistical analysis. Prediction of protein secondary structure using feature selection and analysis approach. Acta Biotheoretica. Use of tetrapeptide signals for protein secondary structure prediction. Amino acids. Feng YE. Prediction of four kinds of simple super secondary structures in Protein by using chemical shifts.

Scientific world journal, , Identify five kinds of simple super secondary structures with quadratic discriminant algorithm based on the chemical shifts. J Theor Biol. Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms.

Nat Protocol. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. Hayat M, Khan A. Protein Pept Lett. Using over-represented tetrapeptides to predict protein submitochondria locations. Acta biotheoretica. Anal Biochem. Characterization-based Q-Q plots for testing multinormality. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics ; 9: Using same three methods in the ArchDB40 dataset, we obtain the accuracy and Matthew's correlation coefficient of View on IEEE.

Save to Library Save. Create Alert Alert. Share This Paper. Tables from this paper. Citation Type. Has PDF. Publication Type. More Filters. Biology, Computer Science. View 1 excerpt, cites methods. Enzymes are a kind of protein that has catalytic function. The study of supersecondary structures in enzymes plays an important role in the structure and function of enzymes.

Based on enzyme sequence … Expand. View 1 excerpt. Highly Influential. View 3 excerpts, references methods. View 6 excerpts, references methods, results and background.

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Protegrin-1 is an residue β-hairpin antimicrobial peptide (AMP) that has been suggested to form transmembrane β-barrels in biological. The motif typical of brevenins and known as “Rana box” was found in many amphibian AMPs: esculentins, gaegurins, and ranalexins. In all of these molecules, the. We have demonstrated that the PDC approach enables β-hairpin peptides isolated from protein surfaces to be multimerized and conformationally.