Classifier ensembles for protein structural class prediction with varying homology

Kedarisetti, Kanaka Durga; Kurgan, Lukasz; Dick, Scott

doi:10.1016/j.bbrc.2006.07.141

Cited by 161 publications

(101 citation statements)

References 44 publications

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“…Finally, a simple voting based meta-predictor is shown to provide some improvements although more complex designs should be considered to better exploit complementarity between the existing prediction methods. Such advanced heterogeneous (using diverse types of member methods) meta-predictors were already successfully used in sequence-based prediction of other protein properties such as fold type [76,77], subcellular localization [78][79][80], structural class [81], and solvent accessibility [82].…”

Section: Complementarity Of Existing Methodsmentioning

confidence: 99%

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce diffraction quality crystals for X-ray crystallography based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of insilico methods for sequence-based prediction/ assessment of the protein crystallization propensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discovered to correlate with the ability to form crystals. We also contrast and empirically compare modern sequence-based predictors of crystallization propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and complimentary predictions. Although their average accuracy is similar at around 70%, we show that application of a simple majority-vote based ensemble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystallization propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

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Section: Complementarity Of Existing Methodsmentioning

confidence: 99%

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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“…the "Molecular weight", (the use of the molecular weight in the clustering procedure is motivated by (Homaeian et al 2007, Kedarisetti et al 2006. Column c3 the "Hydrophobicity".…”

Section: Resultsmentioning

confidence: 99%

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

Georgiou

Karakasidis

Nieto

et al. 2009

Journal of Theoretical Biology

184

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“…For convenience, the PSSM is denoted as is to feed these features to an appropriate classification algorithm to efficiently and accurately predict structural class. Up to now, a lot of machine-learning algorithms have been proposed, such as neural network [21], support vector machine (SVM) [22][23][24][25], fuzzy clustering [26], fuzzy k-nearest neighbor [27,28], Bayesian classification [29], logistic regression [30], rough sets [31] and classifier fusion techniques [32][33][34][35][36]. Among the aforementioned classification algorithms, SVM is the most reliable and attained excellent performance on the SCOP problem [19].…”

Section: Position-specific Scoring Matrixmentioning

confidence: 99%

Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles

et al. 2015

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Knowledge of protein structure plays a key role in the analysis of protein functions, protein binding, rational drug design, and many other related fields and applications. In this study, a novel feature extraction model based on linear predictive coding (LPC) and position-specific score matrices (PSSM) was proposed to predict structural class from protein sequences. First, the PSI-BLAST tool was employed to transform the original protein sequences into PSSMs. Then, the LPC, a signal processing tool, was applied to extract the features from PSSMs. The selected features were finally fed to a support vector machine to perform the prediction. Cross-validation tests on the four benchmark datasets Z277, Z498, 1189 and 25PDB, showed a significant leap in overall accuracy using the proposed method. Compared to existing methods, our method achieved better performance in prediction of protein structural class.

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Classifier ensembles for protein structural class prediction with varying homology

Cited by 161 publications

References 44 publications

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles

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