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Cited by 277

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Pattern-induced multi-sequence alignment (PUMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling

Smith, Randall F., Smmith, Temple F.·1992·"Protein Engineering, Design and Selection"

Abstract

A multiple sequence alignment algorithm is described that uses a dynamic programming-based pattern construction method to align a set of homologous sequences based on their common pattern of conserved sequence elements. This pattern-induced multi-sequence alignment (PIMA) algorithm can employ secondary-structure dependent gap penalties for use in comparative modelling of new sequences when the three-dimensional structure of one or more members of the same family is known. We show that the use of secondary structure information can significantly improve the accuracy of aligning structure boundaries in a set of homologous sequences even when the structure of only one member of the family is known.

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Crossref · 10.1093/protein/5.1.35

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Metadata

Authors
Smith, R. F., & Smmith, T. F.
Venue
"Protein Engineering, Design and Selection"
Published
1992
Volume
5
Issue
1
Pages
35-41
ISSN
  • 0269-2139 (ISSN-L)
  • 1741-0126
  • 1741-0134
OpenAlex
W2128442209
Cited by
277
References
0
FWCI
5.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Pattern-induced multi-sequence alignment (PUMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling. https://app.cochat.ai/papers/10.1093/protein/5.1.35
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Smith, R. F., & Smmith, T. F. (1992). Pattern-induced multi-sequence alignment (PUMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling. "Protein Engineering, Design and Selection". https://doi.org/10.1093/protein/5.1.35

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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.