Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature

<p>Abstract</p> <p>Background</p> <p>Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction.</p> <p>Results</p> <p>In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including the impact of interior residues, different contributions of adjacent residues, and the imbalanced data which contain much more non-epitope residues than epitope residues. In order to address above issues, we take following strategies. Firstly, a concept of 'thick surface patch' instead of 'surface patch' is introduced to describe the local spatial context of each surface residue, which considers the impact of interior residue. The comparison between the thick surface patch and the surface patch shows that interior residues contribute to the recognition of epitopes. Secondly, statistical significance of the distance distribution difference between non-epitope patches and epitope patches is observed, thus an adjacent residue distance feature is presented, which reflects the unequal contributions of adjacent residues to the location of binding sites. Thirdly, a bootstrapping and voting procedure is adopted to deal with the imbalanced dataset. Based on the above ideas, we propose a new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine. The experiments show that our method can predict conformational B-cell epitopes with high accuracy. Evaluated by leave-one-out cross validation (LOOCV), our method achieves the mean AUC value of 0.633 for the benchmark bound dataset, and the mean AUC value of 0.654 for the benchmark unbound dataset. When compared with the state-of-the-art prediction models in the independent test, our method demonstrates comparable or better performance.</p> <p>Conclusions</p> <p>Our method is demonstrated to be effective for the prediction of conformational epitopes. Based on the study, we develop a tool to predict the conformational epitopes from 3D structures, available at <url>http://code.google.com/p/my-project-bpredictor/downloads/list</url>.</p

The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction

A conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell prediction. In this paper, we compare several conformational epitope B cell prediction models from non-ensemble and ensemble approaches. A sampling method from Random undersampling, SMOTE, and cluster-based undersampling is combined with a decision tree or SVM to build a non-ensemble model. A random forest model and several variants of the bagging method is used to construct the ensemble model. A 10-fold cross-validation method is used to validate the model.  The experiment results show that the combination of the cluster-based under-sampling and decision tree outperformed the other sampling method when combined with the non-ensemble and the ensemble method. This study provides a baseline to improve existing models for dealing with the class imbalance in the conformational epitope prediction

Tertiary structure-based prediction of conformational B-cell epitopes through B factors

Motivation: B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods. Method and results: We propose a much more accurate B-cell epitope prediction method. Our method uses a new feature B factor (obtained from X-ray crystallography), combined with other basic physicochemical, statistical, evolutionary and structural features of each residue. These basic features are extended by a sequence window and a structure window. All these features are then learned by a two-stage random forest model to identify clusters of antigenic residues and to remove isolated outliers. Tested on a dataset of 55 epitopes from 45 tertiary structures, we prove that our method significantly outperforms all three existing structure-based epitope predictors. Following comprehensive analysis, it is found that features such as B factor, relative accessible surface area and protrusion index play an important role in characterizing B-cell epitopes. Our detailed case studies on an HIV antigen and an influenza antigen confirm that our second stage learning is effective for clustering true antigenic residues and for eliminating self-made prediction errors introduced by the first-stage learning. © 2014 The Author. Published by Oxford University Press. All rights reserved

Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class

AI driven B-cell Immunotherapy Design

Antibodies, a prominent class of approved biologics, play a crucial role in detecting foreign antigens. The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction, which demands resource-intensive experimental techniques for characterisation. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. The past decade has also witnessed the evolution of computational approaches aiming to support immunotherapy design. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design, encompassing linear and conformational epitope prediction, paratope prediction, and antibody design. We mapped the most commonly used data sources, evaluation metrics, and method availability and thoroughly assessed their significance and limitations, discussing the main challenges ahead

Conformational B-Cell Epitope Prediction on Antigen Protein Structures: A Review of Current Algorithms and Comparison with Common Binding Site Prediction Methods

Accurate prediction of B-cell antigenic epitopes is important for immunologic research and medical applications, but compared with other bioinformatic problems, antigenic epitope prediction is more challenging because of the extreme variability of antigenic epitopes, where the paratope on the antibody binds specifically to a given epitope with high precision. In spite of the continuing efforts in the past decade, the problem remains unsolved and therefore still attracts a lot of attention from bioinformaticists. Recently, several discontinuous epitope prediction servers became available, and it is intriguing to review all existing methods and evaluate their performances on the same benchmark. In addition, these methods are also compared against common binding site prediction algorithms, since they have been frequently used as substitutes in the absence of good epitope prediction methods

Recent advances in B-cell epitope prediction methods

Identification of epitopes that invoke strong responses from B-cells is one of the key steps in designing effective vaccines against pathogens. Because experimental determination of epitopes is expensive in terms of cost, time, and effort involved, there is an urgent need for computational methods for reliable identification of B-cell epitopes. Although several computational tools for predicting B-cell epitopes have become available in recent years, the predictive performance of existing tools remains far from ideal. We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods

Positive-unlabeled learning for the prediction of conformational B-cell epitopes

© 2015 Ren et al. Background: The incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data); the remaining residues are unlabeled. As some of these uncertain residues can possibly be grouped to form novel but currently unknown epitopes, it is misguided to unanimously classify all the unlabeled residues as negative training data following the traditional supervised learning scheme. Results: We propose a positive-unlabeled learning algorithm to address this problem. The key idea is to distinguish between epitope-likely residues and reliable negative residues in unlabeled data. The method has two steps: (1) identify reliable negative residues using a weighted SVM with a high recall; and (2) construct a classification model on the positive residues and the reliable negative residues. Complex-based 10-fold cross-validation was conducted to show that this method outperforms those commonly used predictors DiscoTope 2.0, ElliPro and SEPPA 2.0 in every aspect. We conducted four case studies, in which the approach was tested on antigens of West Nile virus, dihydrofolate reductase, beta-lactamase, and two Ebola antigens whose epitopes are currently unknown. All the results were assessed on a newly-established data set of antigen structures not bound by antibodies, instead of on antibody-bound antigen structures. These bound structures may contain unfair binding information such as bound-state B-factors and protrusion index which could exaggerate the epitope prediction performance. Source codes are available on request

Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction

Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed