T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In t...
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Autor*in:	Yueming Hu [verfasserIn] Yejun Wang [verfasserIn] Xiaotian Hu [verfasserIn] Haoyu Chao [verfasserIn] Sida Li [verfasserIn] Qinyang Ni [verfasserIn] Yanyan Zhu [verfasserIn] Yixue Hu [verfasserIn] Ziyi Zhao [verfasserIn] Ming Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	T4SEpp T4SE Prediction T4SS Deep learning Protein language model Helicobacter pylori T4SEs

Übergeordnetes Werk:	In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 23(2024), Seite 801-812
Übergeordnetes Werk:	volume:23 ; year:2024 ; pages:801-812

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.csbj.2024.01.015

Katalog-ID:	DOAJ095908463

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spelling	10.1016/j.csbj.2024.01.015 doi (DE-627)DOAJ095908463 (DE-599)DOAJd38f51f29d5447beb54e80e50788113d DE-627 ger DE-627 rakwb eng TP248.13-248.65 Yueming Hu verfasserin aut T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp. T4SEpp T4SE Prediction T4SS Deep learning Protein language model Helicobacter pylori T4SEs Biotechnology Yejun Wang verfasserin aut Xiaotian Hu verfasserin aut Haoyu Chao verfasserin aut Sida Li verfasserin aut Qinyang Ni verfasserin aut Yanyan Zhu verfasserin aut Yixue Hu verfasserin aut Ziyi Zhao verfasserin aut Ming Chen verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 801-812 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:801-812 https://doi.org/10.1016/j.csbj.2024.01.015 kostenfrei https://doaj.org/article/d38f51f29d5447beb54e80e50788113d kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000151 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 801-812
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allfieldsGer	10.1016/j.csbj.2024.01.015 doi (DE-627)DOAJ095908463 (DE-599)DOAJd38f51f29d5447beb54e80e50788113d DE-627 ger DE-627 rakwb eng TP248.13-248.65 Yueming Hu verfasserin aut T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp. T4SEpp T4SE Prediction T4SS Deep learning Protein language model Helicobacter pylori T4SEs Biotechnology Yejun Wang verfasserin aut Xiaotian Hu verfasserin aut Haoyu Chao verfasserin aut Sida Li verfasserin aut Qinyang Ni verfasserin aut Yanyan Zhu verfasserin aut Yixue Hu verfasserin aut Ziyi Zhao verfasserin aut Ming Chen verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 801-812 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:801-812 https://doi.org/10.1016/j.csbj.2024.01.015 kostenfrei https://doaj.org/article/d38f51f29d5447beb54e80e50788113d kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000151 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 801-812
allfieldsSound	10.1016/j.csbj.2024.01.015 doi (DE-627)DOAJ095908463 (DE-599)DOAJd38f51f29d5447beb54e80e50788113d DE-627 ger DE-627 rakwb eng TP248.13-248.65 Yueming Hu verfasserin aut T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp. T4SEpp T4SE Prediction T4SS Deep learning Protein language model Helicobacter pylori T4SEs Biotechnology Yejun Wang verfasserin aut Xiaotian Hu verfasserin aut Haoyu Chao verfasserin aut Sida Li verfasserin aut Qinyang Ni verfasserin aut Yanyan Zhu verfasserin aut Yixue Hu verfasserin aut Ziyi Zhao verfasserin aut Ming Chen verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 801-812 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:801-812 https://doi.org/10.1016/j.csbj.2024.01.015 kostenfrei https://doaj.org/article/d38f51f29d5447beb54e80e50788113d kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000151 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 801-812
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callnumber-first	T - Technology
author	Yueming Hu
spellingShingle	Yueming Hu misc TP248.13-248.65 misc T4SEpp misc T4SE Prediction misc T4SS misc Deep learning misc Protein language model misc Helicobacter pylori T4SEs misc Biotechnology T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors
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topic_title	TP248.13-248.65 T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors T4SEpp T4SE Prediction T4SS Deep learning Protein language model Helicobacter pylori T4SEs
topic	misc TP248.13-248.65 misc T4SEpp misc T4SE Prediction misc T4SS misc Deep learning misc Protein language model misc Helicobacter pylori T4SEs misc Biotechnology
topic_unstemmed	misc TP248.13-248.65 misc T4SEpp misc T4SE Prediction misc T4SS misc Deep learning misc Protein language model misc Helicobacter pylori T4SEs misc Biotechnology
topic_browse	misc TP248.13-248.65 misc T4SEpp misc T4SE Prediction misc T4SS misc Deep learning misc Protein language model misc Helicobacter pylori T4SEs misc Biotechnology
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title	T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors
ctrlnum	(DE-627)DOAJ095908463 (DE-599)DOAJd38f51f29d5447beb54e80e50788113d
title_full	T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors
author_sort	Yueming Hu
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author_browse	Yueming Hu Yejun Wang Xiaotian Hu Haoyu Chao Sida Li Qinyang Ni Yanyan Zhu Yixue Hu Ziyi Zhao Ming Chen
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title_sort	t4sepp: a pipeline integrating protein language models to predict bacterial type iv secreted effectors
callnumber	TP248.13-248.65
title_auth	T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors
abstract	Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.
abstractGer	Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.
abstract_unstemmed	Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.
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title_short	T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors
url	https://doi.org/10.1016/j.csbj.2024.01.015 https://doaj.org/article/d38f51f29d5447beb54e80e50788113d http://www.sciencedirect.com/science/article/pii/S2001037024000151 https://doaj.org/toc/2001-0370
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author2	Yejun Wang Xiaotian Hu Haoyu Chao Sida Li Qinyang Ni Yanyan Zhu Yixue Hu Ziyi Zhao Ming Chen
author2Str	Yejun Wang Xiaotian Hu Haoyu Chao Sida Li Qinyang Ni Yanyan Zhu Yixue Hu Ziyi Zhao Ming Chen
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up_date	2024-07-03T17:19:36.594Z
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T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. 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T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

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