Martyna Ogos

Despite widespread use of molecular dynamics (MD) to refine docking poses, no large-scale statistical validation exists for metalloproteins like Cytochrome P450 (CYP450). We present the first statistically rigorous benchmark of docking engines and MD refinement on CYP450 systems, evaluating AutoDock Vina, GNINA, GalaxyDock2-HEME, and RoseTTAFold All-Atom (RFAA) across crossdocking scenarios. Our analyses revealed that short MD simulations provide no statistically significant geometric refinement toward experimental structures. Using automated interaction comparison that categorises protein-ligand contacts relative to crystal references, we found this behaviour showed no significant dependence on ligand size or flexibility. Among docking methods, RFAA achieved geometric drift approximately three times lower than AutoDock Vina. These findings provide clarity regarding the efficacy of MD refinement for organometallic complexes. The automated framework developed for this study (DOCKTOPUS/CYCLOPS packages) enables the high-throughput simulations and statistical testing necessary to rigorously validate computational methods and reduce publication bias inherent in stochastic simulations.

Molecular docking is a standard tool for predicting ligand binding, yet refining these poses via molecular dynamics (MD) remains challenging to automate for metalloproteins like Cytochrome P450 (CYP450). Here, we introduce DOCKTOPUS (Dock to P450) and CYCLOPS (CYtochrome Complex Ligand Optimization with Protein Simulation) Python packages designed to automate the docking and molecular dynamics setup for CYP450-ligand complexes. These tools integrate seamlessly with representative predictive engines AutoDock Vina, GNINA, GalaxyDock2-HEME, and RoseTTAFold All-Atom (RFAA) by automatically handling the complex parameterisation of the covalently bound heme-thiolate cofactor and ligand topologies, while also providing capabilities to filter out unphysical ligand geometries generated by RFAA. We validated the pipeline through a medium-sized case study to enable a rigorous statistical analysis. In crossdocking scenarios RFAA achieved a mean geometric drift relative to experiments approximately three times lower than that of AutoDock Vina. Our structural and pharmacophoric analyses revealed that subsequent MD simulations provide no statistically significant geometric refinement towards the experimental crystal structure. This behaviour was remarkably consistent: we observed no significant differences by specific interaction type or ligand flexibility. These findings suggest that short MD refinement with contemporary classical force fields for organometallic systems might have limited effects beyond pose validity. Furthermore the DOCKTOPUS and CYCLOPS packages offer a robust platform for statistical testing of docking and molecular dynamics effects.

Docking and molecular dynamics are commonly used methods in drug development for the prediction of interactions between the drug in question and the target protein. When used together, docking studies can provide starting structures for molecular dynamics, which can improve the relatively low accuracy of docking but at greater computational cost. When the structure contains a cofactor with covalently bound metal atom, few automated methods are capable of setting up the system to perform molecular dynamics simulations. Another limitation that can be encountered is the parametrization of the ligand, which is not always easy to set up and automate using popular suites, such as AMBER or GROMACS. Here, we propose an automated workflow for setting up and running the simulation, providing a seamless integration for both flexible and rigid protein docking engines. The automatic setup works for around 90% of all ligands for rigid docking engines, with this number decreasing for RoseTTAFold All-Atom (flexible docking engine) to around 30% due to its known problem with hallucination of the ligand conformation.

Alzheimer’s disease, the most common form of dementia, is characterized by the deposition of amyloid plaques and neurofibrillary tangles in the brain, leading to the loss of neurons and a decline in a person’s memory and cognitive function. As a multifactorial disease, Alzheimer’s involves multiple pathogenic mechanisms, making its treatment particularly challenging. Current drugs approved for the treatment of Alzheimer’s disease only alleviate symptoms but cannot stop the progression. Moreover, these drugs typically target a single pathogenic mechanism, leaving other contributing factors unaddressed. Recent advancements in drug design have led to the development of multi-target-directed ligands (MTDLs), which have gained popularity for their ability to simultaneously target multiple pathogenic mechanisms. This paper focuses on analyzing the activity, mechanism of action, and binding properties of the anti-Alzheimer’s MTDLs developed between 2020 and 2024.