IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions
IntroductionIntrinsically disordered regions (IDRs) of proteins have traditionally been overlooked as drug targets. However, with growing recognition of their crucial role in biological activity and their involvement in various diseases, IDRs have emerged as promising targets for drug discovery. Des...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1627836/full |
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| author | Clara Shionyu-Mitusyama Satoshi Ohmori Subaru Hirata Hirokazu Ishida Tsuyoshi Shirai Tsuyoshi Shirai |
| author_facet | Clara Shionyu-Mitusyama Satoshi Ohmori Subaru Hirata Hirokazu Ishida Tsuyoshi Shirai Tsuyoshi Shirai |
| author_sort | Clara Shionyu-Mitusyama |
| collection | DOAJ |
| description | IntroductionIntrinsically disordered regions (IDRs) of proteins have traditionally been overlooked as drug targets. However, with growing recognition of their crucial role in biological activity and their involvement in various diseases, IDRs have emerged as promising targets for drug discovery. Despite this potential, rational methodologies for IDR-targeted drug discovery remain underdeveloped, primarily due to a lack of reference experimental data.MethodsThis study explores a machine learning approach to predict IDR functions, drug interaction sites, and interacting molecular substructures within IDR sequences. To address the data gap, stepwise transfer learning was employed. IDRdecoder sequentially generate predictions for IDR classification, interaction sites, and interacting ligand substructures. In the first step, the neural net was trained as autoencoder by using 26,480,862 predicted IDR sequences. Then it was trained against 57,692 ligand-binding PDB sequences with higher IDR tendency via transfer learning for predict ligand interacting sites and ligand types.ResultsIDRdecoder was evaluated against 9 IDR sequences, which were experimentally detailed as drug targets. In the encoding space, specific GO terms related to the hypothesized functions of the evaluation IDR sequences were highly enriched. The model’s prediction performance for drug interacting sites and ligand types demonstrated the area under the curve (AUC) of 0.616 and 0.702, respectively. The performance was compared with existing methods including ProteinBERT, and IDRdecoder demonstrated moderately improved performance.DiscussionIDRdecoder is the first application for predicting drug interaction sites and ligands in IDR sequences. Analysis of the prediction results revealed characteristics beneficial for IDR-drug design; for instance, Tyr and Ala are preferred target sites, while flexible substructures, such as alkyl groups, are favored in ligand molecules. |
| format | Article |
| id | doaj-art-e78ce32e8bbd48a0ad0c1393d83bf39d |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-e78ce32e8bbd48a0ad0c1393d83bf39d2025-08-20T03:28:01ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-07-01510.3389/fbinf.2025.16278361627836IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regionsClara Shionyu-Mitusyama0Satoshi Ohmori1Subaru Hirata2Hirokazu Ishida3Tsuyoshi Shirai4Tsuyoshi Shirai5Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga, JapanDepartment of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga, JapanFaculty of Data Science, Shiga University 1-1-1 Banba, Hikone, Shiga, JapanDepartment of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga, JapanDepartment of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga, JapanFaculty of Data Science, Shiga University 1-1-1 Banba, Hikone, Shiga, JapanIntroductionIntrinsically disordered regions (IDRs) of proteins have traditionally been overlooked as drug targets. However, with growing recognition of their crucial role in biological activity and their involvement in various diseases, IDRs have emerged as promising targets for drug discovery. Despite this potential, rational methodologies for IDR-targeted drug discovery remain underdeveloped, primarily due to a lack of reference experimental data.MethodsThis study explores a machine learning approach to predict IDR functions, drug interaction sites, and interacting molecular substructures within IDR sequences. To address the data gap, stepwise transfer learning was employed. IDRdecoder sequentially generate predictions for IDR classification, interaction sites, and interacting ligand substructures. In the first step, the neural net was trained as autoencoder by using 26,480,862 predicted IDR sequences. Then it was trained against 57,692 ligand-binding PDB sequences with higher IDR tendency via transfer learning for predict ligand interacting sites and ligand types.ResultsIDRdecoder was evaluated against 9 IDR sequences, which were experimentally detailed as drug targets. In the encoding space, specific GO terms related to the hypothesized functions of the evaluation IDR sequences were highly enriched. The model’s prediction performance for drug interacting sites and ligand types demonstrated the area under the curve (AUC) of 0.616 and 0.702, respectively. The performance was compared with existing methods including ProteinBERT, and IDRdecoder demonstrated moderately improved performance.DiscussionIDRdecoder is the first application for predicting drug interaction sites and ligands in IDR sequences. Analysis of the prediction results revealed characteristics beneficial for IDR-drug design; for instance, Tyr and Ala are preferred target sites, while flexible substructures, such as alkyl groups, are favored in ligand molecules.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1627836/fullintrinsically disordered proteinsneural netsequence-based prediction methodstructural bioinformaticsdrug design |
| spellingShingle | Clara Shionyu-Mitusyama Satoshi Ohmori Subaru Hirata Hirokazu Ishida Tsuyoshi Shirai Tsuyoshi Shirai IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions Frontiers in Bioinformatics intrinsically disordered proteins neural net sequence-based prediction method structural bioinformatics drug design |
| title | IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| title_full | IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| title_fullStr | IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| title_full_unstemmed | IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| title_short | IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| title_sort | idrdecoder a machine learning approach for rational drug discovery toward intrinsically disordered regions |
| topic | intrinsically disordered proteins neural net sequence-based prediction method structural bioinformatics drug design |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1627836/full |
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