Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity

Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.Fil: Rasmussen, Michael. Universidad de Copenhagen; DinamarcaFil: Fenoy, Luis Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas ; ArgentinaFil: Harndahl, Mikkel. Universidad de Copenhagen; DinamarcaFil: Kristensen, Anne Bregnballe. Universidad de Copenhagen; DinamarcaFil: Nielsen, Ida Kallehauge. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Copenhagen; DinamarcaFil: Buus, Søren. Universidad de Copenhagen; Dinamarc

CD4 T cell responses to Theileria parva in immune cattle recognise a diverse set of parasite antigens presented on the surface of infected lymphoblasts

Parasite-specific CD8 T cell responses play a key role in mediating immunity against Theileria parva in cattle (Bos taurus), and there is evidence that efficient induction of these responses requires CD4 T cell responses. However, information on the antigenic specificity of the CD4 T cell response is lacking. The current study used a high-throughput system for Ag identification using CD4 T cells from immune animals to screen a library of ~40,000 synthetic peptides representing 499 T. parva gene products. Use of CD4 T cells from 12 immune cattle, representing 12 MHC class II types, identified 26 Ags. Unlike CD8 T cell responses, which are focused on a few dominant Ags, multiple Ags were recognized by CD4 T cell responses of individual animals. The Ags had diverse properties, but included proteins encoded by two multimember gene families: five haloacid dehalogenases and five subtelomere-encoded variable secreted proteins. Most Ags had predicted signal peptides and/or were encoded by abundantly transcribed genes, but neither parameter on their own was reliable for predicting antigenicity. Mapping of the epitopes confirmed presentation by DR or DQ class II alleles and comparison of available T. parva genome sequences demonstrated that they included both conserved and polymorphic epitopes. Immunization of animals with vaccine vectors expressing two of the Ags demonstrated induction of CD4 T cell responses capable of recognizing parasitized cells. The results of this study provide detailed insight into the CD4 T cell responses induced by T. parva and identify Ags suitable for use in vaccine development.Fil: Morrison, W. Ivan. University of Edinburgh; Reino UnidoFil: Aguado, Adriana. University of Edinburgh; Reino UnidoFil: Sheldrake, Tara A.. University of Edinburgh; Reino UnidoFil: Palmateer, Nicholas C.. University of Maryland; Estados UnidosFil: Ifeonu, Olukemi O.. University of Maryland; Estados UnidosFil: Tretina, Kyle. University of Maryland; Estados UnidosFil: Parsons, Keith. Institute For Animal Health; Reino UnidoFil: Fenoy, Luis Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Connelley, Timothy. University of Edinburgh; Reino UnidoFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Silva, Joana C.. University of Maryland; Estados Unido

Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks

A representation method is an algorithm that calculates numerical feature vectors for samples in a dataset. Such vectors, also known as embeddings, define a relatively low-dimensional space able to efficiently encode high-dimensional data. Very recently, many types of learned data representations based on machine learning have appeared and are being applied to several tasks in bioinformatics. In particular, protein representation learning methods integrate different types of protein information (sequence, domains, etc.), in supervised or unsupervised learning approaches, and provide embeddings of protein sequences that can be used for downstream tasks. One task that is of special interest is the automatic function prediction of the huge number of novel proteins that are being discovered nowadays and are still totally uncharacterized. However, despite its importance, up to date there is not a fair benchmark study of the predictive performance of existing proposals on the same large set of proteins and for very concrete and common bioinformatics tasks. Therefore, this lack of benchmark studies prevent the community from using adequate predictive methods for accelerating the functional characterization of proteins. In this study, we performed a detailed comparison of protein sequence representation learning methods, explaining each approach and comparing them with an experimental benchmark on several bioinformatics tasks: (i) determining protein sequence similarity in the embedding space; (ii) inferring protein domains and (iii) predicting ontology-based protein functions. We examine the advantages and disadvantages of each representation approach over the benchmark results. We hope the results and the discussion of this study can help the community to select the most adequate machine learning-based technique for protein representation according to the bioinformatics task at hand.Fil: Fenoy, Luis Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Edera, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin