#11thICCS Moira Michelle Rachman on Automated frag evolution (FrEvolAted) applied to frags bound to NUDT21
Fragment chemical space is a lot smaller and easily to explore a larger chemical space more efficiently. Smaller and so more likely to bind, higher hit rates, more chemical tractable.
Some strategies in FBDD. Fragment linking, merging and growing - these are diff approaches. Growing is the most popular. Shows example of this from Astex (Murray ACS MedChemLett 2015, 6, 798)
Try to automate frag evolution while taking into a/c syn accessibility. Will alleviate the cost of doing fragment growth.
Start with ZINC "in stock", 15M. Would like to move to 200M and billions of cmpds. Start with sim search between ref and library for ligands of similar size.
Uses MACCS and Morgan for sim search. Then LigPrep for those selected. Then MCS superposition between fragments and their similarity hits. Tether docking (rDOCK) keeping the MCS in place. Some attrition at this point - bad scoring in docking.
Final step is MMGBSA minisation to correct docking scores. Finally dynamic undocking (DUck). To see if the interaction we want is present and how strong it is. Now we have children cmpds which are used in the next iteration.
We select the children with the most parents and also consider the parents' scores. One day per iteration depending on how GPUs are available.
We keep growing 2 heavy atoms at a time. My job was to make the platform more robust across diff targets. For example, for XChem, a collab with Anthony Bradley and Frank von Delft.
NUDT21 is a cleavage factor involved in pre-mRNA processing. Only Nudix protein that binding signaling molecule and RNA thru seq specific recog. Suggests potential for a role of small molecules in reg of mRNA processing.
Approach 1: virtual screen of 1100 cmpds based on synthetic feasibilty, followed by visual inspection, 19 molecules elected for synthesis/testing. Also approach 2 based on purchasability. Approach 3: the fragment evolution approach from ZINC in-stock.
Shows visualization of the relationship between one parent and all of its children, highlight the MCSes.
When superimposing them, there is attrition due to conformation strain or RMSD is poor. We see that very early on we are removing "bad children" so that they don't become "bad parents" in the next generation.
Docking should not discriminate between families. In every iteration, scores are increasing. In every iteration we are choosing children that score better than their parents.
MMGBSA scores are increasing over time.
Protocol is able to scaffold hop. Efficient at removing ligands. More novelty compared to approach 1.
Protocol is able to scaffold hop. Efficient at removing ligands. More novelty compared to approach 1.
Areas of improvement. We sacrifice ligands for the sake of time. We converge towards certain areas of chemical space, leading to less hits for other spaces. Plan to use larger datasets to improve this.
