Graduate Category: Physical and Life Sciences Degree Seeking: PhD Abstract ID# 2277 Using Computation to Identify Catalytic Features of and to POOL Server: www.pool.neu.edu Engineer Enzymes with Enhanced or Altered Function DNA Lab Site: www.dna.neu.edu 205-R2-CX-0011 Timothy A. Coulther, Israel Adam, Penny J. Beuning, Mary Jo Ondrechen (TAC) NSF MCB-1158176 ORG Site: nuweb15.neu.edu/org NSF-MCB-1517290 Retro-aldolases: de novo Engineering a replicative polymerase for accurate damage bypass The catalytic properties of enzymes make them useful for wide application, THEoretical Microscopic Anomalous designed and evolved in Identify mutations that promote the accurate lesion conformation while such as in medicine, biofuel production, or sample analysis. However, we are TItration Curve Shapes1 laboratories3,4,5,6,7 retaining catalytic properties currently restricted to the enzymes that nature has to offer, limiting their Typical H-H Accurate Mutagenic applicability. Unfortunately, most initial design attempts Curve Identification of catalytic residues G stays G G becomes T have resulted in very low activities or no activity at all. Long-term directed evolution computationally for understanding activity and further engineering experiments have increased enzyme activities, but these require Approach years, high cost, and sometimes sophisticated equipment, that limit their development and use. A major issue is the sheer size of mutational space, as there Perturbed H-H

48 Charge are over 1.0x10 possibilities for a with 200 residues. The goal of our 4 Curves research is to use computation to reduce the number of possibilities, identifying a smaller number of potential designs that are more likely to be successful. 0 4 7 7 The Ondrechen Lab has previously developed the catalytic residue predictors THEMATICS and POOL, which are now being applied to build pH important chemical properties into designed enzymes. One application of the • Perturbation indicative of 5 7 methods is on the class of de novo designed enzymes called Retro-aldolases, where catalytic role we have identified residues that contribute to their catalytic abilities. Another case • Quantified through moments 5 Opportunity is a natural enzyme, a DNA polymerase from Thermus aquaticus, which already has • 4th moment (µ4) good Retro-aldolase Cleavage Reaction activity but may not accurately copy damaged DNA. Through combination of our 2 Polymerase Fidelity indicator 8,9 computational methods with others such as docking, we have identified positions 6 Assay • Higher is “better” for to mutate to alter fidelity but still retain catalytic activity. High-throughput assays Polymerase Activity will be carried out to test our focused variants for activity and fidelity. Assay10,11,12

Increasing Moments Across Identification of Residues • Enzymes are clean and selective catalysts Retro-aldolase Evolution of Evolving Retro-aldolases Identification of 10,11,12 • Designed enzymes generally result in low activity Cleavage of (+)-methodol Residue Scan of RA95.5-5 evolution: Taq mutations • Computational techniques need to aid screening and kcat (s-1)6 µ4 of K83 µ4 of Y51 µ4 of Y180 K83 mu4 across single mutations removing ionizable residues 100 RA95.0 0.00005 ------likely to adversely development 90 RA95.5* 0.0043 23 37.2 --- 80 RA95.5-5 0.073 36.6 54.9 --- impact catalysis 70 RA95.5-8 0.17 40.5 60.1 --- • Damaged DNA can block polymerases or cause inaccurate 60 95.5 BLys through RA95.5-8F 10.8 70.9 156.5 69.4 50 95.5 ALys *µ4 values shown for when K83 in B conformation in PDBID 4A2S, which amplification and results 40 95.5-5 THEMATICS aligns with that in the evolved RA95.5-5. K83 in the A conformation has 95.5-8 Mu4 Value of K83 of Mu4 Value 30 • High-fidelity polymerases that can bypass damage accurately µ4 of 43.2, while Y51 is 58.7 95.5-8F 20 needed 10 WT µ4 Values 0 • Targeted mutagenesis through computational identification can

0 50 100 150 200 250 Active Polymerase Impact Positon of the mutation along the sequence reduce experimental work needed Cleavage of (+)-methodol -1 6 • Higher percentage of active polymerases kcat (s ) µ4 of K83 µ4 of Y51 µ4 of Y180 Values Far from WT RA95.5-8F 10.8 70.9 156.5 69.4 Y51F 0.12 28.7 --- 28.9 Bad Targets The unique feature about my Y180F 2.7 32.9 33.3 ---

Data innovation/research is: Y51F/Y180F 0.00063 8.6 ------Values Close to WT THEMATICS correctly identifies the key catalytic residues Good Targets new computational procedures in fully evolved retro-aldolase and predecessors Identification of currently unknown contributors underway to aide enzyme engineering This addresses the problem of: PDBIDs: 4A2S, 4A2R5 Mutations during evolution, specifically S53T, Mutagenic the difficulty of identifying caused repositioning of Y51 relative to the catalytic lysine, K83. This lysine conformation positive enzyme variants may be favored for ligand binding and catalysis, but our THEMATICS calculations Accurate References 1. Ondrechen, M. J., et al. (2001). Proc Natl Acad Sci U S A, 6. Obexer, R., et al. (2016). Nature Chemistry, 9, 50- 56. show the tyrosine repositioning is important 98(22), 12473-12478. 7. Obexer, R., et al. (2016). Protein Eng. Des. Sel., 29(9), 355-366. for providing the lysine with higher moments, Bad Target Good Target Identification of mutations to 2. Ko, J. et al. (2005). , 59, 183-195. 8. Beard, H., et al. (2013). PLOS One, 8(12), e82849. 3. Jiang, L., et al. (2008). Science, 319(5868), 1387-1391. 9. Harder, E., et al. (2016). J. Chem. Theory Comput., 2(1), 281-296. important for catalysis. Frequency of mutations increasing affinity favorably bind the accurate 4. Althoff, E.A., et al. (2012). Protein Science, 21, 717-726. 10. Pritchard, A. E., and C. S. McHenry. (1999). J Mol Biol, 285(3), towards specific lesion conformation8,9 lesion conformation 5. Giger, L., et al. (2013). Nature Chemical Biology, 9, 494-498. 1067-1080. 11. Bailey, S., et al., (2006). Cell, 126(5), 893-904. 12. Wing, R.A., et al., (2008). J. Mol. Biol., 382, 859-869.