1
|
Schulman A, Rousu J, Aittokallio T, Tanoli Z. Attention-based approach to predict drug-target interactions across seven target superfamilies. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae496. [PMID: 39115379 DOI: 10.1093/bioinformatics/btae496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/12/2024] [Accepted: 08/06/2024] [Indexed: 08/29/2024]
Abstract
MOTIVATION Drug-target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets. RESULTS This study introduces an attention-based method (called as MMAtt-DTA) to predict drug-target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185 676 drug-target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications. AVAILABILITY AND IMPLEMENTATION https://github.com/AronSchulman/MMAtt-DTA.
Collapse
Affiliation(s)
- Aron Schulman
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, 02150, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, 0379, Norway
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, 0372, Norway
| | - Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland
- Drug Discovery and Chemical Biology (DDCB) Consortium, Biocenter, Helsinki, 00014, Finland
- BioICAWtech, Helsinki, Helsinki, 00410, Finland
| |
Collapse
|
2
|
Wang J, Chen C, Yao G, Ding J, Wang L, Jiang H. Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review. Molecules 2023; 28:7865. [PMID: 38067593 PMCID: PMC10707872 DOI: 10.3390/molecules28237865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/13/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.
Collapse
Affiliation(s)
| | | | | | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Hui Jiang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| |
Collapse
|