QUELO Updates & Roadmap

For more information on QUELO-G, visit the webpage: https://qsimulate.com/products/quelo-g

New softcore potential produces better results, especially for larger ligands with relatively large mutations (12+ atoms differ) with conformational flexibility.

In development, learn more here:

• Center of star map based on provided reference ligand
• Most computationally efficient mutation map - minimum number of mutations required to span ligand set

• Uploaded 3D conformers used to determine MCS, calculate similarity scores, and select mutations that make up the mutation map
• Consistent with alignment procedure - similarity scores in the mutation map reflect what actual aligned structures will look like for each mutation

• Extra mutations are generated automatically to attach new ligands to the existing map, with the option to modify them by hand. New mutations and ligands appear in blue, while old mutations and ligands are gray.
• Saves time and compute spent on an entirely new and partially redundant batch

• Adds handling for RNA for both MM- and QM-FEP within QUELO
• Brings the accuracy of QM-FEP to these biomolecules for the first time. The underlying MM forcefield (amber14), for MM-FEP and residues outside the QM region, proves to be well parameterized for RNA compared to others. QM-FEP builds on top of this: the common pi-stacking interactions and complex web of hydrogen bonds associated with RNA and DNA are better modeled with QM.

• After ligand upload, select ligand to see rotatable bonds. A second conformer is generated with the selected bond rotated 180 degrees. This can be repeated for any number of bonds.
• Allows better conformational sampling, and therefore more accurate results, for ligands with high barriers to rotation. This especially helps ligands with asymmetric conjugated ring systems.

• Select solvent already generated and upload PDB of target in solvent box. PDB can contain equilibrated membrane-bound target.
• Explicit, fully atomistic membrane, while computationally expensive to model, can be required to get accurate results for some systems. In particular, systems with an active site close to the protein-membrane interface.
  • Also supports solvent boxes generated with other software, with or without membrane, giving users full control over the solvation process.

• Select perfectly prealigned after uploading ligand structures. Alignment will not move ligand structures at all and define single and dual regions off provided coordinates. The single region comprises all groups of two or more topologically contiguous heavy atoms (and bound hydrogen) within 0.1 angstrom of a topologically identical atom on the other ligand.
• Gives full control over single and dual region definition and certainty that all ligand conformers are not changed at all during alignment. Should not be used unless the atoms in the desired single region overlap almost exactly for each mutation.

• Utilizes grand canonical Monte Carlo approach to generate and test different water configurations within buried, solvent-shielded regions of the target. Considers even ligand-dependent water sites and places water that disappears throughout the mutation into the dual-region as appropriate.
• Improves accuracy, particularly when a mutation leaves space for a water molecule in a buried pocket. Required to get decent results in some cases

• Runs vacuum phase FEP calculations based on structures obtained from unbound simulations. The difference between this dG in vacuum and the dG in solvent is calculated to obtain a ddG of solvation for each mutation.
• Solvation energies provide insight into whether improved binding for certain ligands is driven by affinity for the target or decreased solubility. If a mutation greatly increases solubility alongside improved binding, it may have less target specificity. This is important information when selecting lead compounds to move on to the next phase.

• Command line version of QUELO with all functionality of the GUI.
• Improves ease of use in some cases, particularly when integrating QUELO into an existing pipeline, working with inputs in certain formats (like from a database), or running high volumes of calculations.