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Cited by 294

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Combining local-structure, fold-recognition, and new fold methods for protein structure prediction

Karplus, Kevin, Karchin, Rachel, Draper, Jenny, Casper, Jonathan, Mandel-Gutfreund, Yael, Diekhans, Mark, Hughey, Richard·2003·Proteins: Structure, Function, and Genetics

Abstract

This article presents an overview of the SAM-T02 method for protein fold recognition and the UNDERTAKER program for ab initio predictions. The SAM-T02 server is an automatic method that uses two-track hidden Markov models (HMMS) to find and align template proteins from PDB to the target protein. The two-track HMMs use an amino acid alphabet and one of several different local structure alphabets. The UNDERTAKER program is a new fragment-packing program that can use short or long fragments and alignments to create protein conformations. The HMMs and fold-recognition alignments from the SAM-T02 method were used to generate the fragment and alignment libraries used by UNDERTAKER. We present results on a few selected targets for which this combined method worked particularly well: T0129, T0181, T0135, T0130, and T0139.

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Metadata

Authors
Karplus, K., Karchin, R., Draper, J., Casper, J., Mandel-Gutfreund, Y., Diekhans, M., & Hughey, R.
Venue
Proteins: Structure, Function, and Genetics
Publisher
Wiley
Published
2003
Volume
53
Issue
S6
Pages
491-496
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
OpenAlex
W2106066024
Cited by
294
References
1
FWCI
8.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Combining local-structure, fold-recognition, and new fold methods for protein structure prediction. https://app.cochat.ai/papers/10.1002/prot.10540
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Karplus, K., Karchin, R., Draper, J., Casper, J., Mandel-Gutfreund, Y., Diekhans, M., & Hughey, R. (2003). Combining local-structure, fold-recognition, and new fold methods for protein structure prediction. Proteins: Structure, Function, and Genetics. https://doi.org/10.1002/prot.10540

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