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

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.

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface.

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.

N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites.

Protein acetylation, which is catalyzed by acetyltransferases, is a type of post-translational modification and crucial to numerous essential biological processes, including transcriptional regulation, apoptosis, and cytokine signaling. As the experimental identification of protein acetylation sites is time consuming and laboratory intensive, several computational approaches have been developed for identifying the candidates of experimental validation. In this work, solvent accessibility and the physicochemical properties of proteins are utilized to identify acetylated alanine, glycine, lysine, methionine, serine, and threonine. A two-stage support vector machine was applied to learn the computational models with combinations of amino acid sequences, and the accessible surface area and physicochemical properties of proteins. The predictive accuracy thus achieved is 5% to 14% higher than that of models trained using only amino acid sequences. Additionally, the substrate specificity of the acetylated site was investigated in detail with reference to the subcellular colocalization of acetyltransferases and acetylated proteins. The proposed method, N-Ace, is evaluated using independent test sets in various acetylated residues and predictive accuracies of 90% were achieved, indicating that the performance of N-Ace is comparable with that of other acetylation prediction methods. N-Ace not only provides a user-friendly input/output interface but also is a creative method for predicting protein acetylation sites. This novel analytical resource is now freely available at http://N-Ace.mbc.NCTU.edu.tw/.

A comprehensive resource for integrating and displaying protein post-translational modifications.

BACKGROUND: Protein Post-Translational Modification (PTM) plays an essential role in cellular control mechanisms that adjust protein physical and chemical properties, folding, conformation, stability and activity, thus also altering protein function. FINDINGS: dbPTM (version 1.0), which was developed previously, aimed on a comprehensive collection of protein post-translational modifications. In this update version (dbPTM2.0), we developed a PTM database towards an expert system of protein post-translational modifications. The database comprehensively collects experimental and predictive protein PTM sites. In addition, dbPTM2.0 was extended to a knowledge base comprising the modified sites, solvent accessibility of substrate, protein secondary and tertiary structures, protein domains, protein intrinsic disorder region, and protein variations. Moreover, this work compiles a benchmark to construct evaluation datasets for computational study to identifying PTM sites, such as phosphorylated sites, glycosylated sites, acetylated sites and methylated sites. CONCLUSION: The current release not only provides the sequence-based information, but also annotates the structure-based information for protein post-translational modification. The interface is also designed to facilitate the access to the resource. This effective database is now freely accessible at http://dbPTM.mbc.nctu.edu.tw/.

Incorporating support vector machine for identifying protein tyrosine sulfation sites.

Tyrosine sulfation is a post-translational modification of many secreted and membrane-bound proteins. It governs protein-protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty-two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five-fold cross-validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in five-fold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites.

Incorporating structural characteristics for identification of protein methylation sites.

Studies over the last few years have identified protein methylation on histones and other proteins that are involved in the regulation of gene transcription. Several works have developed approaches to identify computationally the potential methylation sites on lysine and arginine. Studies of protein tertiary structure have demonstrated that the sites of protein methylation are preferentially in regions that are easily accessible. However, previous studies have not taken into account the solvent-accessible surface area (ASA) that surrounds the methylation sites. This work presents a method named MASA that combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine. Since most experimental methylation sites are not associated with corresponding protein tertiary structures in the Protein Data Bank, the effective solvent-accessible prediction tools have been adopted to determine the potential ASA values of amino acids in proteins. Evaluation of predictive performance by cross-validation indicates that the ASA values around the methylation sites can improve the accuracy of prediction. Additionally, an independent test reveals that the prediction accuracies for methylated lysine and arginine are 80.8 and 85.0%, respectively. Finally, the proposed method is implemented as an effective system for identifying protein methylation sites. The developed web server is freely available at http://MASA.mbc.nctu.edu.tw/.