In Silico Structure-Based Vaccine Design

Sakshi Piplani; David Winkler; Yoshikazu Honda-Okubo; Varun Khanna; Nikolai Petrovsky

doi:10.1007/978-1-0716-3239-0_26

In Silico Structure-Based Vaccine Design

Sakshi Piplani
, David Winkler
, Yoshikazu Honda-Okubo
, Varun Khanna
, Nikolai Petrovsky

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Citations (Scopus)

Abstract

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Original language	English
Title of host publication	Computational Vaccine Design
Editors	Pedro A. Reche
Place of Publication	New York
Publisher	Springer Nature
Chapter	26
Pages	371-399
Number of pages	29
ISBN (Electronic)	9781071632390
ISBN (Print)	9781071632383
DOIs	https://doi.org/10.1007/978-1-0716-3239-0_26
Publication status	Published - 2023
Externally published	Yes

Publication series

Name	Methods in molecular biology (Clifton, N.J.)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Computer-aided vaccine design
COVID-19
De novo design
Focused library design
High-throughput virtual screening
Molecular docking
Protein modeling
SARS-CoV-2
Structure-based vaccine design
Target selection

Access to Document

10.1007/978-1-0716-3239-0_26Licence: In Copyright

Cite this

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author = "Sakshi Piplani and David Winkler and Yoshikazu Honda-Okubo and Varun Khanna and Nikolai Petrovsky",

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AU - Piplani, Sakshi

AU - Winkler, David

AU - Honda-Okubo, Yoshikazu

AU - Khanna, Varun

AU - Petrovsky, Nikolai

PY - 2023

Y1 - 2023

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AB - Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

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KW - COVID-19

KW - De novo design

KW - Focused library design

KW - High-throughput virtual screening

KW - Molecular docking

KW - Protein modeling

KW - SARS-CoV-2

KW - Structure-based vaccine design

KW - Target selection

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AN - SCOPUS:85160749883

SN - 9781071632383

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A2 - Reche, Pedro A.

PB - Springer Nature

CY - New York

ER -

In Silico Structure-Based Vaccine Design

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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