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

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

Structure and Computation in Immunoreagent Design : From Diagnostics to Vaccines

Novel immunological tools for efficient diagnosis and treatment of emerging infections are urgently required. Advances in the diagnostic and vaccine development fields are continuously progressing, with reverse vaccinology and structural vaccinology (SV) methods for antigen identification and structure-based antigen (re)design playing increasingly relevant roles. SV, in particular, is predicted to be the front-runner in the future development of diagnostics and vaccines targeting challenging diseases such as AIDS and cancer. We review state-of-the-art methodologies for structure-based epitope identification and antigen design, with specific applicative examples. We highlight the implications of such methods for the engineering of biomolecules with improved immunological properties, potential diagnostic and/or therapeutic uses, and discuss the perspectives of structure-based rational design for the production of advanced immunoreagents. Immunodiagnostic-based serological tests offer rapid and high-throughput diagnosis of multiple pathogens and can ascertain disease progression.3D structures of protein antigens can be used to predict epitope location using computational biology methods.Computationally designed synthetic epitopes can provide new chemical tools with distinct applications, from diagnosis and patient profiling to therapeutic approaches based on new vaccines.Structure-based antigen design is predicted to deliver future vaccines targeting challenging diseases such as HIV and cancer.As an alternative to nanoparticle epitope presentation systems, structure-based in silico epitope grafting and design methods may be adopted to transplant epitopes onto protein scaffolds to generate antigens that stimulate more potent immune responses

Longitudinal Surveillance of Porcine Rotavirus B Strains from the United States and Canada and In Silico Identification of Antigenically Important Sites

Citation: Shepherd, F.K.; Murtaugh, M.P.; Chen, F.; Culhane, M.R.; Marthaler, D.G. Longitudinal Surveillance of Porcine Rotavirus B Strains from the United States and Canada and In Silico Identification of Antigenically Important Sites. Pathogens 2017, 6, 64.Rotavirus B (RVB) is an important swine pathogen, but control and prevention strategies are limited without an available vaccine. To develop a subunit RVB vaccine with maximal effect, we characterized the amino acid sequence variability and predicted antigenicity of RVB viral protein 7 (VP7), a major neutralizing antibody target, from clinically infected pigs in the United States and Canada. We identified genotype-specific antigenic sites that may be antibody neutralization targets. While some antigenic sites had high amino acid functional group diversity, nine antigenic sites were completely conserved. Analysis of nucleotide substitution rates at amino acid sites (dN/dS) suggested that negative selection appeared to be playing a larger role in the evolution of the identified antigenic sites when compared to positive selection, and was identified in six of the nine conserved antigenic sites. These results identified important characteristics of RVB VP7 variability and evolution and suggest antigenic residues on RVB VP7 that are negatively selected and highly conserved may be good candidate regions to include in a subunit vaccine design due to their tendency to remain stable

5th Congress of the Spanish Proteomics Society. Time to Imagine Barcelona

Abstract Book of the 5th Congress of the Spanish Proteomics Society. Time to Imagine, 5-8 February, 201