Free Energy: MovableType
Quickly identify the most promising drug candidates prior to chemical synthesis using high-throughput virtual screening software.
Are you looking for software that will help you identify the best protein:ligand pairs? From scientists in academia to those in pharma, MovableType and the QuantumBio team can help answer your most important questions so you can identify lead compounds faster and accelerate drug discovery.
- Accurately assess drug-like molecules at much lower computational cost
- Quickly calculate binding modes, conformations, and affinity—even for large, flexible molecular complexes
- Easily integrate the software into your current workflow with our user-friendly interface and MOE compatibility
- Use your docker of choice, including the built-in MovableType docking module
- Optimize your methods and get your questions answered quickly with our responsive team—We are here to help!
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Streamline Drug Discovery with MovableType
Instead of relying on molecular dynamics simulations, MovableType utilizes a combination of a novel “smearing” algorithm coupled with molecular docking to perform the sampling necessary to solve the partition function for a protein:ligand complex.
MovableType is broken down into several key modules:
MTScoreE: Ensemble-based binding affinity predictions within 30 minutes per compound
MTScoreES: EndState-based binding affinity predictions with the ability to process thousands of compounds per CPU-hour
MTDock: Our built-in protein:ligand docking module
MTFlex: Protein loop and rotomer modeling (target conformation generation) withside chain flexibility considered.
MTConfSearch (MTCS): Ligand conformation generation, supporting flexible-receptor+flexible-ligand scoring
When you use our software, we guide you every step of the way. From integrating the software to optimizing your methods to troubleshooting your simulations, we rapidly get you the solutions you need to propel your projects forward.
Learn more about MovableType by contacting our team!CONTACT US
Challenges in Computer-Aided Drug Design
Drug R&D is expensive and time-consuming, and to remain ahead of the competition companies need to minimize time devoted to compounds destined to fail and quickly identify the most promising leads.
MovableType utilizes a unique approach to free energy calculations to address key challenges with conventional computer-aided drug design tools.
At QuantumBio, we are constantly improving our platform and are a dedicated collaborator so you can discover and design better drugs faster. Our iterative approach delivers software that facilitates better workflows and methods—and most importantly helps you identify the most promising candidates prior to synthesis.
If you do not see results comparable to those provided by our internal calculations and our publications, we want to hear from you so we can help you with the process. Contact Us today and we’ll schedule a “virtual visit” to discuss your goals and your chosen protocol.
Frequently Asked Questions
Learn more about MoveableType by reading through the full list of FAQs.
Why do we use docking? What function does docking serve?
The term of art we are looking for is “landscape minima.” The critical component is not docking per se, but the accuracy and applicability of the landscape minima we are supplying to the MovableType (MT) calculation. The more accurate/representative the landscape minima provided, the better our predictions.
If we have an experimental (X-ray, NMR, or CryoEM) structure, it can be used as a landscape minimum and, assuming the experimental structure is accurate, it is possible that no further poses are required. However, when we are working with novel compounds which have not yet been made in a test tube, we need a computational method to mimic or represent the experimental structure(s) for the ligand.
We can use molecular dynamics (MD) or docking or even hand placement. Of course MD and hand placement tend to take the most time making them impractical for higher throughput screening. Therefore, our default approach is to pair MT with a docker of some sort. In our experience, your best chance at success with MT is to let the docking software you choose spend the time it needs to generate its best landscape minima instead of trying to make docking as fast as possible. MT—when compared to other free energy methods—will more than make up for the extra docking time.
What dockers can I use with this software?
In our experience, MOE (with our supplied/SVL settings) is the most compatible 3rd party docker available, but MTScoreE has been successfully used "in the field" with MOE, GLIDE, GOLD, OpenEye, DOCK, and other docking functions, and the the method has been implemented to be docker agnostic.
You should choose the right docker for your experiment and you should take particular note of the docker-specific settings required to maximize predictability (e.g. be sure in situ optimization of each pose is turned ON and if possible, use induced fit docking—which modifies the target as well as the ligand—instead of rigid-target docking). Read more on docker compatibility.
What is the benefit of ensemble scoring?
There are critical benefits of MTScoreE (E = Ensemble) scoring versus other, more conventional scoring methods. Our method takes into account numerous conformers, as opposed to one single pose, and uses a little (or a lot) of information from each pose using what’s called a boltzmann average.
Conventional scoring functions rely on the user providing a single, accurate, active protein:ligand complex pose in order to calculate the binding affinity. The keyword there is accurate. If it is inaccurate, the scoring function will likely be wrong as well. It is difficult, and often expensive, to obtain the right pose (in fact, usually the score function is used to determine which score function is correct).
“In the test tube” or “in the body”, true binding affinity between a protein and its ligand is dependent on many poses--an ensemble of poses. Usually the ligand doesn’t bind a single conformation. And often these conformations are similar to each other, but sometimes they can be quite different. By accepting a series of conformations or poses, MT is able to bring together the information contained in the set and calculate a binding free energy.
For more information
- How does MTScoreE w/docked poses compare to MTScoreES w/X-ray poses? See: MovableType: Frequently Asked Questions
- How important are quality docked poses to the MT process? See: MovableType: Frequently Asked Questions