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作者： Walter Pirovano Jaap Heringa

While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user. less

关键词： Multiple Sequence Alignment protein secondary structure prediction structure prediction protein structure Analysis secondary structure

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Cascaded bidirectional recurrent neural networks for protein secondary structure prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 572-582页

作者： Chen, Jinmiao Chaudhari, Narendra S. Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of nonhomologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on a cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against two other BRNN architectures, namely, the original BRNN architecture used for speech recognition and Pollastri's BRNN, which was proposed for PSS prediction. Our cascaded BRNN achieves an overall three-state accuracy Q3 of 74.38 percent and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6 percent.

关键词： protein secondary structure prediction cascaded bidirectional recurrent neural networks long-range interactions

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Neuro-fuzzy structural classification of proteins for improved protein secondary structure prediction

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PROTEOMICS 2003年第8期3卷 1464-1475页

作者： Hering, JA Innocent, PR Haris, PI De Montfort Univ Sch Mol Sci Leicester LE1 9BH Leics England De Montfort Univ Dept Comp Sci Leicester LE1 9BH Leics England

Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neurofuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91 % for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins.

关键词： adaptive neuro-fuzzy inference systems Fourier transform infrared neural networks protein secondary structure prediction structural classification of proteins

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PCI-SS: Web-based human and machine interfaces for protein secondary structure prediction

PCI-SS: Web-based human and machine interfaces for protein s...

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Canadian Conference on Electrical and Computer Engineering

作者： Aboul-Magd, Mohammed Green, James R. Carleton Univ Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781424416424

PCI-SS is a web-based protein secondary structure prediction service that makes use of novel dynamic nonlinear system identification techniques. When used to combine multiple prediction systems, PCI-SS exhibits state-of-the-art prediction accuracy while reducing confusion of helix and strand structures, a particularly detrimental class of error, by up to 25%. Unlike other available secondary structure prediction methods, PCI-SS provides both human-readable and machine-readable web interfaces. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This facilitates incorporation of PCI-SS into systems biology analysis pipelines that require protein secondary structure information, and greatly simplifies high-throughput analyses. WSDL is used to precisely define the SOAP interface. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service.

关键词： protein secondary structure prediction PCI SOAP

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Estimating the Class Posterior Probabilities in protein secondary structure prediction

Estimating the Class Posterior Probabilities in Protein Seco...

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6th IAPR International Conference on Pattern Recognition in Bioinformatics

作者： Guermeur, Yann Thomarat, Fabienne LORIA Equipe ABC F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783642248542

Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not class posterior probability estimates. This paper addresses the problem of post-processing the outputs of multi-class support vector machines used as sequence-to-structure classifiers with a structure-to-structure classifier estimating the class posterior probabilities. The aim of this comparative study is to obtain improved performance with respect to both criteria: prediction accuracy and quality of the estimates.

关键词： protein secondary structure prediction multi-class support vector machines class membership probabilities

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Multi-layer ensemble classifiers on protein secondary structure prediction

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4th International Conference on Intelligent Computing

作者： Li, Wei Chen, Yuehui Zhao, Yaou Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783540874409

protein secondary structure prediction is a bridge between amino acid sequence and tertiary prediction. Many methods have been used to improve the prediction accuracy and have a great development. Here a new method to predict the secondary structure is proposed by using classified database. The structure is similar with PSI-PRED method. But in the middle of the two neural network (NN) a neural ensemble is used to divided the initial database into two databases. Then the two databases are input into the next NN separately in order to predict the possible secondary structure. Experiments show that the proposed method achieves an average Q(3) score of 82% in CB396 database and 86% in CB513 database which produces the best prediction accuracy.

关键词： Ensemble classification protein secondary structure prediction PSSM

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New Trend of protein secondary structure prediction

New Trend of Protein Secondary Structure Prediction

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International Symposium on Intelligent Ubiquitous Computing and Education

作者： Zhai, Yun Yang, Bingru Wang, Lijun An, Bing Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China Liaocheng Univ Sch Comp Liaocheng Peoples R China

ISBN: (纸本)9780769536194

At first, this paper reviews the development history of the protein secondary structure prediction. Some concerned secondary structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49% and RS126 with 82.79% respectively. In the end, this paper points out several possible trends in the protein secondary structure prediction.

关键词： protein secondary structure prediction KDD combination method trend

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Cascading Discriminant and Generative Models for protein secondary structure prediction

Cascading Discriminant and Generative Models for Protein Sec...

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7th International-Association-for-Pattern-Recognition (IAPR) International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Thomarat, Fabienne Lauer, Fabien Guermeur, Yann Univ Lorraine INRIA CNRS LORIA F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783642341236

Most of the state-of-the-art methods for protein seconday structure prediction are complex combinations of discriminant models. They apply a local approach of the prediction which is known to induce a limit on the expected prediction accuracy. A priori, the use of generative models should make it possible to overcome this limitation. However, among the numerous hidden Markov models which have been dedicated to this task over more than two decades, none has come close to providing comparable performance. A major reason for this phenomenon is provided by the nature of the relevant information. Indeed, it is well known that irrespective of the model implemented, the prediction should benefit significantly from the availability of evolutionary information. Currently, this knowledge is embedded in position-specific scoring matrices which cannot be processed easily with hidden Markov models. With this observation at hand, the next significant advance should come from making the best of the two approaches, i.e., using a generative model on top of discriminant models. This article introduces the first hybrid architecture of this kind with state-of-the-art performance. The conjunction of the two levels of treatment makes it possible to optimize the recognition rate both at the residue level and at the segment level.

关键词： protein secondary structure prediction discriminant models class membership probabilities hidden Markov models

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protein secondary structure prediction with partially recurrent neural networks

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SAR and QSAR in Environmental Research 1993年第2-3期1卷 153-159页

作者： M Reczko Department of Molecular Biophysics German Cancer Research Center Heidelberg Germany.

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures a-helix, sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13x3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%. © 1993, Taylor & Francis Group, LLC. All rights reserved.

关键词： hierarchical architectures incremental training protein secondary structure prediction recurrent neural networks

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A Novel Radical Group Encoding Method for protein secondary structure prediction 2

A Novel Radical Group Encoding Method for Protein Secondary ...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Zhang Shuai-yan Liu Yi-hui School of information Qilu University of Technology Institute of Intelligent Information Processing Qilu University of Technology

ISBN: (纸本)9781509036202

In this paper,a new coding method called radical group encoding is used for protein secondary structure *** radical group encoding is used to encode 20 common amino acids,it has 42 features and every feature shows the protein *** use the new encoding method to predict the secondary *** all amino acids can be represented by the coding,it contains the information of stable structure of atoms which exists in amino acids or *** 3 fold cross validation experiments are used with different window length,we will use the Bayes classifier and support vector machine(SVM) to predict the secondary structure of *** will compare the new radical group encoding with the quadrature *** the two experiments,we can conclude that the new radical group encoding method,which contains the information of the internal stable structure of amino acid,has more 1.2% accuracy than the quadrature encoding.

关键词： protein secondary structure prediction radical group encoding quadrature encodin Bayes calssifier SVM

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