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Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.

ABSTRACT: Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.

SUBMITTER: Tsai KC

PROVIDER: S-EPMC3405063 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.

Tsai Keng-Chang KC Jian Jhih-Wei JW Yang Ei-Wen EW Hsu Po-Chiang PC Peng Hung-Pin HP Chen Ching-Tai CT Chen Jun-Bo JB Chang Jeng-Yih JY Hsu Wen-Lian WL Yang An-Suei AS

PloS one 20120725 7

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we s ...[more]

PMID: 22848404

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Project description:Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Project description:Abasic (AP) sites in DNA arise through both endogenous and exogenous mechanisms. Since AP sites can prevent replication and transcription, the cell contains systems for their identification and repair. AP endonuclease (APEX1) cleaves the phosphodiester backbone 5' to the AP site. The cleavage, a key step in the base excision repair pathway, is followed by nucleotide insertion and removal of the downstream deoxyribose moiety, performed most often by DNA polymerase beta (pol-beta). While yeast two-hybrid studies and electrophoretic mobility shift assays provide evidence for interaction of APEX1 and pol-beta, the specifics remain obscure. We describe a theoretical study designed to predict detailed interacting surfaces between APEX1 and pol-beta based on published co-crystal structures of each enzyme bound to DNA. Several potentially interacting complexes were identified by sliding the protein molecules along DNA: two with pol-beta located downstream of APEX1 (3' to the damaged site) and three with pol-beta located upstream of APEX1 (5' to the damaged site). Molecular dynamics (MD) simulations, ensuring geometrical complementarity of interfaces, enabled us to predict interacting residues and calculate binding energies, which in two cases were sufficient (approximately -10.0 kcal/mol) to form a stable complex and in one case a weakly interacting complex. Analysis of interface behavior during MD simulation and visual inspection of interfaces allowed us to conclude that complexes with pol-beta at the 3'-side of APEX1 are those most likely to occur in vivo. Additional multiple sequence analyses of APEX1 and pol-beta in related organisms identified a set of correlated mutations of specific residues at the predicted interfaces. Based on these results, we propose that pol-beta in the open or closed conformation interacts and makes a stable interface with APEX1 bound to a cleaved abasic site on the 3' side. The method described here can be used for analysis in any DNA-metabolizing pathway where weak interactions are the principal mode of cross-talk among participants and co-crystal structures of the individual components are available.

Dataset Information

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.

Publications

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.

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