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Yeast POL5 Is an Evolutionarily Conserved Regulator of rDNA Transcription Unrelated to Any Known DNA Polymerases

CELL CYCLE 2003年第2期2卷 120-122页

作者： Yang, Wei Rogozin, Igor B. Koonin, Eugene V. Natl Lib Med Natl Ctr Biotechnol Informat NIH 8600 Rockville PikeBldg 38A Bethesda MD 20894 USA NIDDK Mol Biol Lab NIH Bethesda MD 20892 USA

We show that yeast protein Yel055cp, which has been identified as the fifth essential DNA polymerase in Saccharomyces cerevisiae (POL5), is a member of a family of predicted rDNA transcription regulators (typified by human MYB-binding protein MYBBP1A), which are represented by a single ortholog in all animals, fungi and plants with sequenced genomes. These proteins are confidently predicted to have an entirely a-helical structure and are unrelated to the B class DNA polymerases, as claimed for yeast POL5, or any other known polymerases.

关键词： DNA polymerase Transcription regulation MYB-binding proteins Conserved sequence motifs Multiple alignment protein secondary structure prediction

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Predicting protein secondary structure and solvent accessibility with an improved multiple linear regression method

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proteinS-structure FUNCTION AND BIOINFORMATICS 2005年第3期61卷 473-480页

作者： Qin, SB He, Y Pan, XM Tsinghua Univ Dept Biol Sci & Biotechnol Beijing 100084 Peoples R China Chinese Acad Sci Natl Lab Biomacromol Inst Biophys Beijing 100864 Peoples R China Chinese Acad Sci Grad Sch Beijing 100864 Peoples R China

We have improved the multiple linear regression (MLR) algorithm for protein secondary structure prediction by combining it with the evolutionary information provided by multiple sequence alignment of PSI-BLAST. On the CB513 dataset, the three states average overall per-residue accuracy, Q(3), reached 76.4%, while segment overlap accuracy, SOV99, reached 73.2%, using a rigorous jackknife procedure and the strictest reduction of eight states DSSP definition to three states. This represents an improvement of approximately 5% on overall per-residue accuracy compared with previous work. The relative solvent accessibility prediction also benefited from this combination of methods. The system achieved 77.7% average jackknifed accuracy for two states prediction based on a 25% relative solvent accessibility mode, with a Mathews' correlation coefficient of 0.548. The improved MLR secondary structure and relative solvent accessibility prediction server is available at http://***. ***/. proteins 2005;61:473-480. (c) 2005 Wiley-Liss, Inc.

关键词： protein secondary structure prediction solvent accessibility prediction multiple linear regression protein folding PSSM

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Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins

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proteinS-structure FUNCTION AND BIOINFORMATICS 2004年第2期54卷 282-288页

作者： Kuhn, M Meiler, J Baker, D Univ Washington Dept Biochem Seattle WA 98195 USA CALTECH Pasadena CA 91125 USA

beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the protein Data Bank and achieves a prediction accuracy of 75.9+/-4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3+/-6.1%, diverging turns with an accuracy of 73.9+/-6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003;***/***).

关键词： artificial neural network protein secondary structure prediction beta-hairpin ROSETTA protein tertiary structure prediction fragment replacement

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Molecular characterization of a mosaic locus in the genome of 'Candidatus Liberibacter asiaticus'

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BMC Microbiology 2012年第1期12卷 1-7页

作者： Wang, Xuefeng Zhou, Changyong Deng, Xiaoling Su, Huanan Chen, Jianchi National Engineering Research Center for Citrus Citrus Research Institute Southwest University Chongqing 400712 China Citrus Huanglongbing Research Center South China Agricultural University Guangzhou 510642 China San Joaquin Valley Agricultural Sciences Center United States Department Agriculture-Agricultural Research Service Parlier CA 93648 United States

Background. Huanglongbing (HLB) is a highly destructive disease of citrus production worldwide. 'Candidatus Liberibacter asiaticus', an unculturable alpha proteobacterium, is a putative pathogen of HLB. Information about the biology and strain diversity of 'Ca. L. asiaticus' is currently limited, inhibiting the scope of HLB research and control. Results. A genomic region (CLIBASIA-05640 to CLIBASIA-05650) of 'Ca. L. asiaticus' showing hyper-sequence variation or locus mosaicism was identified and investigated using 262 bacterial strains (188 from China and 74 from Florida). Based on the characteristic electrophoretic profiles of PCR amplicons generated by a specific primer set, eight electrophoretic types (E-types) were identified, six E-types (A, B, C, D, E, and F) in China and four E-types (A, C, G, and H) in Florida. The 'Ca. L. asiaticus' strains from China consisted predominately of E-type A (71.3%) and E-type B (19.7%). In contrast, the 'Ca. L. asiaticus' strains from Florida was predominated by E-type G (82.4%). Diversity of 'Ca. L. asiaticus' in China was also evidenced. Strains from the high altitude Yunnan Province consisted of five E-types with E-type B being the majority (62.8%), whereas strains from the low altitude coastal Guangdong Province consisted of only two E-types with E-type A as the majority (97.0%). Sequence analyses revealed that variation of DNA amplicons was due to insertion/deletion events at CLIBASIA-05650 and the downstream intergenic region. Conclusions. This study demonstrated the genomic mosaicism of 'Ca. L. asiaticus' resulted from active DNA insertion/deletion activities. Analyses of strain variation depicted the significant inter- and intra-continent diversity of 'Ca. L. asiaticus'. © 2012 Wang et al;licensee BioMed Central Ltd.

关键词： Genomic Locus High Altitude Region protein secondary structure prediction Tandem Repeat Locus Prophage Gene

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A two-stage classifier for protein β-turn prediction using support vector machines

A two-stage classifier for protein β-turn prediction using ...

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IEEE International Conference on Granular Computing

作者： Chiu, Hua-Sheng Lin, Hsin-Nan Lo, Allan Sung, Ting-Yi Hsu, Wen-Lian Acad Sinica Inst Informat Sci Bioinformat Lab Taipei 115 Taiwan

ISBN: (纸本)142440133X

beta-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of beta-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demonstrate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Q(total), and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respectively for BetaTurn.

关键词： beta-turn prediction classification protein secondary structure prediction support vector machines

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Nonlinear system identification provides insight into protein folding

Nonlinear system identification provides insight into protei...

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19th IEEE Canadian Conference on Electrical and Computer Engineering

作者： Green, James R. Korenberg, Michael J. Carleton Univ Syst & Comp Engn Ottawa ON K1S 5B6 Canada Queens Univ Elect & Dept Comp Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781424400379

Much like the shape of a tool suggests its intended purpose, knowledge of a protein's structure can provide substantial insight into its function. Therefore, computational prediction of protein structure based solely on protein sequence data is a challenge of fundamental importance to biomedical research. An effective solution promises significant advances in computational drug discovery and an increased understanding of complex disease processes such as cancer. We have recently developed a novel approach to determining the secondary structure of proteins from protein sequence data which makes use of Parallel Cascade Identification (M), a powerful method of nonlinear system identification. PCI is used to create two layers of dynamic nonlinear systems that map divergent evolutionary profile input data into secondary structure assignment output data. PCI prediction accuracy compares well with eleven top contemporary methods over a dataset of new protein structures. Furthermore, PCI is a highly effective means to combine multiple experts achieving the highest observed accuracy over two test datasets and also the lowest rate of occurrence of a particularly detrimental class of errors. One limitation of the PCI classifiers is that approximately 13% of all amino acids cannot readily be assigned predictions due to settling times introduced by the dynamic linear component in each cascade model. In this paper we describe a number of methods designed to overcome this limitation. While zero-padding of the input sequence data proved to be the most effective solution in terms of prediction accuracy, an analysis of causal, anti-causal, and mixed cascades provides interesting insights into the biological mechanism of protein folding.

关键词： protein secondary structure prediction nonlinear system identification parallel cascade identification bioinformatics

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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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PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State protein secondary structure

PS8-Net: A Deep Convolutional Neural Network to Predict the ...

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Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Ratul, Aminur Rab Elahi, Maryam Tavakol Mozaffari, M. Hamed Lee, WonSook Univ Ottawa Sch Elect Engn & Comp Sci SITE Ottawa ON Canada

ISBN: (纸本)9781728191089

protein secondary structure is crucial to creating an information bridge between the primary and tertiary structures. Precise prediction of eight-state protein secondary structure (PSS) has been significantly utilized in the structural and functional analysis of proteins. Deep learning techniques have been recently applied in this area and raised the eight-state (Q8) protein secondary structure prediction accuracy remarkably. Nevertheless, from a theoretical standpoint, there are still many rooms for improvement, specifically in the eight-state PSS prediction. In this study, we have presented a new deep convolutional neural network called PS8-Net, to enhance the accuracy of eight-class PSS prediction. The input of this architecture is a carefully constructed feature matrix from the proteins sequence features and profile features. We introduce a new PS8 module with skip connection to extracting the long-term inter-dependencies from higher layers, obtaining local contexts in earlier layers, and achieving global information during secondary structure prediction. This architecture enables the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class. To the best of our knowledge, our proposed PS8-Net experiment results demonstrate that it outperforms all the state-of-the-art methods on the benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets.

关键词： Deep Convolutional Neural Network protein secondary structure prediction Bioinformatics Skip Connection

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An Improved CBA prediction Algorithm in Compound Pyramid Model

An Improved CBA Prediction Algorithm in Compound Pyramid Mod...

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21st Chinese Control and Decision Conference

作者： Zhou Zhun Yang Bingru Hou Wei Univ Sci & Technol Beijing Informat Engn Sch Beijing 100083 Peoples R China

ISBN: (纸本)9781424427239

As one of KDTICM[8] theory researches, this paper propose an improved algorithm -- CBA, which is based on KDD* model and combined with KAAPRO method, for protein secondary structure prediction problem. Further, multi-layer systematic prediction model--Compound Pyramid Model, is proposed. The kernel of this model is CBA which is a classic association rules analysis algorithm. Domain knowledge is used through the model, and the phy-chemical attributes is chosen by Causal Cellular Automation. In experiment, the proteins bias alpha/beta structure are precisely predicted. The structures of amino acids, whose structure are obscure, are predicted well by the improved CBA. Finally, the result of this model is satisfied.

关键词： Association Rule protein secondary structure prediction Compound Pyramid Model CBA algorithm

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Optimized Local protein structure with Support Vector Machine to Predict protein secondary structure

Optimized Local Protein Structure with Support Vector Machin...

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3rd Conference of Knowledge Technology Week (KTW 2011)

作者： Chin, Yin Fai Hassan, Rohayanti Mohamad, Mohd Saberi Univ Teknologi Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Sci & Informat Syst Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642328251

protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. Therefore, determination of protein structure is fundamental for the understanding of the cell's functions. The function of a protein is also largely determined by its structure. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. Computational methods which were able to predict protein structure for the determination of protein function efficiently and accurately are in high demand. In this study, local protein structure with Support Vector Machine is proposed to predict protein secondary structure.

关键词： Local protein structure Support Vector Machine protein secondary structure prediction

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