In silico binding assay
Molecular dynamics for binding mode prediction
What is it?
There are many great and fast docking software solutions to predict the binding mode of a ligand to its target, however, in some scenarios, like those where water molecules or protein flexibility play a key role, docking software can fail to predict reasonable poses.
In contrast, molecular dynamics simulations naturally account for these variables and can easily capture the formation of water bridges and networks, induced fit effects or the opening of cryptic pockets. That was precisely the case in a collaboration with Pfizer, where docking failed to produce a pose that could explain the behaviour of some mutants, while our MD protocol succeded.
With our in silico approach, we can sample the binding mode of a ligand to its target in a reasonable amount of time (2-3 weeks), and estimate its binding kinetics and pathway.
What is the value of this service?
- Rational drug design: Knowing the binding mode of your ligand is the first step towards structure-based design and lead-optimization.
- Patent reverse engineering: Although patents disclose the 2D structure of the compound and the target to which it binds, its binding mode might not necessarily be disclosed. We can help you finding it.
- Estimate binding affinity and kinetics: One of the key properties of a drug is its binding affinity, we can estimate KD, Kon and Koff with markov state models.
- Binding pathway: In addition to the binding mode, you will obtain the pathway that the ligand follows from bulk to bound state.
What will you obtain?
- A PyMol or VMD scene with binding mode predictions and binding pathway.
- The full simulations (.xtc and .pdb files).
- An extensive report summarizing the structural insights obtained.
- Conference calls with the team to discuss the results.
- Martinez-Rosell, G., Lovera, S., Sands, Z. A., & De Fabritiis, G. (2020). PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations. Journal of Chemical Information and Modeling, 60(4), 2314–2324. https://doi.org/10.1021/acs.jcim.9b01209
- Arcon, J. P., Defelipe, L. A., Modenutti, C. P., López, E. D., Alvarez-Garcia, D., Barril, X., Turjanski, A. G., & Martí, M. A. (2017). Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions. Journal of Chemical Information and Modeling, 57(4), 846–863. https://doi.org/10.1021/acs.jcim.6b00678