Detection of circular permutations by Protein Language Models
Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004525 |
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| author | Yue Hu Bin Huang Chun Zi Zang Jia Jie Xu |
| author_facet | Yue Hu Bin Huang Chun Zi Zang Jia Jie Xu |
| author_sort | Yue Hu |
| collection | DOAJ |
| description | Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input. However, many current Protein Language Model-based alignment methods, which rely on sequence alignment algorithms like the Smith-Waterman algorithm, face significant difficulties when dealing with circular permutation (CP) due to their dependency on linear sequence order. This sequence order dependency makes them unsuitable for accurately detecting CP. Our approach, named plmCP, combines classical genetic principles with modern alignment techniques leveraging Protein Language Models to address these limitations. By integrating genetic knowledge, the plmCP method avoids the sequence order dependency, allowing for effective detection of circular permutations and contributing significantly to protein research and engineering by embracing structural flexibility. |
| format | Article |
| id | doaj-art-8ffc9437e5004964bd3da06d79d4a984 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-8ffc9437e5004964bd3da06d79d4a9842025-01-06T04:08:36ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127214220Detection of circular permutations by Protein Language ModelsYue Hu0Bin Huang1Chun Zi Zang2Jia Jie Xu3School of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, China; Kyiv College, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, China; Corresponding author at: School of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, ChinaSchool of Life Sciences, Yunnan Normal University, Kunming, Yunnan 650500, China; Corresponding author.Kyiv College, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, ChinaSchool of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, ChinaProtein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input. However, many current Protein Language Model-based alignment methods, which rely on sequence alignment algorithms like the Smith-Waterman algorithm, face significant difficulties when dealing with circular permutation (CP) due to their dependency on linear sequence order. This sequence order dependency makes them unsuitable for accurately detecting CP. Our approach, named plmCP, combines classical genetic principles with modern alignment techniques leveraging Protein Language Models to address these limitations. By integrating genetic knowledge, the plmCP method avoids the sequence order dependency, allowing for effective detection of circular permutations and contributing significantly to protein research and engineering by embracing structural flexibility.http://www.sciencedirect.com/science/article/pii/S2001037024004525Circular Permutation； Protein Language Models； Protein Structure Alignment |
| spellingShingle | Yue Hu Bin Huang Chun Zi Zang Jia Jie Xu Detection of circular permutations by Protein Language Models Computational and Structural Biotechnology Journal Circular Permutation； Protein Language Models； Protein Structure Alignment |
| title | Detection of circular permutations by Protein Language Models |
| title_full | Detection of circular permutations by Protein Language Models |
| title_fullStr | Detection of circular permutations by Protein Language Models |
| title_full_unstemmed | Detection of circular permutations by Protein Language Models |
| title_short | Detection of circular permutations by Protein Language Models |
| title_sort | detection of circular permutations by protein language models |
| topic | Circular Permutation； Protein Language Models； Protein Structure Alignment |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004525 |
| work_keys_str_mv | AT yuehu detectionofcircularpermutationsbyproteinlanguagemodels AT binhuang detectionofcircularpermutationsbyproteinlanguagemodels AT chunzizang detectionofcircularpermutationsbyproteinlanguagemodels AT jiajiexu detectionofcircularpermutationsbyproteinlanguagemodels |