https://orcid.org/0000-0003-3139-3700
Abstract
Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines.
pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.
1 Introduction
Enzymes with buried active sites contain structural pathways used by substrates and the products of reactions to bind or unbind. These pathways also create an optimal microenvironment to boost the catalytic properties of the enzyme. Therefore, these pathways play an essential role in the enzyme’s activity, selectivity, specificity, and stability (Brezovsky et al. 2016, Kaushik et al. 2018). The pathways can be distinguished into two classes: (i) tunnels with one opening connecting the active site with the outer environment; (ii) channels with openings on both sides, which facilitate the transport of ligands through the whole protein molecule (Gora et al. 2013).
The state-of-the-art computational methods for studying ligand transport, i.e. molecular dynamics, are time-demanding and not practical for screening purposes. Therefore, several methods were developed recently to facilitate the study of these processes. These methods apply approximations to increase the computational speed at the cost of precision. The tools differ in the approach and the required input and provided output (Vavra et al. 2022). For example, CaverDock (Filipovic et al. 2019, Vavra et al. 2019) and SLITHER (Lee et al. 2009) are based on docking engines. MoMA-LigPath (Devaurs et al. 2013) and ARR-RRT (Nguyen et al. 2018) rely on space search robotic algorithms, and GPathFinder (Sánchez-Aparicio et al. 2019) uses a genetic algorithm. Recently, these approximative tools started gaining popularity due to their successful usage in practical applications in protein engineering. Our in-house method, CaverDock, was successfully used in a comparative study of enzyme tunnels (Pinto et al. 2019), a virtual screening campaign (Pinto et al. 2021), the design of improved catalyst (Rapp et al. 2021), engineering of an enzyme with novel function (Papadopoulou et al. 2021), the study of enantioselectivity (Knez et al. 2020), or inhibition of a metabolic pathway (Singh et al. 2021). CaverDock was developed to be easy to set up and use, but based on the feedback from users, further work was needed to facilitate the setup of screening pipelines. Here we present pyCaverDock, Python 3 API, which provides means to automatize and simplify both single and batch CaverDock calculations.
2 Materials and methods
2.1 CaverDock
CaverDock is a tool designed for rapid analysis of ligand transport of a ligand molecule from the outside environment into the receptor binding site (or vice versa). The current version of CaverDock uses CAVER 3.02 (Chovancova et al. 2012) for the pathway identification and AutoDock Vina 1.1.2 (Trott and Olson 2010) as the docking engine. The input for the calculation is a receptor, ligand, and precomputed tunnel geometry. The CaverDock can calculate ligand passage through any type of 3D space as long as it is prepared as a set of spheres that are not completely inside each other. Generally, an identified tunnel is discretized into a set of discs which are used to guide the ligand through the protein during the simulation. In each step of the CaverDock calculation, the ligand is constrained to a disc, and the docking algorithm optimizes the conformation. Then the ligand is moved to the next disc and the process is repeated until the molecule reaches the end of the tunnel. The outputs are the ligand’s trajectory and the energetic profile of the process. The calculation time is tens of minutes on average, making CaverDock suitable even for virtual screening.
2.2 pyCaverDock
In the original CaverDock distribution archive, the CaverDock binary was accompanied by several standalone scripts that help the users prepare the input data and plot the resulting profiles. However, these scripts cover only the most crucial and basic tasks, which makes the intended use of CaverDock for large-scale screenings difficult as the users need to develop their results parsing and handling. Therefore, we developed a new Python 3 library, pyCaverDock which significantly extends the capabilities of the original scripts.
For users without any advanced needs, we provide three new standalone scripts. The script cd-screening allows the execution of large-scale screenings with multiple receptors, tunnels, and ligands (Fig. 1). The only inputs needed are receptors and tunnels in PDB format, ligands in MOL2 format and screening definition in the YAML file. The structured YAML file uses specifically named variables to define all the input files and CaverDock settings needed for the screening. The screening experiment consists of receptor–tunnel pairs and a set of ligands automatically combined based on the YAML file definition. Suppose the user adds more ligands to the list, pauses, or terminates the calculation. In that case, the screenings can be restarted from the last calculated CaverDock run due to the implementation of data caching. Moreover, several screening runs can be launched from a single YAML file. The second script, cd-analysis, can be used to run single CaverDock calculations. In this case, the input files are specified in the command line and all the outputs are provided automatically. The third script cd-analyseeprofile provides simple extraction of important energy values from output energy profiles. The user can save energies from specific disks or parts of the profile.
Schematic visualization of batch calculation with pyCaverDock screening script. The scheme shows the individual parts of the implemented pipeline: Input files and configuration (blue), input conversion and preparation (green), experiment definition (orange), CaverDock calculation (red), and the provided output (black). The dashed line encompasses the black box of the screening script.
Users interested in setting up their customized pipelines can write their scripts using the provided API functions and classes. For a single CaverDock run, they can use the basic functions for handling input or output. Screening batch calculations can be done through the Experiment class, which needs input files defined in a YAML file. The rest of the settings can be either defined in the YAML file or in a custom script. Example pipelines with detailed descriptions are available in the online documentation. The package provides generally applicable results parsers, allowing users to process calculated profiles directly in their code. Moreover, it brings advanced support for batch processing with results caching. This allows rapid development of highly customized screening pipelines with reliable reuse of already calculated data and advanced plotting functionality for easy and quick results assessment. These additions reduce the number of operations and saved files in large screenings. Since most of the data are handled in computer memory, such calculations are more computationally efficient.
3 Conclusions
We developed a novel Python API for an established approximative method CaverDock to simplify the simulation and analysis of ligand transport through tunnels and channels. The API provides an easy way to prepare single or batch calculations. Moreover, a new robust script enables combinatorial preparation of the screening batch, runs all calculation steps, and collects all results at once. The caching features allow continuation without the need to rerun preparation steps or CaverDock calculations in the cases of unexpected stops or failures of used devices or after adding new molecules to the screened dataset. pyCaverDock is easy to use and can bring the ligand transport analysis via CaverDock to the broader scientific community.
Conflict of interest
None declared.
Funding
This work was supported by the Czech Ministry of Education [grant numbers INBIO—CZ.02.1.01/0.0/0.0/16_026/0008451, RECETOX RI—LM2023069, ELIXIR CZ—LM2023055, e-INFRA—LM2018140, and TEAMING—CZ.02.1.01/0.0/0.0/17_043/0009632]; the European Commission [grant number TEAMING—857560]; the Technology Agency of the Czech Republic [grant numbers Permed—TN01000013 and TN02000109]; National Institute for Cancer Research funded by the European Union—Next Generation EU [EXCELES—LX22NPO5102]; and the Grant Agency of the Czech Republic [grant number 20-15915Y]. O.V. is the recipient of a Ph.D. Talent award provided by Brno City Municipality.
Data availability
The pyCaverDock library is distributed using Python’s PyPi repository at https://pypi.org/project/pycaverdock/ and as a part of Apptainer/Singularity image for simple and efficient deployment. The documentation with API description, downloads, and examples is available online at https://loschmidt.chemi.muni.cz/caverdock.
References
Author notes
Ondrej Vavra and Jakub Beranek Equal contribution.
