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SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs.

Charoenkwan P, Shoombuatong W, Lee HC, Chaijaruwanich J, Huang HL, Ho SY - PLoS ONE (2013)

Bottom Line: The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%).The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively.The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.

ABSTRACT
Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

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Heat map of the propensity scores of dipeptides obtained from the SCM method.
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pone-0072368-g001: Heat map of the propensity scores of dipeptides obtained from the SCM method.

Mentions: Due to the non-deterministic characteristic of genetic algorithms which use randomicity mechanism resulting in non-constant results, 10 independent runs were conducted to generate 10 SCM classifiers for each value of p where p = 0, 1, …, 9. The mean performances of SCM using the p-collocated AA pairs are shown in Table 3. The best SCM classifier is the one using dipeptide composition (p = 0) that the test performance is 73.90±0.57%, MCC  = 0.38±0.02, Sensitivity  = 0.45±0.03, and Specificity  = 0.88±0.01. The optimization stage improves SCM with dipeptide composition that the test accuracy increases from 71.47% to 73.90%, and the MCC value increases from 0.30 to 0.38. In the following analysis, the propensity scores of dipeptides obtained from the best result of SCM are adopted, as shown in Figure 1.


SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs.

Charoenkwan P, Shoombuatong W, Lee HC, Chaijaruwanich J, Huang HL, Ho SY - PLoS ONE (2013)

Heat map of the propensity scores of dipeptides obtained from the SCM method.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3760885&req=5

pone-0072368-g001: Heat map of the propensity scores of dipeptides obtained from the SCM method.
Mentions: Due to the non-deterministic characteristic of genetic algorithms which use randomicity mechanism resulting in non-constant results, 10 independent runs were conducted to generate 10 SCM classifiers for each value of p where p = 0, 1, …, 9. The mean performances of SCM using the p-collocated AA pairs are shown in Table 3. The best SCM classifier is the one using dipeptide composition (p = 0) that the test performance is 73.90±0.57%, MCC  = 0.38±0.02, Sensitivity  = 0.45±0.03, and Specificity  = 0.88±0.01. The optimization stage improves SCM with dipeptide composition that the test accuracy increases from 71.47% to 73.90%, and the MCC value increases from 0.30 to 0.38. In the following analysis, the propensity scores of dipeptides obtained from the best result of SCM are adopted, as shown in Figure 1.

Bottom Line: The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%).The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively.The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.

ABSTRACT
Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p=0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

Show MeSH
Related in: MedlinePlus