Staurosporine

Staurosporine's influential role in cancer research and its analysis using Manoraa.

The field of drug design and discovery has exponentially grown with advancements in bioinformatics tools and computational methods. These tools have the potential to predict drug interactions, side effects, and even therapeutic uses (Ramharack & Soliman, 2018; Tiwari et al., 2023). However, there is a need to make these insights more accessible to researchers, clinicians, and the general public. This research seeks to bridge this gap using Manoraa, focusing on its capabilities and potential applications in drug design systems.

By knowing the different interactions between ligands and proteins it is possible to design such drugs that will be personalised for each patient depending on personal needs and preferences. Such medicine is called personalised medicine, or precision medicine (Delpierre & Lefèvre, 2023). It is quite a new approach that takes into consideration a patient's genomic, environmental and lifestyle information. Thanks to this, more precise diagnostics and treatments can be used. This approach enhances the efficacy of treatment while minimising adverse effects, providing a significant advancement over traditional "one-size-fits-all" healthcare models (Delpierre & Lefèvre, 2023). Often general treatments are developed largely relying on information on the genetics of people of European descent. This is because the largest databases are created using European DNA. Such treatment does not necessarily give the same results when used for people of different descent. This is the reason why personalised medicine is important. The completion of the Human Genome Project in 2003 marked a milestone in medical research, paving the way for personalised medicine. This project decoded the entire human genome, providing invaluable insights into genetic variations and their implications on health and disease (Lu et al., 2014). Understanding protein-ligand interactions is important to drug design and also, they are important in personalised medicine (Zhao & Bourne, 2022).

Proteins are complex macromolecules, which are crucial for people’s life. They interact with other molecules such as peptides or nucleic acids and also with small molecules, and ligands, such as oxygen, solvent or metal, influencing their activity. Ligands can bind to proteins with high specificity and affinity. The interaction between proteins and ligands is central to drug design, as drugs often function by binding to specific proteins, modulating their activity to treat diseases (Du et al., 2016). The drug design process begins with identifying a disease-related protein target. Researchers then search for ligands that interact with the target, optimizing these interactions to develop effective and safe drugs (Chen et al., 2023). This process involves a combination of computational modelling, laboratory testing, and clinical trials to validate the drug candidates (Zhou & Zhong, 2017).

Staurosporine, a microbial alkaloid isolated from Streptomyces staurosporeus, is a potent inhibitor renowned for its ability to effectively bind to and inhibit a broad spectrum of protein kinases (Tanramluk et al., 2009). Its significance lies in its role as a universal kinase inhibitor, providing invaluable insights into kinase selectivity and promiscuity. Despite its toxicity, staurosporine is a crucial tool for probe molecule research, offering a deeper understanding of the binding affinities essential for developing effective and selective therapeutic agents.

The inhibitor's binding intricacies are closely related to the size of the gatekeeper residue and the interaction between the initial glycine of the GXGXXG motif and the aspartate in the DFG loop. This relationship is pivotal for ensuring tight binding, with the number of hydrogen bonds around staurosporine's methylamine group being directly proportional to the binding tightness. Furthermore, the conserved structure within staurosporine’s binding site, particularly in the main chain of the hinge region, plays a vital role in its promiscuous inhibitory action (Tanramluk et al., 2009).

Research in drug design and protein-ligand interactions has grown substantially over time. One of the challenges faced by researchers is the efficient organisation and retrieval of this extensive data. Manoraa serves as a solution to this problem, functioning as a comprehensive bioinformatics tool for protein-ligand design. The platform offers features like fragment analysis and pocket conservation analysis, and it links ligands to protein pathways, SNPs, and expression data. With Manoraa, researchers have a centralised platform to better understand the complexities of drug design and protein-ligand interactions.

Manoraa is an advanced bioinformatics tool specifically designed to facilitate protein-ligand design. The platform, which stands for Mapping Analogous Nuclei Onto Residue and Affinity, utilises structure-based approaches to provide valuable insights into drug design. It employs algorithms that interpret drug binding affinities, which have been experimentally validated, to facilitate the understanding of protein-ligand interactions (Tanramluk et al., 2016). The Manoraa platform is not only an analytical tool but also a guide for drug design, providing algorithms that interpret experimentally proven drug-binding affinities. It introduces the concept of molecular anchors and influential distances, which are pivotal in guiding drug design processes. These features allow Manoraa to pinpoint potential side effects of drugs and identify target organs effectively. Manoraa is equipped with in-depth analysis capabilities, analysing pockets, frequently occurring atoms, influential distances, and active-site boundaries for active sites analysis. It can predict changes in binding affinity resulting from variations in distances, providing a valuable resource for researchers looking to improve binding affinity through structural modifications. Furthermore, Manoraa is linked to major biological databases, offering a web-based analysis tool for drug design. It provides a comprehensive analysis of protein-ligand binding affinities, utilising empirical analyses of numerous crystal structures and ligands.

The platform is particularly useful in the era of SARS-CoV-2, as it can analyse the frequency of atoms within main protease structures, aiding in the molecular design process (Tanramluk et al., 2022). Manoraa’s user-friendly interface supports various query methods, including searches by UniProt, SMILES expressions (Weininger, 1988), and PDB ligands using the 3-letter code. The platform is continuously updated, reflecting Manoraa's commitment to advancing the field of protein-ligand design and providing a breadth of data to users in drug design and bioinformatics fields (Tanramluk et al., 2016). With its augmented intelligence capabilities, Manoraa serves as an invaluable resource for researchers and professionals, facilitating the design of new ligands and providing new perspectives for medicine design. Its integration of big data and machine learning techniques makes it a centralised system that supports small-molecule drug discovery, providing fast, easy-to-use, and affordable tools to assist in the drug design process (Tanramluk et al., 2022).


References:

Chen, Y., Wang, Z., Wang, L., Wang, J., Li, P., Cao, D., Zeng, X., Ye, X., & Sakurai, T. (2023). Deep generative model for drug design from protein target sequence. Journal of Cheminformatics, 15(1). https://doi.org/10.1186/s13321-023-00702-2

Delpierre, C., & Lefèvre, T. (2023). Precision and personalized medicine: What their current definition says and silences about the model of health they promote. implication for the development of Personalized Health. Frontiers in Sociology, 8. https://doi.org/10.3389/fsoc.2023.1112159

Du, X., Li, Y., Xia, L., Ai, M., Liang, J., Sang, P., Ji, L., & Liu, Q. (2016). Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. International Journal of Molecular Sciences, 17(2). https://doi.org/10.3390/ijms17020144

Hirozane, Y., Toyofuku, M., Yogo, T., Tanaka, Y., Sameshima, T., Miyahisa, I., & Yoshikawa, M. (2019). Structure-based rational design of Staurosporine-based fluorescent probe with broad-ranging kinase affinity for kinase panel application. Bioorganic & Medicinal Chemistry Letters, 29(21), 126641. https://doi.org/10.1016/j.bmcl.2019.126641

Lu, Y.-F., Goldstein, D. B., Angrist, M., & Cavalleri, G. (2014). Personalized medicine and human genetic diversity. Cold Spring Harbor Perspectives in Medicine, 4(9). https://doi.org/10.1101/cshperspect.a008581

Ramharack, P., & Soliman, M. E. S. (2018). Bioinformatics-based tools in drug discovery: The cartography from single gene to Integrative Biological Networks. Drug Discovery Today, 23(9), 1658–1665. https://doi.org/10.1016/j.drudis.2018.05.041

Ryan, A. J., Gray, N. M., Lowe, P. N., & Chung, C. W. (2003). Effect of detergent on "promiscuous" inhibitors. Journal of medicinal chemistry, 46(16), 3448–3451. https://doi.org/10.1021/jm0340896

Tanramluk, D., Schreyer, A., Pitt, W. R., & Blundell, T. L. (2009). On the Origins of Enzyme Inhibitor Selectivity and Promiscuity: A Case Study of Protein Kinase Binding to Staurosporine. Chemical Biology & Drug Design, 74(1), 16-24. https://doi.org/10.1111/j.1747-0285.2009.00832.x

Tanramluk, D., Narupiyakul, L., Akavipat, R., Gong, S., & Charoensawan, V. (2016). MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein–ligand fragment interaction, pathways and SNPs. Nucleic Acids Research, 44(W1), W514-W521. https://doi.org/10.1093/nar/gkw314

Tanramluk, D., Pakotiprapha, D., Phoochaijaroen, S., Chantravisut, P., Thampradid, S., Vanichtanankul, J., Narupiyakul, L., Akavipat, R., & Yuvaniyama, J. (2022). MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances. Structure, 30(1), 181–189. https://doi.org/10.1016/j.str.2021.09.004

Tiwari, P. C., Pal, R., Chaudhary, M. J., & Nath, R. (2023). Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Development Research. https://doi.org/10.1002/ddr.22115

Weininger, D. (1988). Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of Chemical Information & Computer Sciences. https://pubs.acs.org/doi/10.1021/ci00057a005

You, K. (2023). Imatinib targeted cancer therapy. Retrieved from https://manoraa.icbs.mahidol.ac.th/gleevec/ on 19.10.2023

Zhao, Z., & Bourne, P. E. (2022). Harnessing systematic protein–ligand interaction fingerprints for drug discovery. Drug Discovery Today, 27(10), 103319. https://doi.org/10.1016/j.drudis.2022.07.004

Zhou, F., & Zhong, Z. (2017). Drug Design and Discovery: Principles and Applications. Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry, 22(2). https://doi.org/10.3390/molecules22020279