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作者： Morten Källberg Gohar Margaryan Sheng Wang Jianzhu Ma Jinbo Xu

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server (http://*** ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling. Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min. less

关键词： protein structure prediction Homology Modeling protein structure Analysis protein secondary structure prediction structure prediction Jmol Model quality assessment protein threading

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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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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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T-Cell Epitope prediction Methods: An Overview

T-Cell Epitope Prediction Methods: An Overview

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作者： Dattatraya V. Desai Urmila Kulkarni-Kale

The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same. less

关键词： Machine Learning Neural Network Model Molecular Docking Gene prediction protein secondary structure prediction Microarray Position-Specific Scoring Matrices Epitope Mapping Bioinformatics Immunoinformatics T-cell epitope Vaccine design Motif Proteasomal cleavage MHC–peptide binding TAP transport Quantitative matrix MHC polymorphism Epitope prediction algorithm

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Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure

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JOURNAL OF MOLECULAR MODELING 2013年第10期19卷 4337-4348页

作者： Saraswathi, S. Fernandez-Martinez, J. L. Kolinski, A. Jernigan, R. L. Kloczkowski, A. Nationwide Childrens Hosp Res Inst Battelle Ctr Math Med Columbus OH USA Univ Oviedo Dept Math Oviedo Spain Warsaw Univ Lab Theory Biopolymers Fac Chem PL-02093 Warsaw Poland Iowa State Univ Dept Biochem Biophys & Mol Biol Ames IA USA Ohio State Univ Coll Med Dept Pediat Columbus OH 43210 USA

Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generated by 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 to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. secondary structure predictions and their accuracy are usually presented for three secondary structure elements: alpha-helix, beta-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.

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

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Data Mining Techniques to Predict protein secondary structures

Data Mining Techniques to Predict Protein Secondary Structur...

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5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Fayech, Sond Es Essoussi, Nadia Limam, Mohamed Univ Tunis LARODEC ISG Tunis Tunisia

ISBN: (纸本)9781467358149;9781467358125

protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequences, a sequential pattern mining method to detect frequent patterns, features extraction and quantification approaches to prepare features and SVM method to predict structures. When tested on the most popular secondary structure datasets, DM-pred achieved a Q3 accuracy of 78.20% and a SOV of 76.49% which illustrates that it is one of the top range methods for protein secondary structure prediction.

关键词： protein secondary structure prediction clustering sequential pattern mining features SVM

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Influence of Encoding Scheme on protein secondary structure prediction

Influence of Encoding Scheme on Protein Secondary Structure ...

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7th World Congress on Intelligent Control and Automation

作者： Zou, Dongsheng He, Zhongshi He, Jingyuan Huang, Xiaofeng Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781424421138

secondary structure prediction of proteins has increasingly been a central research area in bioinformatics. In order to know which data encoding approach is more effective whiling predicting secondary structure using SVM, five approaches: ENCOrth, ENCFive, ENCCodBas, ENCCodExt and ENCProf are discussed in this paper. The results of data encoding are used as input of SVM. By performing ENCProf approach, the accuracy of Q3 can be improved 19.4%similar to 23.9% more than the other four approaches.

关键词： SVM data encoding protein secondary structure prediction Profile encoding

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Influence of Encoding Scheme on protein secondary structure prediction

Influence of Encoding Scheme on Protein Secondary Structure ...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.4

作者： Dongsheng Zou Zhongshi He Jingyuan He Xiaofeng Huang College of Computer Science Chongqing University Chongqing China

关键词： SVM Data encoding protein secondary structure prediction Profile encoding

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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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2008 Canadian Conference on Electrical and Computer Engineering (CCECE 2008), vol.4

作者： Mohammed Aboul-Magd James R. Green Systems and Computer Engineering Carleton University Canada

关键词： protein secondary structure prediction PCI SOAP

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HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

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NEURAL COMPUTING & APPLICATIONS 2012年第2期21卷 337-349页

作者： Qu, Wu Yang, Bingru Jiang, Wei Wang, Lijun Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid's physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q (3) and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q (3) and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional metho

关键词： Intelligent prediction system protein secondary structure prediction Hybrid BP neural network Compound pyramid model

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