The Prediction of the Structural Class of Protein: Application of the Measure of Diversity

Li, Qian‐Zhong; Zhi-qing, LU

doi:10.1006/jtbi.2001.2441

Cited by 62 publications

(18 citation statements)

References 17 publications

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“…Increment of diversity (ID) algorithm was a classifier which has been successfully used in identification of protein folds and complex secondary structures in recent years (Laxton, 1978;Li and Lu, 2001;Hu et al, 2010). In the state space of s dimension, the measure of diversity source X{n 1 , n 2 ,…, n s } was:…”

Section: Increment Of Diversity (Id) Valuementioning

confidence: 99%

Identification of Ca2+-binding residues of a protein from its primary sequence

Jiang

Geriletu

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Calcium is one of the most abundant minerals in the human body, playing a critical role in many cellular activities by interacting with different calcium ion (Ca 2+ )-binding proteins. Therefore, the correct identification of Ca 2+ -binding residues is essential for protein functional research. In this study, a new method was developed to predict Ca 2+ -binding residues from the primary sequence without using three-dimensional information. Through statistical analysis, four kinds of feature parameters were extracted from amino acid sequences: the increment of diversity values of amino acid composition, the matrix scoring values of position conservation, the autocross covariance of physicochemical properties, and the center motif. These features served as input for a support vector machine to predict Ca 2+ -binding residues. This method was tested on four well-established datasets using a five-fold cross-validation. The accuracies and Matthews correlation coefficients were 75.9% and 0.53 (dataset 1), 79.2% and 0.58 (dataset 2), 77.4% and 0.55 (dataset 3), and 79.1% and 0.58 (dataset 4). Comparative results show that the developed method outperforms previous methods. Based on this study, a web server was developed for predicting Ca 2+ -binding

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Section: Increment Of Diversity (Id) Valuementioning

confidence: 99%

Identification of Ca2+-binding residues of a protein from its primary sequence

Jiang

Geriletu

et al. 2016

Genet. Mol. Res.

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“…Primary structure homology has been used for this purpose and is undoubtedly one of the most useful methods (Altschul et al, 1990). Without primary sequence homology, function can be annotated from secondary or tertiary homology with known proteins (Bhaduri et al, 2004;Jung and Lee, 2004;Li and Lu, 2001;Meiler et al, 2000;Jones, 1999a,b;Rychlewski et al, 1999;Thompson and Goldstein, 1997;Sander and Schneider, 1991;Kabsch and Sander, 1983). Methods to find secondary and tertiary structure homology are continuing to be developed and become more reliable when based on, or associated to, experimental data.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the amount of secondary structure of proteins using unassigned NMR spectra: a tool for target selection in structural proteomics

2006

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With the advent of structural genomics, the need for fast structural information about unknown proteins has increased. We describe a new methodology, based on 13 C, 15 N and 1 H chemical shift dispersion to predict the amount of secondary structure of unassigned proteins from their 15 N-and/or 13 C-edited heteronuclear single quantum coherence (HSQC) spectra. This methodology has been coded into a software called PASSNMR (Prediction of the Amount of Secondary Structure by Nuclear Magnetic Resonance), which can be accessed directly from the Internet. PASSNMR program is a powerful tool for screening proteins for proteomic or structural genomic investigations when used with recent methodologies that take advantage of the use of the antibiotic rifampicin to selectively label the heterologous proteins expressed in E. coli. PASSNMR analysis can be useful as a first approach to predict the amount of secondary structure in proteins to structural genomics. Information about the secondary structure of proteins can be obtained even before protein purification, with small quantities of protein, just by performing two simple nuclear magnetic resonance (NMR) experiments and using PASSNMR program.

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“…The increment of diversity [18] (ID) is an effective identification algorithm. In this work, the value of ID was used for characteristic parameters.…”

Section: Correlation Information Of Amino Acids (G)mentioning

confidence: 99%

Identifying the Sulfate Ion Binding Residues in Proteins

Li¹,

Hu²,

Sun³

et al. 2017

Proceedings of the 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)

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Abstract. Many proteins function execution depends on the process of protein and ligand interact with each other. The identification of ligand binding residues is important for the research of the protein function. The 4442 protein chains with <25% sequence identity and resolution <3.0 Å were analyzed using Ligand Protein Contact database. Our final dataset contained 8112 sulfate ion binding residues (SIBR). We did a statistical analysis on window size as 7 amino acids. Using the amino acid composition, hydropathy information, correlation information and predicted structure information as the characteristic parameter, a Support Vector Machine algorithm for identifying sulphate ion binding residues was proposed. The overall accuracy and Matthew's correlation coefficient achieved 78.5% and 0.571 using the 5 fold cross validation. The Acc and MCC achieved 72.7% and 0.455 by using independent test. In addition, an online web server was established. http: //202.207.30.72:7321/

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The Prediction of the Structural Class of Protein: Application of the Measure of Diversity

Cited by 62 publications

References 17 publications

Identification of Ca2+-binding residues of a protein from its primary sequence

Identification of Ca2+-binding residues of a protein from its primary sequence

Prediction of the amount of secondary structure of proteins using unassigned NMR spectra: a tool for target selection in structural proteomics

Identifying the Sulfate Ion Binding Residues in Proteins

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