Proof of principle for epitope-focused vaccine design

By Bruno E. Correia, J. Bates, Rebecca J. Loomis, G. Baneyx, Chris Carrico, Joseph G. Jardine, P. Rupert, C. Correnti, Oleksandr Kalyuzhniy, Vinayak Vittal and 17 more. 2014

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Vaccines prevent infectious disease largely by inducing protective neutralizing antibodies against vulnerable epitopes. Several major pathogens have resisted traditional vaccine development, although vulnerable epitopes targeted by neutralizing antibodies have been identified for several such cases. Hence, new vaccine design methods to induce epitope-specific neutralizing antibodies are needed. Here we show, with a neutralization epitope from respiratory syncytial virus, that computational protein design can generate small, thermally and conformationally stable protein scaffolds that accurately mimic the viral epitope structure and induce potent neutralizing antibodies. These scaffolds represent promising leads for the research and development of a human respiratory syncytial virus vaccine needed to protect infants, young children and the elderly. More generally, the results provide proof of principle for epitope-focused and scaffold-based vaccine design, and encourage the evaluation and further development of these strategies for a variety of other vaccine targets, including antigenically highly variable pathogens such as human immunodeficiency virus and influenza.

Subfields: Epidemiology, Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “omputational protein design has emerged as a powerful tool for rational protein design, enabling signi ﬁ cant achievements in the engineering of therapeutics 1 – 3 , 4 – 6 , enzymes 7,8 , and more 9 – 11 .”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “[7] Bruno E Correia, John T Bates, Rebecca J Loomis, Gretchen Baneyx, Chris Carrico, Joseph G Jardine, Peter Rupert, Colin Correnti, Oleksandr Kalyuzhniy, Vinayak Vittal, Mary J Connell, Eric Stevens, Alexandria Schroeter, Man Chen, Skye Macpherson, Andreia M Serra, Yumiko Adachi, Margaret A Holmes,”
Scaffolding protein functional sites using deep learning (2022, methodology): “…acid identities which produce the desired activity--for enzymes this can be done using quantum chemistry calculations (1–3) and for protein binders by fragment docking calculations (4, 5); alternatively, functional sites can be extracted from a native protein having the desired activity (6, 7).”
Achievements and Challenges in Computational Protein Design. (2017, methodology): “In 2014 the Schief lab collaborated with Baker and others to apply CPD for the important cause of epitope-focused vaccine design [116].”
Protein sequence design with a learned potential (2020, background): “These advances have led to significant achievements in the engineering of therapeutics [13, 14, 15], biosensors [16, 17, 18], enzymes [19, 20, 21, 22], and more [23, 24, 25, 26, 27].”
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network (2019, background): “One benifit can be in computational vaccine deveopment, where a large diversity of protein structures as saffolds is required for a successful graft of a desired motif[27] [28] [29].”