Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chiara Gasbarri [verfasserIn] Serena Rosignoli [verfasserIn] Giacomo Janson [verfasserIn] Dalila Boi [verfasserIn] Alessandro Paiardini [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	PepThreader protein–protein interactions protein–peptide interactions template-based modeling

Übergeordnetes Werk:	In: Biomolecules - MDPI AG, 2013, 12(2022), 2, p 201
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:2, p 201

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/biom12020201

Katalog-ID:	DOAJ014481553

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allfields	10.3390/biom12020201 doi (DE-627)DOAJ014481553 (DE-599)DOAJb7c2a7d7429544bbad1524a2551efa16 DE-627 ger DE-627 rakwb eng QR1-502 Chiara Gasbarri verfasserin aut Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization. PepThreader protein–protein interactions protein–peptide interactions template-based modeling Microbiology Serena Rosignoli verfasserin aut Giacomo Janson verfasserin aut Dalila Boi verfasserin aut Alessandro Paiardini verfasserin aut In Biomolecules MDPI AG, 2013 12(2022), 2, p 201 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:12 year:2022 number:2, p 201 https://doi.org/10.3390/biom12020201 kostenfrei https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 kostenfrei https://www.mdpi.com/2218-273X/12/2/201 kostenfrei https://doaj.org/toc/2218-273X 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 2, p 201
spelling	10.3390/biom12020201 doi (DE-627)DOAJ014481553 (DE-599)DOAJb7c2a7d7429544bbad1524a2551efa16 DE-627 ger DE-627 rakwb eng QR1-502 Chiara Gasbarri verfasserin aut Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization. PepThreader protein–protein interactions protein–peptide interactions template-based modeling Microbiology Serena Rosignoli verfasserin aut Giacomo Janson verfasserin aut Dalila Boi verfasserin aut Alessandro Paiardini verfasserin aut In Biomolecules MDPI AG, 2013 12(2022), 2, p 201 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:12 year:2022 number:2, p 201 https://doi.org/10.3390/biom12020201 kostenfrei https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 kostenfrei https://www.mdpi.com/2218-273X/12/2/201 kostenfrei https://doaj.org/toc/2218-273X 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 2, p 201
allfields_unstemmed	10.3390/biom12020201 doi (DE-627)DOAJ014481553 (DE-599)DOAJb7c2a7d7429544bbad1524a2551efa16 DE-627 ger DE-627 rakwb eng QR1-502 Chiara Gasbarri verfasserin aut Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization. PepThreader protein–protein interactions protein–peptide interactions template-based modeling Microbiology Serena Rosignoli verfasserin aut Giacomo Janson verfasserin aut Dalila Boi verfasserin aut Alessandro Paiardini verfasserin aut In Biomolecules MDPI AG, 2013 12(2022), 2, p 201 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:12 year:2022 number:2, p 201 https://doi.org/10.3390/biom12020201 kostenfrei https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 kostenfrei https://www.mdpi.com/2218-273X/12/2/201 kostenfrei https://doaj.org/toc/2218-273X 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 2, p 201
allfieldsGer	10.3390/biom12020201 doi (DE-627)DOAJ014481553 (DE-599)DOAJb7c2a7d7429544bbad1524a2551efa16 DE-627 ger DE-627 rakwb eng QR1-502 Chiara Gasbarri verfasserin aut Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization. PepThreader protein–protein interactions protein–peptide interactions template-based modeling Microbiology Serena Rosignoli verfasserin aut Giacomo Janson verfasserin aut Dalila Boi verfasserin aut Alessandro Paiardini verfasserin aut In Biomolecules MDPI AG, 2013 12(2022), 2, p 201 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:12 year:2022 number:2, p 201 https://doi.org/10.3390/biom12020201 kostenfrei https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 kostenfrei https://www.mdpi.com/2218-273X/12/2/201 kostenfrei https://doaj.org/toc/2218-273X 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 2, p 201
allfieldsSound	10.3390/biom12020201 doi (DE-627)DOAJ014481553 (DE-599)DOAJb7c2a7d7429544bbad1524a2551efa16 DE-627 ger DE-627 rakwb eng QR1-502 Chiara Gasbarri verfasserin aut Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization. PepThreader protein–protein interactions protein–peptide interactions template-based modeling Microbiology Serena Rosignoli verfasserin aut Giacomo Janson verfasserin aut Dalila Boi verfasserin aut Alessandro Paiardini verfasserin aut In Biomolecules MDPI AG, 2013 12(2022), 2, p 201 (DE-627)735688915 (DE-600)2701262-1 2218273X nnns volume:12 year:2022 number:2, p 201 https://doi.org/10.3390/biom12020201 kostenfrei https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 kostenfrei https://www.mdpi.com/2218-273X/12/2/201 kostenfrei https://doaj.org/toc/2218-273X 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 2, p 201
language	English
source	In Biomolecules 12(2022), 2, p 201 volume:12 year:2022 number:2, p 201
sourceStr	In Biomolecules 12(2022), 2, p 201 volume:12 year:2022 number:2, p 201
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authorswithroles_txt_mv	Chiara Gasbarri @@aut@@ Serena Rosignoli @@aut@@ Giacomo Janson @@aut@@ Dalila Boi @@aut@@ Alessandro Paiardini @@aut@@
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callnumber-first	Q - Science
author	Chiara Gasbarri
spellingShingle	Chiara Gasbarri misc QR1-502 misc PepThreader misc protein–protein interactions misc protein–peptide interactions misc template-based modeling misc Microbiology Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach
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topic_title	QR1-502 Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach PepThreader protein–protein interactions protein–peptide interactions template-based modeling
topic	misc QR1-502 misc PepThreader misc protein–protein interactions misc protein–peptide interactions misc template-based modeling misc Microbiology
topic_unstemmed	misc QR1-502 misc PepThreader misc protein–protein interactions misc protein–peptide interactions misc template-based modeling misc Microbiology
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title	Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach
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title_full	Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach
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title_sort	prediction and modeling of protein–protein interactions using “spotted” peptides with a template-based approach
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title_auth	Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach
abstract	Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.
abstractGer	Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.
abstract_unstemmed	Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.
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title_short	Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach
url	https://doi.org/10.3390/biom12020201 https://doaj.org/article/b7c2a7d7429544bbad1524a2551efa16 https://www.mdpi.com/2218-273X/12/2/201 https://doaj.org/toc/2218-273X
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Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

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