Cited by 266Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

LINUS: a hierarchic procedure to predict the fold of a protein

Srinivasan, Rajgopal, Rose, George D.·1995·Proteins Structure Function and Bioinformatics

Abstract

We describe LINUS, a hierarchic procedure to predict the fold of a protein from its amino acid sequence alone. The algorithm, which has been implemented in a computer program, was applied to large, overlapping fragments from a diverse test set of 7 X-ray-elucidated proteins, with encouraging results. For all proteins but one, the overall fragment topology is well predicted, including both secondary and supersecondary structure. The algorithm was also applied to a molecule of unknown conformation, groES, in which X-ray structure determination is presently ongoing. LINUS is an acronym for Local Independently Nucleated Units of Structure. The procedure ascends the folding hierarchy in discrete stages, with concomitant accretion of structure at each step. The chain is represented by simplified geometry and folds under the influence of a primitive energy function. The only accurately described energetic quantity in this work is hard sphere repulsion--the principal force involved in organizing protein conformation [Richards, F. M. Ann. Rev. Biophys. Bioeng. 6:151-176, 1977]. Among other applications, the method is a natural tool for use in the human genome initiative.

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Crossref · 10.1002/prot.340220202

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Authors
Srinivasan, R., & Rose, G. D.
Affiliations
Johns Hopkins University (US)
Corresponding author
Rose, George D.
Venue
Proteins Structure Function and Bioinformatics
Publisher
Wiley
Published
1995-06
Volume
22
Issue
2
Pages
81-99
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
OpenAlex
W2132820390
Cited by
266
References
77
Citations by year
2025: 1 · 2021: 5 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 5 · 2016: 4 · 2015: 5 · 2014: 4 · 2013: 4 · 2012: 5
FWCI
12.16
Type
journal article
Language
en
Open access
closed
Locations
  • Proteins Structure Function and Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute of General Medical Sciences (GM-29458, R01GM029458)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
Linus, protein fold prediction, secondary structure prediction, supersecondary structure, hard-sphere repulsion, GroES, Human Genome Project
MeSH terms
Algorithms, Amino Acids, Apoproteins, Base Sequence, Carrier Proteins, Computer Simulation, Cytochrome b Group, Models, Molecular, Molecular Sequence Data, Myoglobin, Neoplasm Proteins, Plastocyanin, Tetrahydrofolate Dehydrogenase, Thermodynamics, Molecular Structure, Serpins, Protein Structure, Secondary, Protein Folding, Crystallography, X-Ray, Chaperonin 10, Myelin P2 Protein, Escherichia coli Proteins, Fatty Acid-Binding Proteins

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