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HYBRID SYSTEM FOR protein secondary structure prediction

JOURNAL OF MOLECULAR BIOLOGY 1992年第4期225卷 1049-1063页

作者： ZHANG, X MESIROV, JP WALTZ, DL Thinking Machines Corporation 245 First Street Cambridge MA 02142 U.S.A.

We have developed a hybrid system to predict the secondary structures (α-helix, β-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of C coil = 0.429, C α = 0.470 and C β = 0.387. This system contains three subsystems (“experts”): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77 % of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts.

关键词： protein secondary structure prediction HYBRID SYSTEM NEURAL NETWORKS MEMORY-BASED REASONING STATISTICAL METHODS

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Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses

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JOURNAL OF MOLECULAR BIOLOGY 1996年第2期263卷 196-208页

作者： Goldman, N Thorne, JL Jones, DT N CAROLINA STATE UNIV DEPT STAT PROGRAM STAT GENET RALEIGH NC 27695 USA UNIV WARWICK DEPT BIOL SCI COVENTRY CV4 7AL W MIDLANDS ENGLAND

Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments, A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method, (C) 1996 Academic Press Limited

关键词： comparative sequence analysis evolution hidden Markov model phylogenetic trees protein secondary structure prediction

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Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction

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JOURNAL OF MOLECULAR MODELING 2012年第9期18卷 4275-4289页

作者： Saraswathi, S. Fernandez-Martinez, J. L. Kolinski, A. Jernigan, R. L. Kloczkowski, A. Ohio State Univ Coll Med Battelle Ctr Math Med Res InstNationwide Childrens HospDept Pediat Columbus OH 43205 USA Univ Oviedo Dept Math Oviedo Spain Univ Warsaw Fac Chem Lab Theory Biopolymers PL-02093 Warsaw Poland Iowa State Univ Dept Biochem Biophys & Mol Biol Ames IA USA

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called fast learning optimized prediction methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. protein secondary structures are predicted reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83 % and 87 % over a widerange of hidden neurons and a cross-validated testing accuracy ranging between 81 % and 84 % and a segment overlap (SOV) score of 78 % that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost.

关键词： Knowledge-based potentials Machine learning Neural networks Particle swarm optimization protein secondary structure prediction

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protein secondary structure prediction

Protein Secondary Structure Prediction

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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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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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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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Ensemble of Bidirectional Recurrent Networks and Random Forests for protein secondary structure prediction 27

Ensemble of Bidirectional Recurrent Networks and Random Fore...

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27th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil

ISBN: (数字)9781728175393

ISBN: (纸本)9781728175393

The prediction of secondary protein structures is one of the classic problems of bioinformatics and has several practical applications. In this work, we present an ensemble of bidirectional recurrent networks (capable of doing long-term analyses of amino acid sequences) with random forests (capable of doing local analyses). The fusion was performed using weights for each of the classifiers and classes, found by searching through genetic algorithms. To evaluate our method, we used the PDB, CB6133 and CB513 datasets. We achieved 73.1%, 59.1% and 55.8% Q8 accuracy on PDB, CB6133 and CB513 proteins, respectively, whereas 81.5% Q3 accuracy on PDB proteins using only amino acid sequence information. In order to compare our results against the literature, we also evaluated our method using amino acid sequence and sequence profile features, achieving 73.4% and 68.9% Q8 accuracy on CB6133 and CB513. Our method yielded good results when using only the amino acid sequence and presents competitive results compared to the literature when using amino acid sequence information and protein sequence similarity.

关键词： protein secondary structure prediction Bidirectional Recurrent Network Random Forest Ensemble

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