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Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

Doğan, Tunca; Akhan Güzelcan, Ece; Baumann, Marcus; Koyas, Altay; Atas, Heval; Baxendale, Ian R.; Martin, Maria; Cetin-Atalay, Rengul

Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases Thumbnail


Tunca Doğan

Ece Akhan Güzelcan

Marcus Baumann

Altay Koyas

Heval Atas

Maria Martin

Rengul Cetin-Atalay


Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at:


Doğan, T., Akhan Güzelcan, E., Baumann, M., Koyas, A., Atas, H., Baxendale, I. R., …Cetin-Atalay, R. (2021). Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases. PLoS Computational Biology, 17(11), Article e1009171.

Journal Article Type Article
Acceptance Date Nov 9, 2021
Online Publication Date Nov 29, 2021
Publication Date 2021
Deposit Date Jan 6, 2022
Publicly Available Date Jan 7, 2022
Journal PLOS Computational Biology
Print ISSN 1553-734X
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 17
Issue 11
Article Number e1009171
Public URL


Published Journal Article (3.7 Mb)

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Copyright Statement
© 2021 Doğan et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

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