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Application of protein grey incidence degree measure to predict protein quaternary structural types

AMINO ACIDS 2009年第4期37卷 741-749页

作者： Xiao, Xuan Lin, Wei-Zhong Jing De Zhen Ceram Inst Dept Comp Jing De Zhen 333001 Peoples R China

Many proteins are composed of two or more subunits, each associated with different polypeptide chains. The number and arrangement of subunits forming a protein are referred to as quaternary structure. It has been known for long that the functions of proteins are closely related to their quaternary structure. In this paper the grey incidence degree is introduced that can calculate the numerical relation between various components, expressed the similar or different degree between these components. We have demonstrated that introduction of the grey incidence degree can remarkably enhance the success rates in predicting the protein quaternary structural class. It is anticipated that the grey incidence degree can be also used to predict many other protein attributes, such as subcellular localization, membrane protein type, enzyme functional class, GPCR type, protease type, among many others.

关键词： Protein sequence distance measure Grey system Grey incidence degree Protein quaternary structural type nearest neighbor algorithm

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Protein functional class prediction using global encoding of amino acid sequence

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JOURNAL OF THEORETICAL BIOLOGY 2009年第2期261卷 290-293页

作者： Li, Xi Liao, Bo Shu, Yu Zeng, Qingguang Luo, Jiawei Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Sch Langshan Shaoyang 422000 Hunan Peoples R China

A key goal of the post-genomic era is to determine protein functions. In this paper, we proposed a global encoding method of protein sequence (GE) to descript global information of amino acid sequence, and then assign protein functional class using machine learning methods nearest neighbor algorithm (NNA). We predicted the function of 1818 Saccharomyces cerevisiae proteins which was used in Vazquez's global optimization method (GOM) except eight proteins which cannot get from the data base now or whose sequence length is too short. Using our approach, the computed accuracy is better than Vazquez's global optimization method (GOM) in some cases. The experiment results show that our new method is efficient to predict functional class of unknown proteins. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Protein functional class prediction Global encoding nearest neighbor algorithm Physiochemical property

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A Novel Computational Approach To Predict Transcription Factor DNA Binding Preference

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JOURNAL OF PROTEOME RESEARCH 2009年第2期8卷 999-1003页

作者： Cai, Yudong He, JianFeng Li, XinLei Lu, Lin Yang, XinYi Feng, KayYan Lu, WenCong Kong, XiangYin Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai 200031 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200040 Peoples R China Shanghai Jiao Tong Univ Inst Hlth Sci Shanghai 200025 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Shanghai 200025 Peoples R China Univ Manchester Div Imaging Sci & Biomed Engn Manchester M13 9PT Lancs England Shanghai Univ Coll Sci Dept Chem Lab Chem Data Min Shanghai 200444 Peoples R China

Transcription is one of the most important processes in cell in which transcription factors translate DNA sequences into RNA sequences. Accurate prediction of DNA binding preference of transcription factors is valuable for understanding the transcription regulatory mechanism and(1) elucidating regulation network.(2-4) Here we predict the DNA binding preference of transcription factor based on the protein amino acid composition and physicochemical properties, 0/1 encoding system of nucleotide, minimum Redundancy Maximum Relevance Feature Selection method,(5) and nearest neighbor algorithm. The overall prediction accuracy of Jackknife cross-validation test is 91.1%, indicating that this approach is a useful tool to explore the relation between transcription factor and its binding sites. Moreover, we find that the secondary structure and polarizability of transcriptor contribute mostly in the prediction. Especially, a 7-nt motif with AT-rich region of the DNA binding sites discovered via our method is also consistent with the statistical analysis from the TRANSFAC database.(6)

关键词： Transcription factor Transcription factor DNA binding preference mRMR nearest neighbor algorithm 0/1 System Jackknife cross-validation test

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On determining how many computers to use in parallel VLSI simulation 09

On determining how many computers to use in parallel VLSI si...

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23rd International Workshop on Principles of Advanced and Distributed Simulation

作者： Xu, Qing Tropper, Carl McGill Univ Sch Comp Sci Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9780769537139

Parallel discrete event simulation has been established as a technique which has great potential to speed up the execution of gate level circuit simulation. A fundamental problem posed by a parallel environment is the decision of whether it is best to simulate a particular circuit sequentially or on a parallel platform. Furthermore, in the event that a circuit should be simulated on a parallel platform, it is necessary to decide how many computing nodes should be used on the given platform. In this paper we propose a machine learning algorithm as an aid in making these decisions. The algorithm is based on the well-known K-nearest neighbor algorithm. After an extensive training regime, it was shown to make a correct prediction 99% of the time on whether to use a parallel or sequential simulator. The predicted number of nodes to use on a parallel platform was shown to produce an average execution time which was not more than 12% of the smallest execution time. The configuration which resulted in the minimal execution time was picked 61 % of the time.

关键词： execution time Parallel Lines Machine learning algorithms discrete event simulation Platform Circuit simulation simulating Ganglia Very large scale integration Computers nearest neighbor algorithm Legal Executions

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Improvement of Indoor Location Sensing algorithm Using Wireless Local Area Network (WLAN) IEEE 802.11b

Improvement of Indoor Location Sensing Algorithm Using Wirel...

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IEEE Malaysia International Conference on Communications

作者： Chan Weng Yuen Nagaletchumi Balasubramaniam Norashidah Md. Din Center for Communications Service Convergence Technologies College of Engineering Universiti Tenaga Nasional

ISBN: (纸本)9781424455317;9781424455324

This paper proposes to improve the performance of the K-nearest Neighbour algorithm in predicting the location of a mobile user in an indoor environment. Emphasis is placed on RSS sample vector fluctuation stabilization, resulting in an overall 12% increase in the precision of predicting correct locations compared to previous efforts.

关键词： Location Awareness WLAN IEEE 802.11b K-nearest Neighbour Location awareness wireless local area network (WLAN) nearest neighbor algorithm INSTITUTE OF ELECTRICAL AND ELECTRONIC ENGINEERS

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Prediction of compounds' biological function (metabolic pathways) based on functional group composition

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MOLECULAR DIVERSITY 2008年第2期12卷 131-137页

作者： Cai, Yu-Dong Qian, Ziliang Lu, Lin Feng, Kai-Yan Meng, Xin Niu, Bing Zhao, Guo-Dong Lu, Wen-Cong Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai 200072 Peoples R China Univ Manchester Inst Sci & Technol Dept Math Manchester M60 1QD Lancs England Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Chinese Acad Sci Bioinformat Ctr Key Lab Mol Syst Biol Shanghai Inst Biol Sci Shanghai 200031 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200240 Peoples R China Shanghai Univ Sch Mat Sci & Engn Shanghai 200072 Peoples R China

Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (nearest neighbor algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.

关键词： compound biological functions nearest neighbor algorithm functional group composition metabolic pathway

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Prediction of protein structural classes using hybrid properties

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MOLECULAR DIVERSITY 2008年第3-4期12卷 171-179页

作者： Li, Wenjin Lin, Kao Feng, Kaiyan Cai, Yudong Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai Peoples R China Univ Manchester Div Imaging Sci & Biomed Engn Manchester M13 9PT Lancs England

In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://***:8080/liwenjin/***.

关键词： Protein structural class nearest neighbor algorithm mRMR (Minimum Redundancy, Maximum Relevance) Physiochemical properties Amino acid compositions

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Prediction of membrane protein types in a hybrid space

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JOURNAL OF PROTEOME RESEARCH 2008年第3期7卷 1131-1137页

作者： Jia, Peilin Qian, Ziliang Feng, Kaiyan Lu, Wencong Li, Yixue Cai, Yudong Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Mol Syst Biol Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Chinese Acad Sci CAS MPG Partner Inst Computat Biol Shanghai Inst Biol Sci Beijing 100864 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Univ Manchester Div Imaging Sci & Biomed Engn Manchester M13 9PT Lancs England Shanghai Jiao Tong Univ Coll Life Sci & Biotechnol Shanghai Peoples R China Shanghai Univ Dept Chem Coll Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Mat Sci & Engn Shanghai 200444 Peoples R China

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins' functions. We describe a new strategy for the prediction of the types of membrane proteins using the nearest neighbor algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins' domain profile and proteins' physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called "mRMR" (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226-1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://***/***.

关键词： membrane protein nearest neighbor algorithm feature selection

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Predicting N-terminal acetylation based on feature selection method

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2008年第4期372卷 862-865页

作者： Cai, Yu-Dong Lu, Lin Chinese Acad Sci Dept Combinator & Geometry MPG Partner Inst Computat Biol Shanghai Inst Biol Sci Shanghai 200031 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200040 Peoples R China Univ Manchester Dept Math Manchester M60 1QD Lancs England

Methionine aminopeptidase and N-terminal acetyltransferase are two enzymes that contribute most to the N-terminal acetylation, which has long been recognized as a frequent and important kind of co-translational modifications [R.A. Bradshaw, W.W. Brickey, K.W. Walker, N-terminal processing: the methionine aminopeptidase and N alpha-acetyl transferase families, Trends Biochem. Sci. 23 (1998) 263267]. The combined action of these two enzymes leads to two types of N-terminal acetylated proteins that are with/without the initiator methionine after the N-terminal acetylation. To accurately predict these two types of N-terminal acetylation, a new method based on feature selection has been developed. 1047 N-terminal acetylated and non-acetylated decapeptides retrieved from Swiss-Prot database (http://***) are encoded into feature vectors by amino acid properties collected in Amino Acid Index database (http://***/aaindex). The Maximum Relevance Minimum Redundancy method (mRMR) combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) is then applied to extract informative features. nearest neighbor algorithm (NNA) is used to build prediction models. Tested by jackknife Cross-Validation, the correct rate of predictors reach 91.34% and 75.49% for each type, which are both better than that of 84.41% and 62.99% acquired by using motif methods [S. Huang, R.C. Elliott, P.S. Liu, R.K. Koduri, J.L. Weickmann, J.H. Lee, L.C. Blair, P. Ghosh-Dastidar, R.A. Bradshaw, K.M. Bryan, et al., Specificity of cotranslational amino-terminal processing of proteins in yeast, Biochemistry 26 (1987) 8242-8246: R. Yamada, R.A. Bradshaw, Rat liver polysome N alpha-acetyltransferase: substrate specificity, Biochemistry 30 (1991) 1017-1021]. Furthermore, the analysis of the informative features indicates that at least six downstream residues might have effect on the rules that guide the N-terminal acetylation, besides the penultimate residue. The softwar

关键词： N-terminal acetylation co-translational modification Maximum Relevance Minimum Redundancy method Amino acid index database nearest neighbor algorithm

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Prediction of subcellular protein localization based on functional domain composition

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2007年第2期357卷 366-370页

作者： Jia, Peilin Qian, Ziliang Zeng, ZhenBin Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci MPG Partner Inst Computat Biol Shanghai Peoples R China Univ Manchester Dept Math Manchester M60 1QD Lancs England Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Shanghai Jiao Tong Univ Life Sci Sch Shanghai 200030 Peoples R China E China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China

Assigning subcellular localization (SL) to proteins is one of the major tasks of functional proteomics. Despite the impressive technical advances of the past decades, it is still time-consuming and laborious to experimentally determine SL on a high throughput scale. Thus, computational predictions are the preferred method for large-scale assignment of protein SL, and if appropriate, followed up by experimental studies. In this report, using a machine learning approach, the nearest neighbor algorithm (NNA), we developed a prediction system for protein SL in which we incorporated a protein functional domain profile. The overall accuracy achieved by this system is 93.96%. Furthermore, comparisons with other methods have been conducted to demonstrate the validity and efficiency of our prediction system. We also provide an implementation of our Subcellular Location Prediction System (SLPS), which is available at http://***. (c) 2007 Elsevier Inc. All rights reserved.

关键词： protein subcellular localization pfam nearest neighbor algorithm

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