Evidence map›Paper›PMID 36582435›Full record

ArticleComputational and structural biotechnology journal2023

SeqCP: A sequence-based algorithm for searching circularly permuted proteins.

Chi-Chun Chen, Yu-Wei Huang, Hsuan-Cheng Huang, Wei-Cheng Lo, Ping-Chiang Lyu

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Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Detection of circular permutations by Protein Language Models.Computational and structural biotechnology journal · 2025
    Article
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chi-Chun ChenBioinformatics Program, Institute of Information Science, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan.
Yu-Wei HuangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Hsuan-Cheng HuangBioinformatics Program, Institute of Information Science, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan.
Wei-Cheng LoInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Ping-Chiang LyuInstitute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu 300, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.

Indexed as

AUC, area under the ROC curveCE, combinatorial extensionCE-CP, CE with Circular PermutationsCircular permutantsCircular permutationCP, circular permutationCPDB, Circular Permutation DatabaseCPMs, circular permutantsCPSARST, Circular Permutation Search Aided by Ramachandran Sequential TransformationMCC, Matthews correlation coefficientProtein sequence analysisProtein structure modelingRMSD, root-mean-square distanceROC, receiver operating characteristic

Identifiers

PMID36582435
PMCPMC9763678

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.