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Cited by 422

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Polymer principles and protein folding

Open access
Dill, Ken A.·1999·Protein Science

Abstract

This paper surveys the emerging role of statistical mechanics and polymer theory in protein folding. In the polymer perspective, the folding code is more a solvation code than a code of local ØΨ propensities. The polymer perspective resolves two classic puzzles: (1) the Blind Watchmaker' Paradox that biological proteins could not have originated from random sequences, and (2) Levinthal' Paradox that the folded state of a protein cannot be found by random search. Both paradoxes are traditionally framed in terms of random unguided searches through vast spaces, and vastness is equated with impossibility. But both processes are partly guided. The searches are more akin to balls rolling down funnels than balls rolling aimlessly on flat surfaces. In both cases, the vastness of the search is largely irrelevant to the search time and success. These ideas are captured by energy and fitness landscapes. Energy landscapes give a language for bridging between microscopics and macroscopics, for relating folding kinetics to equilibrium fluctuations, and for developing new and faster computational search strategies.

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Metadata

Authors
Dill, K. A.
Venue
Protein Science
Publisher
Wiley
Published
1999-01
Volume
8
Issue
6
Pages
1166-1180
ISSN
  • 0961-8368 (ISSN-L)
  • 1469-896X
OpenAlex
W2165890129
Cited by
422
References
4
FWCI
7.29
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Polymer principles and protein folding. https://app.cochat.ai/papers/10.1110/ps.8.6.1166
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Dill, K. A. (1999). Polymer principles and protein folding. Protein Science. https://doi.org/10.1110/ps.8.6.1166

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.