Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs
Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is diffic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2023.1193025/full |
| _version_ | 1797819792105668608 |
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| author | Haruki Yamane Takashi Ishida |
| author_facet | Haruki Yamane Takashi Ishida |
| author_sort | Haruki Yamane |
| collection | DOAJ |
| description | Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches. |
| first_indexed | 2024-03-13T09:27:48Z |
| format | Article |
| id | doaj.art-0945d9aa9021456f9bbd26e1a5099f94 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-03-13T09:27:48Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-0945d9aa9021456f9bbd26e1a5099f942023-05-26T04:36:44ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472023-05-01310.3389/fbinf.2023.11930251193025Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRsHaruki YamaneTakashi IshidaClass A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.https://www.frontiersin.org/articles/10.3389/fbinf.2023.1193025/fullcompound-protein interactionclass A GPCRdeep learningligand binding sitetransmembrane regionextracellular loop |
| spellingShingle | Haruki Yamane Takashi Ishida Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs Frontiers in Bioinformatics compound-protein interaction class A GPCR deep learning ligand binding site transmembrane region extracellular loop |
| title | Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs |
| title_full | Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs |
| title_fullStr | Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs |
| title_full_unstemmed | Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs |
| title_short | Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs |
| title_sort | helix encoder a compound protein interaction prediction model specifically designed for class a gpcrs |
| topic | compound-protein interaction class A GPCR deep learning ligand binding site transmembrane region extracellular loop |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2023.1193025/full |
| work_keys_str_mv | AT harukiyamane helixencoderacompoundproteininteractionpredictionmodelspecificallydesignedforclassagpcrs AT takashiishida helixencoderacompoundproteininteractionpredictionmodelspecificallydesignedforclassagpcrs |