Structural Biology

Structural Biology Tools

Use our X-ray crystallography and Cryo-EM compatible software to generate better structures

Our experience makes us a strong partner in structure-based drug discovery. With our software, you can easily reveal protein-ligand binding states and accelerate your workflow.



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The Future Is Here: Structural Biology + Computer-Aided Drug Discovery

We all know that your outputs are only as good as your inputs, and we have been working hard to create more effective avenues for structure-based drug discovery.

With the DivCon QM/MM X-ray crystallography and cryogenic electron microscopy software, the patented XModeScore method, and MovableType, you can generate a “feedback loop” between computational chemistry and structural biology to improve your understanding of protein:ligand chemistry in order to build and optimize better drugs—faster and cheaper.



Identify interactions that really matter with QM/MM

Here’s an example of how QuantumBio’s QM/MM can help you design better drug discovery campaigns

Conventional crystallographic refinement misses key protein-ligand intermolecular (and intramolecular) interactions—electrostatics, polarization, charge transfer, and even van der Waals and hydrogen binding—resulting in significant atomic coordinate uncertainties and structural errors.

With QuantumBio’s QM/MM, you get a true understanding of the interactions—creating an opportunity to make more informed decisions and, ultimately, more promising drug candidates.

With the DivCon QM/MM implementation, you can avoid structural errors associated with conventional methods that are difficult to detect:

  • False positives: inaccurate protein-ligand structures lead you to believe that you have found a ligand that optimizes structural interactions that don’t actually exist
  • False negatives: likewise, inaccurate structures result in overlooked molecular interactions that are critical for binding
  • Incorrect bond lengths, angles, and torsions lead to higher strain conformations that are impractical in real life

In the figure above, you’ll see two ligand interaction diagrams for the same protein-ligand pair: one generated with conventional PHENIX refinement (left) and one generated with QM/MM refinement (right). The black arrows depict interaction differences between the profiles. The upper arrow highlights that QM/MM can help avoid false positives. The lower arrow highlights that it can rescue false negatives. Importantly, the QM/MM-generated profile produced better binding affinity predictions: (GBVI/WSA score) increased from − 6.76 kcal/mol in the PHENIX refined structure to − 7.07 kcal/mol in the new QM/MM-refined structure. Read More

With QuantumBio’s QM/MM implementation, your drug discovery campaigns thrive since the right interactions are targeted for optimization.

False negatives and, in particular, false positives can be extremely expensive in your drug discovery campaign. When it comes to false positives, you think your ligand has an interaction with its target that doesn’t actually exist, you may believe that you have designed or discovered a molecule that addresses a critical active site residue interaction when in fact the interaction is unsatisfied. On the other hand, when it comes to false negatives, QM/MM rescues interactions that may be missing in the model. Together, a better understanding of accurate structure leads to a more accurate understanding of binding mode and binding affinity—leading to more efficient and more effective drug discovery campaigns.

Fortunately, QuantumBio’s QM/MM Hamiltonian helps produce structures that will guide you in the right direction, so you can avoid optimizing the wrong part of the ligand—saving time and costs.



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