1
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Yamamoto K, Nagatoishi S, Matsunaga R, Nakakido M, Kuroda D, Tsumoto K. Conformational features and interaction mechanisms of V H H antibodies with β-hairpin CDR3: A case of Nb8-HigB2 interaction. Protein Sci 2023; 32:e4827. [PMID: 37916305 PMCID: PMC10661080 DOI: 10.1002/pro.4827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/07/2023] [Accepted: 10/30/2023] [Indexed: 11/03/2023]
Abstract
The β-hairpin conformation is regarded as an important basic motif to form and regulate protein-protein interactions. Single-domain VH H antibodies are potential therapeutic and diagnostic tools, and the third complementarity-determining regions of the heavy chains (CDR3s) of these antibodies are critical for antigen recognition. Although the sequences and conformations of the CDR3s are diverse, CDR3s sometimes adopt β-hairpin conformations. However, characteristic features and interaction mechanisms of β-hairpin CDR3s remain to be fully elucidated. In this study, we investigated the molecular recognition of the anti-HigB2 VH H antibody Nb8, which has a CDR3 that forms a β-hairpin conformation. The interaction was analyzed by evaluation of alanine-scanning mutants, molecular dynamics simulations, and hydrogen/deuterium exchange mass spectrometry. These experiments demonstrated that positions 93 and 94 (Chothia numbering) in framework region 3, which is right outside CDR3 by definition, play pivotal roles in maintaining structural stability and binding properties of Nb8. These findings will facilitate the design and optimization of single-domain antibodies.
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Affiliation(s)
- Koichi Yamamoto
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
| | - Satoru Nagatoishi
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
- The Institute of Medical ScienceThe University of TokyoTokyoJapan
- Medical Device Development and Regulation Research Center, School of EngineeringThe University of TokyoTokyoJapan
| | - Ryo Matsunaga
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
| | - Makoto Nakakido
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
| | - Daisuke Kuroda
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
- Research Center for Drug and Vaccine DevelopmentNational Institute of Infectious DiseasesTokyoJapan
| | - Kouhei Tsumoto
- Department of Bioengineering, Graduate School of EngineeringThe University of TokyoTokyoJapan
- The Institute of Medical ScienceThe University of TokyoTokyoJapan
- Medical Device Development and Regulation Research Center, School of EngineeringThe University of TokyoTokyoJapan
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2
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Gogoi CR, Rahman A, Saikia B, Baruah A. Protein Dihedral Angle Prediction: The State of the Art. ChemistrySelect 2023. [DOI: 10.1002/slct.202203427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
| | - Aziza Rahman
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Bondeepa Saikia
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Anupaul Baruah
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
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3
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Hu X, Ge R, Feng Z. Recognizing five molecular ligand-binding sites with similar chemical structure. J Comput Chem 2019; 41:110-118. [PMID: 31642535 DOI: 10.1002/jcc.26077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/27/2019] [Accepted: 08/31/2019] [Indexed: 02/03/2023]
Abstract
Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations. The data set of binding sites by five ligands (ATP, ADP, GTP, GDP, and NAD) are collated from Biolip database. Then, five features, containing increment of diversity value, matrix scoring value, auto-covariance, secondary structure information, and surface accessibility information are used in binding site predictions. The support vector machine (SVM) model is used with the five features to predict ligand-binding sites. Finally, prediction results are tested by fivefold cross validation. Accuracy (Acc) of five ligands (ATP, ADP, GTP, GDP, and NAD) achieves 77.4%, 71.2%, 82.1%, 82.9%, and 85.3%, respectively; and Matthew correlation coefficient (MCC) of the above five ligands achieves 0.549, 0.424, 0.643, 0.659, and 0.702, respectively. The research result shows that for ligands with similar chemical structures, microenvironment of their binding sites and their sensitivities to features are similar, while, differences of their ligand-binding properties exist at the same time. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiuzhen Hu
- Departments of Physics, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Riletu Ge
- Departments of Physics, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Zhenxing Feng
- Departments of Mathematics, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
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4
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Oldfield CJ, Chen K, Kurgan L. Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences. Methods Mol Biol 2019; 1958:73-100. [PMID: 30945214 DOI: 10.1007/978-1-4939-9161-7_4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.
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Affiliation(s)
- Christopher J Oldfield
- Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - Ke Chen
- School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin, People's Republic of China
| | - Lukasz Kurgan
- Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA.
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5
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Li D, Hu X, Liu X, Feng Z, Ding C. Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes. Saudi J Biol Sci 2016; 24:1361-1369. [PMID: 28855832 PMCID: PMC5562482 DOI: 10.1016/j.sjbs.2016.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 11/16/2016] [Accepted: 11/17/2016] [Indexed: 11/28/2022] Open
Abstract
β-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the β-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme β-hairpin database containing 2818 β-hairpins and 1098 non-β-hairpins. With 5-fold cross-validation on the training dataset, the overall accuracy of 90.08% and Matthew’s correlation coefficient (Mcc) of 0.74 were obtained, while on the independent test dataset, the overall accuracy of 88.93% and Mcc of 0.76 were achieved. Furthermore, the method was validated on 845 β-hairpins with ligand binding sites. With 5-fold cross-validation on the training dataset and independent test on the test dataset, the overall accuracies were 85.82% (Mcc of 0.71) and 84.78% (Mcc of 0.70), respectively. With an integration of mRMR feature selection and SVM algorithm, a reasonable high accuracy was achieved, indicating the method to be an effective tool for the further studies of β-hairpins in enzymes structure. Additionally, as a novelty for function prediction of enzymes, β-hairpins with ligand binding sites were predicted. Based on this work, a web server was constructed to predict β-hairpin motifs in enzymes (http://202.207.29.251:8080/).
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Affiliation(s)
- Dongmei Li
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Xingxing Liu
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Changjiang Ding
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
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6
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Sun L, Hu X, Li S, Jiang Z, Li K. Prediction of complex super-secondary structure βαβ motifs based on combined features. Saudi J Biol Sci 2015; 23:66-71. [PMID: 26858540 PMCID: PMC4705255 DOI: 10.1016/j.sjbs.2015.10.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 10/08/2015] [Accepted: 10/12/2015] [Indexed: 11/17/2022] Open
Abstract
Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins.
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Affiliation(s)
| | - Xiuzhen Hu
- Corresponding author. Tel.: +86 471 6576281; fax: +86 471 6575863.
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7
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YongE F, GaoShan K. Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts. PLoS One 2015; 10:e0139280. [PMID: 26422468 PMCID: PMC4589334 DOI: 10.1371/journal.pone.0139280] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 09/09/2015] [Indexed: 01/13/2023] Open
Abstract
Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew’s correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.
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Affiliation(s)
- Feng YongE
- College of Science, Inner Mongolia Agriculture University, Hohhot, PR China
- * E-mail:
| | - Kou GaoShan
- College of Science, Inner Mongolia Agriculture University, Hohhot, PR China
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8
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Kou G, Feng Y. Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts. J Theor Biol 2015; 380:392-8. [DOI: 10.1016/j.jtbi.2015.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 06/02/2015] [Accepted: 06/04/2015] [Indexed: 10/23/2022]
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9
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Singh H, Singh S, Raghava GPS. Evaluation of protein dihedral angle prediction methods. PLoS One 2014; 9:e105667. [PMID: 25166857 PMCID: PMC4148315 DOI: 10.1371/journal.pone.0105667] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 07/26/2014] [Indexed: 11/30/2022] Open
Abstract
Tertiary structure prediction of a protein from its amino acid sequence is one of the major challenges in the field of bioinformatics. Hierarchical approach is one of the persuasive techniques used for predicting protein tertiary structure, especially in the absence of homologous protein structures. In hierarchical approach, intermediate states are predicted like secondary structure, dihedral angles, Cα-Cα distance bounds, etc. These intermediate states are used to restraint the protein backbone and assist its correct folding. In the recent years, several methods have been developed for predicting dihedral angles of a protein, but it is difficult to conclude which method is better than others. In this study, we benchmarked the performance of dihedral prediction methods ANGLOR and SPINE X on various datasets, including independent datasets. TANGLE dihedral prediction method was not benchmarked (due to unavailability of its standalone) and was compared with SPINE X and ANGLOR on only ANGLOR dataset on which TANGLE has reported its results. It was observed that SPINE X performed better than ANGLOR and TANGLE, especially in case of prediction of dihedral angles of glycine and proline residues. The analysis suggested that angle shifting was the foremost reason of better performance of SPINE X. We further evaluated the performance of the methods on independent ccPDB30 dataset and observed that SPINE X performed better than ANGLOR.
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Affiliation(s)
- Harinder Singh
- Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India
| | - Sandeep Singh
- Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India
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10
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Prediction of four kinds of simple supersecondary structures in protein by using chemical shifts. ScientificWorldJournal 2014; 2014:978503. [PMID: 25050407 PMCID: PMC4090465 DOI: 10.1155/2014/978503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 06/03/2014] [Accepted: 06/04/2014] [Indexed: 12/23/2022] Open
Abstract
Knowledge of supersecondary structures can provide important information about its spatial structure of protein. Some approaches have been developed for the prediction of protein supersecondary structure. However, the feature used by these approaches is primarily based on amino acid sequences. In this study, a novel model is presented to predict protein supersecondary structure by use of chemical shifts (CSs) information derived from nuclear magnetic resonance (NMR) spectroscopy. Using these CSs as inputs of the method of quadratic discriminant analysis (QD), we achieve the overall prediction accuracy of 77.3%, which is competitive with the same method for predicting supersecondary structures from amino acid compositions in threefold cross-validation. Moreover, our finding suggests that the combined use of different chemical shifts will influence the accuracy of prediction.
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11
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Bonet J, Planas-Iglesias J, Garcia-Garcia J, Marín-López MA, Fernandez-Fuentes N, Oliva B. ArchDB 2014: structural classification of loops in proteins. Nucleic Acids Res 2013; 42:D315-9. [PMID: 24265221 PMCID: PMC3964960 DOI: 10.1093/nar/gkt1189] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.
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Affiliation(s)
- Jaume Bonet
- Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Barcelona, Catalonia, 08950, Spain and Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, SY23 3DA Aberystwyth, Ceredigion, UK
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12
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Ho HK, Zhang L, Ramamohanarao K, Martin S. A survey of machine learning methods for secondary and supersecondary protein structure prediction. Methods Mol Biol 2013; 932:87-106. [PMID: 22987348 DOI: 10.1007/978-1-62703-065-6_6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.
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Affiliation(s)
- Hui Kian Ho
- Department of Computer Science and Software Engineering, University of Melbourne, National ICT Australia, Parkville, VIC, Australia
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13
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Li T, Li QZ. Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure. J Theor Biol 2012; 312:55-64. [PMID: 22874580 DOI: 10.1016/j.jtbi.2012.07.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Revised: 07/19/2012] [Accepted: 07/21/2012] [Indexed: 12/11/2022]
Abstract
RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.
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Affiliation(s)
- Tao Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qian-Zhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China.
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14
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Chen K, Kurgan L. Computational prediction of secondary and supersecondary structures. Methods Mol Biol 2012; 932:63-86. [PMID: 22987347 DOI: 10.1007/978-1-62703-065-6_5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The sequence-based prediction of the secondary and supersecondary structures enjoys strong interest and finds applications in numerous areas related to the characterization and prediction of protein structure and function. Substantial efforts in these areas over the last three decades resulted in the development of accurate predictors, which take advantage of modern machine learning models and availability of evolutionary information extracted from multiple sequence alignment. In this chapter, we first introduce and motivate both prediction areas and introduce basic concepts related to the annotation and prediction of the secondary and supersecondary structures, focusing on the β hairpin, coiled coil, and α-turn-α motifs. Next, we overview state-of-the-art prediction methods, and we provide details for 12 modern secondary structure predictors and 4 representative supersecondary structure predictors. Finally, we provide several practical notes for the users of these prediction tools.
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Affiliation(s)
- Ke Chen
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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15
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Zou D, He Z, He J, Xia Y. Supersecondary structure prediction using Chou's pseudo amino acid composition. J Comput Chem 2010; 32:271-8. [DOI: 10.1002/jcc.21616] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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16
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Zou D, He Z, He J. Beta-hairpin prediction with quadratic discriminant analysis using diversity measure. J Comput Chem 2009; 30:2277-84. [PMID: 19263434 DOI: 10.1002/jcc.21229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
On the basis of the features of protein sequential pattern, we used the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to predict beta-hairpins motifs in protein sequences. Three rules are used to extract the raw beta-beta motifs sequential patterns for fixed-length. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are combined to represent the compositional features. Eighteen feature variables on a sequential pattern to be predicted are defined in terms of ID. They are integrated in a single formal framework given by IDQD. The method is trained and tested on ArchDB40 dataset containing 3088 proteins. The overall accuracy of prediction and Matthew's correlation coefficient for the independent testing dataset are 81.7% and 0.60, respectively. In addition, a higher accuracy of 84.5% and Matthew's correlation coefficient of 0.68 for the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nucleic Acids Res 2005, 33, 154), which contains 2088 proteins. For a fair assessment of our method, the performance is also evaluated on all 63 proteins used in CASP6. The overall accuracy of prediction is 74.2% for the independent testing dataset.
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Affiliation(s)
- Dongsheng Zou
- College of Computer Science, Chongqing University, Chongqing 400044, China.
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17
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Zhang N, Ruan J, Duan G, Gao S, Zhang T. The interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of beta-strands. Biochem Biophys Res Commun 2009; 386:537-43. [PMID: 19540200 DOI: 10.1016/j.bbrc.2009.06.072] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2009] [Accepted: 06/16/2009] [Indexed: 12/12/2022]
Abstract
It is widely considered that it is not appropriate to treat beta-pairs in isolation, since other secondary structural models (such as helices, coils), protein topology and protein tertiary structures would limit beta-strand pairing. However, to understand the underlying mechanisms of beta-sheet formation, studies ought to be performed separately on more concrete aspects. In this study, we focus on the parallel or antiparallel orientation of beta-strands. First, statistical analysis was performed on the relative frequencies of the interstrand amino acid pairs within parallel and antiparallel beta-strands. Consequently, features were extracted by singular value decomposition from the statistical results. By using the support vector machine to distinguish the features extracted from the two types of beta-strands, high accuracy was achieved (up to 99.4%). This suggests that the interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of beta-strands. These results may provide useful information for developing other useful algorithms to examine to the beta-strand folding pathways, and could eventually lead to protein structure predictions.
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Affiliation(s)
- Ning Zhang
- Key Laboratory of Bioactive Materials, Ministry of Education and College of Life Science, Nankai University, Tianjin 300071, PR China
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18
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Recognition of β-hairpin motifs in proteins by using the composite vector. Amino Acids 2009; 38:915-21. [DOI: 10.1007/s00726-009-0299-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2008] [Accepted: 04/20/2009] [Indexed: 10/20/2022]
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