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Protein disorder prediction by condensed PSSM considering propensity for order or disorder - PubMed

  • ️Sun Jan 01 2006

Comparative Study

Protein disorder prediction by condensed PSSM considering propensity for order or disorder

Chung-Tsai Su et al. BMC Bioinformatics. 2006.

Abstract

Background: More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.

Results: This paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties.

Conclusion: Distinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.

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Figures

Figure 1
Figure 1

The relation of the selected properties after the first level of redundancy analysis.

Figure 2
Figure 2

Comparison of using different feature sets on testing data R80.

Figure 3
Figure 3

Comparison of using different feature sets on testing data U79 and P80.

Figure 4
Figure 4

Comparison of using different feature sets on testing data R80, U79, and P80.

Figure 5
Figure 5

Comparing the performance of thirteen disorder prediction packages on testing data R80.

Figure 6
Figure 6

Comparing the performance of thirteen disorder prediction packages on testing data U79 and P80.

Figure 7
Figure 7

Comparing the performance of thirteen disorder prediction packages on testing data R80, U79, and P80.

Figure 8
Figure 8

The procedure of preparing feature set for training and testing data.

Figure 9
Figure 9

The propensity for order and the frequency of each amino acid in the training set.

Figure 10
Figure 10

The flowchart of the hybrid feature selection mechanism.

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References

    1. Dunker AK, Obradovic Z, Romero P, Kissinger C, Villafrance E. On the importance of being disordered. PDB Newsletter. 1997;81:3–5.
    1. Plaxco KW, Gross M, Cell biology The importance of being unfolded. Nature. 1997;386:657–659. doi: 10.1038/386657a0. - DOI - PubMed
    1. Wright PE, Dyson HJ. Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol. 1999;293:321–331. doi: 10.1006/jmbi.1999.3110. - DOI - PubMed
    1. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol. 2004;337:635–645. doi: 10.1016/j.jmb.2004.02.002. - DOI - PubMed
    1. Lise S, Jones DT. Sequence patterns associated with disordered regions in proteins. Proteins. 2005;58:144–150. doi: 10.1002/prot.20279. - DOI - PubMed

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