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Cited by 572

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

De Novo Design and Structural Characterization of Proteins and Metalloproteins

DeGrado, William F., Summa, Christopher M., Pavone, Vincenzo, Nastri, Flavia, Lombardi, Angela·1999·Annual Review of Biochemistry

Abstract

▪ Abstract De novo protein design has recently emerged as an attractive approach for studying the structure and function of proteins. This approach critically tests our understanding of the principles of protein folding; only in de novo design must one truly confront the issue of how to specify a protein's fold and function. If we truly understand proteins, it should be possible to design receptors, enzymes, and ion channels from scratch. Further, as this understanding evolves and is further refined, it should be possible to design proteins and biomimetic polymers with properties unprecedented in nature.

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Crossref · 10.1146/annurev.biochem.68.1.779

Cites

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Metadata

Authors
DeGrado, W. F., Summa, C. M., Pavone, V., Nastri, F., & Lombardi, A.
Venue
Annual Review of Biochemistry
Published
1999-06
Volume
68
Issue
1
Pages
779-819
ISSN
  • 0066-4154 (ISSN-L)
  • 1545-4509
OpenAlex
W2104460080
Cited by
572
References
12
FWCI
2.66
Type
journal article
Language
en
Open access
closed

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This page
CoChat. (2026). Citation check: De Novo Design and Structural Characterization of Proteins and Metalloproteins. https://app.cochat.ai/papers/10.1146/annurev.biochem.68.1.779
The paper
DeGrado, W. F., Summa, C. M., Pavone, V., Nastri, F., & Lombardi, A. (1999). De Novo Design and Structural Characterization of Proteins and Metalloproteins. Annual Review of Biochemistry. https://doi.org/10.1146/annurev.biochem.68.1.779

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.