Speaker
Description
Docking is a computational method which searches through conformational space to find an optimal pose of a molecule in a specified environment within a protein target. It is designed to scan vast libraries of molecules against proteins to recreate binding geometry which is later used in drug design.
There are a lot of docking engines available, and it is of utmost importance to choose the right one for a specified task. It requires many engines to try, many trials and, we all know that, many errors even before we start to use the software. The question is: how to avoid it?
The core of the presentation focuses on escaping dependency hell for installation and configuration of software using the example of docking engines. Traditional software deployment requires one to recreate the development environment which, in general, might be hard to do especially for novel machine learning models used in the scientific process. Containerization encapsulates the entire software environment, ensuring that the software can run consistently regardless of the underlying infrastructure.
This work focuses on cytochromes P450 enzymes, a major drug metabolizing enzyme from the liver. It is reported that 75% of all commercially available drugs is metabolized by only few out of many of those enzymes in humans [1]. The CYPs contains an important structural feature for docking simulations: active site is flexible and therefore presents a challenge for docking with rigid protein approximation which shows limited reliability [2,3].
We will present our simulation workflow, discuss about the containers and how they can improve deployment, provide comments and tips for apptainer containers on IT4I, and finally discuss benchmark results of such high-throughput simulations using significantly differing docking software [4-8].
[1] Brändén, G., Sjögren, T., Schnecke, V., & Xue, Y. (2014). Structure-based ligand design to overcome CYP inhibition in drug discovery projects. Drug Discovery Today, 19(7).
[2] Lokwani, D.K.; Sarkate, A.P.; Karnik, K.S.; Nikalje, A.P.G.; Seijas, J.A. Structure-Based Site of Metabolism (SOM) Prediction of Ligand for CYP3A4 Enzyme: Comparison of Glide XP and Induced Fit Docking (IFD). Molecules 2020, 25, 1622.
[3] Matthew R. Masters, Amr H. Mahmoud, Yao Wei, and Markus A. Lill, Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility, Journal of Chemical Information and Modeling 2023 63 (6), 1695-1707
[4] C. Lee, J. Yang, S. Kwon, C. Seok. GalaxyDock2-HEME: Protein–ligand docking for heme proteins J. Comput. Chem. 2023, 44(14), 1369
[5] Rohith Krishna et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom.Science 384,eadl2528(2024).
[6] Eberhardt, J., Santos-Martins, D., Tillack, A.F., Forli, S. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling.
[7] Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.
[8] McNutt, A.T., Francoeur, P., Aggarwal, R. et al. GNINA 1.0: molecular docking with deep learning. J Cheminform 13, 43 (2021).
