Dataset Information

Prediction of solvent accessibility and sites of deleterious mutations from protein sequence.

ABSTRACT: Residues that form the hydrophobic core of a protein are critical for its stability. A number of approaches have been developed to classify residues as buried or exposed. In order to optimize the classification, we have refined a suite of five methods over a large dataset and proposed a metamethod based on an ensemble average of the individual methods, leading to a two-state classification accuracy of 80%. Many studies have suggested that hydrophobic core residues are likely sites of deleterious mutations, so we wanted to see to what extent these sites can be predicted from the putative buried residues. Residues that were most confidently classified as buried were proposed as sites of deleterious mutations. This proposition was tested on six proteins for which sites of deleterious mutations have previously been identified by stability measurement or functional assay. Of the total of 130 residues predicted as sites of deleterious mutations, 104 (or 80%) were correct.

SUBMITTER: Chen H

PROVIDER: S-EPMC1142490 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of solvent accessibility and sites of deleterious mutations from protein sequence.

Chen Huiling H Zhou Huan-Xiang HX

Nucleic acids research 20050603 10

Residues that form the hydrophobic core of a protein are critical for its stability. A number of approaches have been developed to classify residues as buried or exposed. In order to optimize the classification, we have refined a suite of five methods over a large dataset and proposed a metamethod based on an ensemble average of the individual methods, leading to a two-state classification accuracy of 80%. Many studies have suggested that hydrophobic core residues are likely sites of deleterious ...[more]

PMID: 15937195

Similar Datasets

Project description:BACKGROUND: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. RESULTS: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. CONCLUSION: We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

Dataset Information

Prediction of solvent accessibility and sites of deleterious mutations from protein sequence.

Publications

Prediction of solvent accessibility and sites of deleterious mutations from protein sequence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets