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An efficient Method for protein secondary structure prediction

An efficient Method for Protein Secondary Structure Predicti...

The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Tao WU Junjun MAO Ling ZHANG State Key Laboratory for Novel Software Technology Department of Computer Science & TechnologyNanj School of Mathematics and Computational Science Anhui University Hefei 230039 R.P.China Key Lab IC&SP Anhui University Hefei 230039 R.P.China

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper,a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes,the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result.

关键词： protein secondary structure prediction constructive machine learning spherical neighborhood encoding scheme

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MUPRED: A tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction

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proteinS-structure FUNCTION AND BIOINFORMATICS 2007年第3期66卷 664-670页

作者： Bondugula, Rajkumar Xu, Dong Univ Missouri Christopher S Bond Life Sci Ctr 271C Digital Biol Lab Dept Comp Sci Columbia MO 65211 USA

Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q(3)) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q(3) can be achieved if a query protein has a significant sequence identity (> 25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://***/mupred. proteins 2007;66:664-670. (c) 2006 Wiley-Liss, Inc.

关键词： protein secondary structure prediction fuzzy nearest neighbor neural network hybrid prediction system sequence profile template prediction accuracy assessment

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Predicting protein second structure using a novel hybrid method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第9期38卷 11657-11664页

作者： Yang, Bingru Qu, Wu Xie, Yonghong Zhai, Yun Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China

Accurate protein secondary structure predictions play an important role for direct tertiary structure modeling, and it also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this article, we propose a gradually enhanced, multi-layered prediction systematic model to predict protein secondary structure, Compound Pyramid Model (CPM). This model is composed of four independent coordination's layers by intelligent interfaces, synthesizes several methods, such as KDD*, mixed-modal SVM method, mixed-modal BP method and so on. The model penetrates the whole domain knowledge, and the effective physicochemical properties of amino acids are imported. On the RS126 data set, state overall per-residue accuracy, Q(3), reached 83.99%, while segment overlap (S0V99) accuracy increased to 80.6%. On the CB513 data set, Q(3) reached 85.58%, S0V99 accuracy increased to 79.84%. Meanwhile, the results are found to be superior to those produced by other methods with blind test dataset CASP8's sequences, including the popular Psipred method according to Q(3) and SOV99 accuracy. The result shows that our method has strong generalization ability. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： protein secondary structure prediction Knowledge discovery Compound Pyramid Model

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Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

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KNOWLEDGE-BASED SYSTEMS 2011年第2期24卷 304-313页

作者： Yang, Bingru Wu, Qu Ying, Zhou Sui, Haifeng Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research. In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD*) process, a mixed-modal back propagation neural network (MMBP) module and so on. Testing against data sets of non-redundant protein sequences returned values for the Q(3) accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy. Availability: http://***/protein_Web/. (C) 2010 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Physicochemical properties Mixed-modal SVM Compound pyramid model

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Hybrid SVM kernels for protein secondary structure prediction

Hybrid SVM kernels for protein secondary structure predictio...

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IEEE International Conference on Granular Computing

作者： Altun, Gulsah Hu, Hae-Jin Brinza, Dumitru Harrison, Robert W. Zelikovsky, Alex Pan, Yi

ISBN: (纸本)142440133X

The Support Vector Machine is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. When data are not linearly separable, data are mapped to a high dimensional future space using a nonlinear function which can be computed through a positive definite kernel in the input space. Using a suitable kernel function for a particular problem and input data can change the prediction results remarkably and improve the accuracy. The goal of this work is to find the best kernel functions that can be applied to different types of data and problems. In this paper, we propose two hybrid kernels SVMSM+RBF and SVMEDIT+RBF. SVMSM+RBF is designed by combining the best performed RBF kernel with substitution matrix (SM) based kernel developed by Vanschoenwinkel and Manderick. SVMEDIT+RBF kernel combines the RBF kernel and the edit kernel devised by Li and Jiang. We tested these two hybrid kernels on one of the widely studied problems in bioinformatics which is the protein secondary structure prediction problem. For the protein secondary structure problem, our results were 91% accuracy on HIE binary classifier.

关键词： kernel method protein secondary structure prediction support vector machine

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The Relative Importance of Input Encoding and Learning Methodology on protein secondary structure prediction

The Relative Importance of Input Encoding and Learning Metho...

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作者： Arnshea Clayton Georgia State University

学位级别：硕士

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.

关键词： Neural Networks protein secondary structure prediction Input Encoding Resilient Propagation Learning Vector Quantization

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Improved Chou-Fasman method for protein secondary structure prediction

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BMC BIOINFORMATICS 2006年第s4期7卷 S14-S14页

作者： Chen, Hang Gu, Fei Huang, Zhengge Zhejiang Univ Dept Biomed Engn Coll Biomed Engn & Instrument Sci Hangzhou 310027 Peoples R China Zhejiang Univ Coll Life Sci Dept Biotechnol Hangzhou 310027 Peoples R China Zhejiang Univ Ctr Engn & Sci Computat Dept Comp Sci Hangzhou 310027 Peoples R China

Background: protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. Results: We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10-18% improvement. It is also comparable to other currently popular methods considering all the indices. Conclusion: Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.

关键词： secondary structure Continuous Wavelet Transform protein secondary structure secondary structure Information protein secondary structure prediction

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protein secondary structure prediction USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2006年第4期6卷 551-567页

作者： Gubbi, Jayavardhana Lai, Daniel T. H. Palaniswami, Marimuthu Parker, Michael Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia St Vincents Inst Med Res Fitzroy Vic 3065 Australia

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou-Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q(3) accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on http://***/ISSNIP/bioinf/.

关键词： protein secondary structure prediction support vector machines position specific scoring matrix (PSSM) Chou-Fasman parameters Kyte-Dolittle hydrophobicity Grantham polarity reliability index novel encoding scheme

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Proteomic Tools for the Analysis of Cytoskeleton proteins

Proteomic Tools for the Analysis of Cytoskeleton Proteins

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作者： Ewa Wywial Vishnubhat Nataraj Dongre Shaneen M. Singh

Proteomic tools have become an essential part of the tool kit of the molecular biologist, and provide techniques for detecting homologous sequences, recognizing functional domains, modeling, and analyzing the three-dimensional structure for any given protein sequence. Although a wealth of structural and functional information is available for a large number of members of the various classes of cytoskeletal proteins, many more members remain uncharacterized. These computational tools that are freely and easily accessible to the scientific community provide an excellent starting point to predict the structural and functional properties of such partially or fully uncharacterized protein sequences, and can lead to elegantly designed experiments to probe the hypothesized function. This chapter discusses various proteomic analysis tools with a focus on protein structure and function predictions. less

关键词： Proteomics Multiple Sequence Alignment Homology Modeling structure Analysis protein secondary structure prediction structure prediction protein structure Analysis NCBI BLAST PROSITE Molecular Modeling X-ray Crystallography NMR Jmol protein Binding Site prediction Pull-down Assay Sequence similarity protein domains

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prediction of Beta-Turn in protein Using E-SSpred and Support Vector Machine

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protein JOURNAL 2009年第3-4期28卷 175-181页

作者： Liu, Lirong Fang, Yaping Li, Menglong Wang, Cuicui Sichuan Univ Coll Chem Key Lab Green Chem & Technol Minist Educ Chengdu 610064 Peoples R China

beta-Turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of beta-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q (total) barrier and achieved Q (total) = 80.9%, MCC = 0.44, and Q (predicted) higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that beta-turn prediction accuracy can be improved by inclusion of secondary structure information.

关键词： beta-Turn prediction protein secondary structure prediction Support vector machines

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