Literature DB >> 34596844

Computational Design of Structured and Functional Peptide Macrocycles.

Stephanie Berger1, Parisa Hosseinzadeh2.   

Abstract

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Computational design; Macrocycles; Peptide design; Rosetta

Mesh:

Substances:

Year:  2022        PMID: 34596844     DOI: 10.1007/978-1-0716-1689-5_5

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  34 in total

1.  RNA Display Methods for the Discovery of Bioactive Macrocycles.

Authors:  Yichao Huang; Mareike Margarete Wiedmann; Hiroaki Suga
Journal:  Chem Rev       Date:  2018-11-05       Impact factor: 60.622

2.  In-solution enrichment identifies peptide inhibitors of protein-protein interactions.

Authors:  Fayçal Touti; Zachary P Gates; Anupam Bandyopadhyay; Guillaume Lautrette; Bradley L Pentelute
Journal:  Nat Chem Biol       Date:  2019-03-18       Impact factor: 15.040

Review 3.  The future of peptide-based drugs.

Authors:  David J Craik; David P Fairlie; Spiros Liras; David Price
Journal:  Chem Biol Drug Des       Date:  2013-01       Impact factor: 2.817

Review 4.  Use of Peptide Libraries for Identification and Optimization of Novel Antimicrobial Peptides.

Authors:  Martin Ashby; Asya Petkova; Jurnorain Gani; Ralf Mikut; Kai Hilpert
Journal:  Curr Top Med Chem       Date:  2017       Impact factor: 3.295

Review 5.  Therapeutic peptides: Historical perspectives, current development trends, and future directions.

Authors:  Jolene L Lau; Michael K Dunn
Journal:  Bioorg Med Chem       Date:  2017-07-01       Impact factor: 3.641

Review 6.  Cyclic peptide therapeutics: past, present and future.

Authors:  Alessandro Zorzi; Kaycie Deyle; Christian Heinis
Journal:  Curr Opin Chem Biol       Date:  2017-02-27       Impact factor: 8.822

7.  Accurate computational design of multipass transmembrane proteins.

Authors:  Peilong Lu; Duyoung Min; Frank DiMaio; Kathy Y Wei; Michael D Vahey; Scott E Boyken; Zibo Chen; Jorge A Fallas; George Ueda; William Sheffler; Vikram Khipple Mulligan; Wenqing Xu; James U Bowie; David Baker
Journal:  Science       Date:  2018-03-02       Impact factor: 47.728

8.  Cell-Permeable Bicyclic Peptidyl Inhibitors against NEMO-IκB Kinase Interaction Directly from a Combinatorial Library.

Authors:  Curran A Rhodes; Patrick G Dougherty; Jahan K Cooper; Ziqing Qian; Steffen Lindert; Qi-En Wang; Dehua Pei
Journal:  J Am Chem Soc       Date:  2018-09-11       Impact factor: 15.419

Review 9.  Tumor-targeting peptides from combinatorial libraries.

Authors:  Ruiwu Liu; Xiaocen Li; Wenwu Xiao; Kit S Lam
Journal:  Adv Drug Deliv Rev       Date:  2016-05-19       Impact factor: 15.470

10.  Accurate design of megadalton-scale two-component icosahedral protein complexes.

Authors:  Jacob B Bale; Shane Gonen; Yuxi Liu; William Sheffler; Daniel Ellis; Chantz Thomas; Duilio Cascio; Todd O Yeates; Tamir Gonen; Neil P King; David Baker
Journal:  Science       Date:  2016-07-22       Impact factor: 47.728

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  1 in total

1.  Harnessing protein folding neural networks for peptide-protein docking.

Authors:  Tomer Tsaban; Julia K Varga; Orly Avraham; Ziv Ben-Aharon; Alisa Khramushin; Ora Schueler-Furman
Journal:  Nat Commun       Date:  2022-01-10       Impact factor: 14.919

  1 in total

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