Vaccines research and development: poised for rapid growth

Rappuoli, Rino; Ulmer, Jeffrey M.

doi:10.1016/j.copbio.2007.11.001

Cited by 4 publications

(5 citation statements)

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“…Although current vaccines confer efficient protection against many pathogens, there is a need for novel vaccines protecting against pathogens causing chronic infection, such as HIV or hepatitis C virus (HCV), or stimulating vigorous responses against tumor Ags. In these cases, rational design of vaccines capable of inducing effective CD8 + and CD4 + T cell in addition to humoral responses is an objective of vaccine design (1).…”

mentioning

confidence: 99%

Fusion Proteins for Versatile Antigen Targeting to Cell Surface Receptors Reveal Differential Capacity to Prime Immune Responses

Mauvais

Burgevin

Barilleau

et al. 2010

The Journal of Immunology

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mentioning

confidence: 99%

Fusion Proteins for Versatile Antigen Targeting to Cell Surface Receptors Reveal Differential Capacity to Prime Immune Responses

Mauvais

Burgevin

Barilleau

et al. 2010

The Journal of Immunology

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“…A particularly important case of the need to design protein interacting regions with a function is represented in the emerging field of structural vaccinology (SV) . SV is an emerging driving force in modern immunology whereby structural knowledge on protein antigens is exploited to evolve better immunoreagents for use both in molecular diagnosis and in vaccine development.…”

Section: Computer‐based Design Of Nonnatural Protein Structuresmentioning

confidence: 99%

“…A particularly important case of the need to design protein interacting regions with a function is represented in the emerging field of structural vaccinology (SV). [80][81][82][83][84][85][86][87] SV is an emerging driving force in modern immunology whereby structural knowledge on protein antigens is exploited to evolve better immunoreagents for use both in molecular diagnosis and in vaccine development. In this particular case, the ability to predict the substructure of a protein antigen that is most apt to elicit a response by the B-or T-cell system of the host organism may translate in the development of new biomolecules with optimized immunoreactivity and potentially better and safer vaccine candidates.…”

Section: Applications In Immunological Studiesmentioning

confidence: 99%

Protein design: from computer models to artificial intelligence

Paladino

Marchetti

Rinaldi

et al. 2017

WIREs Comput Mol Sci

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The rational design of novel biomolecules with desired functional properties is one of the most fascinating challenges in science, with implications at the fundamental and practical levels. From the fundamental point of view, the design of proteins able to support nonnatural reactivities represents the decisive test on our understanding of the molecular mechanisms through which biomolecules operate. From the practical point of view, new designs may open the way to applications in a wide variety of fields, ranging from health to life science, and from catalysis to material sciences. During the past decades, we have witnessed an amazing transition in the application of computational methods to protein and enzyme design, from simple fold prediction to ab initio structural design. Herein, we review key areas and fundamental aspects of research in the design of protein structures, interactions, and reactivities. We also provide our perspective on the exciting range of developments that are made possible by the integration of innovations in hardware, software, and theory, while keeping an eye on the applications, challenges, and opportunities that can open up in many different domains of science. WIREs Comput Mol Sci 2017, 7:e1318. doi: 10.1002/wcms.1318 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

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“…Many tools have been and are still being developed to computationally predict epitopes (see S1 Supporting Information ). T-cell epitopes, which are typically short linear peptides, have proved to be easier to predict than B-cell epitopes [9] – [11] . Currently, there are two computational approaches to T-cell epitope prediction based on direct and indirect methods.…”

Section: Introductionmentioning

confidence: 99%

“…Current opinion suggests that T-cell epitope ‘only’ vaccines are not a solution to prevent infection, but are important in controlling an established infection by the recognition and clearance of infected cells [10] . For many infectious diseases (and cancers) it remains an open question if cell-mediated immunity (CMI) is required for successful prevention or eradication, either in addition to or instead of antibodies [11] .…”

Section: Introductionmentioning

confidence: 99%

Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

2014

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Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.

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Vaccines research and development: poised for rapid growth

Cited by 4 publications

References 0 publications

Fusion Proteins for Versatile Antigen Targeting to Cell Surface Receptors Reveal Differential Capacity to Prime Immune Responses

Fusion Proteins for Versatile Antigen Targeting to Cell Surface Receptors Reveal Differential Capacity to Prime Immune Responses

Protein design: from computer models to artificial intelligence

Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

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