SUPPORTING INFORMATION FOR. A Computational Approach to Enzyme Design: Using Docking and MM- GBSA Scoring

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1 SUPPRTING INFRMATIN FR A Computational Approach to Enzyme Design: Predicting ω- Aminotransferase Catalytic Activity Using Docking and MM- GBSA Scoring Sarah Sirin, 1 Rajesh Kumar, 2 Carlos Martinez, 2 Michael J Karmilowicz, 2 Preeyantee Ghosh, 3 Yuriy A Abramov, 2 Van Martin, 2 and Woody Sherman *1 1 Schrödinger, Inc., 120 West 45th Street, 29th Floor, New York, NY 10036, USA 2 Pfizer Worldwide Research and Development, Eastern Point Road, Groton, CT 06340, USA 3 Schrödinger, Sanali Infopark, /113, Banjara Hills, Hyderabad , Andhra Pradesh, India *To whom correspondence should be addressed. E- mail: woody.sherman@schrodinger.com

2 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 2 Ionizable group PMP (amine) PMP (phosphate) Lys285 (amine) State 1 State 2 State 3 State 4 State 5 State Table S1. List of molecular dynamics simulations, denoted states 1 through 6, carried out with various protonation states for cofactor pyridoxamine phosphate (PMP) and reactive lysine ionizable groups. Protonation states are denoted using - 2 through +1 referring to the net charge assigned to the ionizable group listed in first column.

3 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 3 Distances (Å) State 1 State 2 State 3 State 4 State 5 State 6 A:PMP(N)- K285(N) [3.5] 2.79± ± ± ± ± ±0.28 B:PMP(N)- K285(N) [3.5] 2.80± ± ± ± ± ±0.35 A:PMP(N)- PMP(H) [2.7] 3.76± ± ± ± ± ±0.21 B:PMP(N)- PMP(H) [2.9] 3.61± ± ± ± ± ±0.18 A:PMP(N)- PMP(P) [5.2] 3.53± ± ± ± ± ±0.19 B:PMP(N)- PMP(P) [5.0] 3.71± ± ± ± ± ±0.15 A:K285(N)- PMP(P) [5.8] 5.13± ± ± ± ± ±0.37 B:K285(N)- PMP(P) [5.8] 5.13± ± ± ± ± ±0.96 Table S2. Calculated key active site distances averaged over 5 ns molecular dynamics simulations. Distances in the first monomer is denoted A: and the complementary monomer distances are denoted B:. Also, X- ray crystal structure distances are shown inside the square brackets.

4 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 4 Distance (Å) Reactant Transition State N(PMP) N(Lys285) 3.03 ± ± 0.01 N(PMP) HN(PMP) 1.01 ± ± 0.00 HN(PMP) N(Lys285) 2.14 ± ± 0.07 N(PMP) C(Substrate) 3.67 ± ± 0.00 (Substrate) C(Substrate) 1.24 ± ± 0.00 Angle ( ) (Substrate) C(Substrate)- N(PMP) ± ± 3.17 Table S3. Key geometric measurements for the reactant and transition state input structures for wild- type ω- AT. The averages were taken over 26 frames clustered from enhanced MD simulations (metadynamics). The high energy transition state guess geometries were generated using Prime 1 implicit minimization module with distance constraints.

5 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 5 Predicted Most active Active Inactive Most active Actual activity at 4 hours Active Inactive Most active Actual activity at 24 hours Active Inactive Table S4. Contingency matrix of DockingScore in classifying variants as most active, active and inactive. Enzyme variants were considered to be most active if the product yields was greater than 5 percent and greater than 15 percent, when the product yield was measured at 4 and 24 hours respectively. Enzyme variants were grouped as inactive, when the product yield was 0, while an activity in between the two extremes were grouped as active.

6 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 6 Mutations Experimental % 4 hr Experimental % 24 hr Docking Score 163- >LEU, 225- >ALA, 57- >PHE active active active 153- >ALA, 163- >LEU, 179- >ALA, 57- >PHE active active active 163- >LEU, 167- >THR, 169- >GLY, 57- >PHE inactive inactive active 316- >LYS, 57- >PHE active active active 163- >PHE, 57- >PHE active active active 163- >LEU, 377- >LEU, 57- >PHE active active active 321- >TYR, 433- >HIS, 57- >PHE active active active 151- >THR, 163- >LEU, 57- >PHE inactive active inactive 153- >ALA, 300- >ASP, 57- >PHE active active inactive 153- >ALA, 163- >LEU, 21- >TYR, 57- >PHE active active active 163- >LEU, 17- >SER, 57- >PHE inactive inactive inactive 300- >ASP, 415- >PHE, 57- >PHE active active most active 163- >LEU, 57- >PHE active active active 163- >LEU, 264- >LYS, 57- >PHE inactive inactive active 153- >ALA, 163- >LEU, 415- >PHE, 57- >PHE active active most active 322- >PR, 57- >PHE active active inactive 284- >ARG, 322- >ARG, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 415- >PHE, 57- >PHE, active active most active 84- >SER 151- >ALA, 163- >LEU, 57- >PHE active active inactive 119- >GLY, 163- >LEU, 57- >PHE active active inactive 322- >ALA, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 19- >TRP, 57- >PHE active most active active 163- >HIS, 57- >PHE active active active 163- >LEU, 378- >PHE, 57- >PHE active active most active 57- >PHE, 86- >HIS active active active 441- >MET, 57- >PHE active active active 153- >ALA, 163- >LEU, 303- >ILE, 57- >PHE active active active 148- >GLY, 163- >LEU, 57- >PHE active active active 321- >TYR, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 228- >GLY, 57- >PHE active active inactive 163- >LEU, 375- >PHE, 379- >TYR, 57- >PHE inactive inactive active 298- >LEU, 441- >MET, 57- >PHE active active active 300- >ALA, 415- >PHE, 57- >PHE active active most active 57- >PHE, 86- >LEU inactive inactive active

7 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI >SER, 153- >ALA, 163- >LEU, 57- >PHE active active inactive 153- >ALA, 163- >LEU, 303- >PHE, 57- >PHE active active active 322- >ARG, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 57- >PHE, 85- >LEU active active active 163- >LEU, 379- >TYR, 57- >PHE active active active 118- >GLY, 57- >PHE active active inactive 19- >GLU, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 414- >ILE, 57- >PHE inactive inactive inactive 57- >PHE, 85- >LEU active active active 322- >ASN, 57- >PHE active active inactive 298- >LEU, 57- >PHE active active most active 153- >ALA, 163- >LEU, 360- >MET, 57- >PHE active active active 163- >LEU, 169- >THR, 57- >PHE inactive inactive active 153- >ALA, 163- >LEU, 268- >MET, 57- >PHE active active active 153- >ALA, 163- >LEU, 280- >GLY, 57- >PHE active active active 322- >GLY, 57- >PHE inactive inactive inactive 150- >SER, 163- >LEU, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 304- >ALA, 57- >PHE active active active 153- >ALA, 163- >LEU, 259- >VAL, 415- >PHE, 57- >PHE, 84- >ALA most active most active most active 153- >ALA, 163- >LEU, 259- >VAL, 415- >PHE, 57- >PHE, 84- >SER active active most active 153- >ALA, 163- >LEU, 226- >LEU, 57- >PHE active active active 153- >ALA, 163- >LEU, 304- >ASP, 57- >PHE active active active 163- >LEU, 373- >LYS, 57- >PHE active active active 57- >PHE, 86- >MET inactive inactive active 316- >THR, 57- >PHE active active active 57- >PHE, 86- >GLN active inactive active 112- >VAL, 163- >LEU, 57- >PHE active active active 57- >PHE, 86- >PR inactive inactive inactive 322- >LEU, 57- >PHE inactive inactive inactive 163- >LEU, 226- >THR, 57- >PHE active active active 153- >ALA, 163- >LEU, 298- >LEU, 57- >PHE active active active 163- >LEU, 57- >PHE, 83- >THR inactive inactive active 153- >ALA, 163- >PHE, 19- >TRP, 259- >VAL, 415- >PHE, 57- >PHE, 85- >ALA, 88- >LYS most active most active most active 163- >LEU, 253- >GLU, 264- >LYS, 57- >PHE inactive active active 153- >ALA, 163- >LEU, 19- >TRP, 21- >TYR, 57- >PHE active active active 153- >ALA, 163- >LEU, 177- >ILE, 57- >PHE active active active 144- >ILE, 153- >ALA, 163- >LEU, 57- >PHE active active active 153- >ALA, 57- >PHE active active inactive

8 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI >ALA active active most active 322- >VAL, 57- >PHE active active inactive 322- >GLN, 57- >PHE inactive inactive inactive 163- >LEU, 225- >ALA, 285- >ASN, 57- >PHE active active active 19- >HIS, 57- >PHE inactive inactive most active 57- >PHE active active most active 153- >ALA, 163- >LEU, 301- >ALA, 57- >PHE active active active 19- >ASN, 57- >PHE inactive inactive inactive 113- >VAL, 163- >LEU, 57- >PHE inactive inactive active 153- >ALA, 163- >LEU, 288- >SER, 57- >PHE active active active 322- >TYR, 57- >PHE inactive inactive inactive 163- >LEU, 165- >SER, 57- >PHE inactive inactive active 113- >PHE, 153- >ALA, 163- >LEU, 57- >PHE active active inactive 150- >VAL, 163- >LEU, 57- >PHE inactive inactive inactive 153- >ALA, 163- >LEU, 300- >SER, 57- >PHE active active active 153- >ALA, 163- >LEU, 323- >SER, 57- >PHE active active inactive 153- >ALA, 163- >LEU, 57- >PHE active active active Table S5. List of mutations to wild- type ω- AT and the corresponding experimental activity measured at 4 and 24 hours and the computational predictions as judged via docking score. An enzyme variant was experimentally grouped as inactive its product yield was 0 %, active if its product yield was greater than 0% but less than 2.5 %, and most active if the product yield was greater than 5 at 4 hours. Similarly at 24 hours, variants with 0 % product yield was grouped as inactive, 0 to 2.5 % product yield was grouped as active and greater than 15 % product yield was grouped as most active.

Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 9 Apo PMP-bound T322 K285 F321 PMP Figure S1: verlay of the open- to- close conformation switch induced by cofactor binding.

9 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 9 Apo PMP-bound T322 K285 F321 PMP Figure S1: verlay of the open- to- close conformation switch induced by cofactor binding. The open conformation is illustrated using green carbons and corresponds to an apo state (PDB accession code: 3NUI 2 ). While the closed conformation is illustrated using cyan carbons and corresponds to the cofactor, pyridoxamine phosphate (PMP) bound state (PDB accession code: 4E3Q 3 ).

10 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 10 Figure S2. Illustration of the automated KNIME workflow that can perform in silico mutagenesis and compute changes in binding activity relative to wild- type enzyme variant. The generated mutants could be visualized for further examination and/or the computed scores could be exported as a table.

11 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 11 Lys285 - Lys285 Lys285 NH 2 NH 2 H NH 2 H CH + HN - NH 2 + HN - - H 3 P H -H 2 - H 3 P H - H 3 P H N N N H H - H Ash256 C Ash256 C Ash256 C Lys285 Lys285 Lys285 H + HN H 2 N HN H 2 N NH 2 + HN H 3 P H - H 3 P H - H 3 P H N N N H H H Ash256 C Ash256 C Ash256 C Figure S3. Illustration of the generally accepted transamination mechanism, shown for pyruvate to alanine transformation as catalyzed by ω- aminotransferase.

12 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 12 Figure S4. RMSD from the crystal coordinates for the C- α atoms in ω- AT dimer for states 1 though 6. C- α carbons in chain A are shown in black and the corresponding chain B C- α carbons are shown in red.

Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 13 Figure S5. RMSD from the crystal coordinates for the active site non- hydrogen atoms in ω- AT dimer for states 1 though 6.

13 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 13 Figure S5. RMSD from the crystal coordinates for the active site non- hydrogen atoms in ω- AT dimer for states 1 though 6. The active site was defined as cofactor PMP and residues Lys285, Trp57, Arg415, Try150, F321 and T322. C- α carbons in chain A are shown in black and the corresponding chain B C- α carbons are illustrated using red.

Illustration of X- ray crsytallographic water molecules that

14 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 14 T322 W57 PMP Y150 R415 Figure S6. Illustration of X- ray crsytallographic water molecules that bridge PMP phosphate and amine groups with Tyr150, Arg415 and Trp57 side chains.

Illustration of the characterized ω- AT binding pocket and docked native substrate,

PMP cofactor is illustrated using salmon colored carbons, and resiudes around the

15 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 15 Pyruvate PMP Figure S7. Illustration of the characterized ω- AT binding pocket and docked native substrate, shown using yellow carbons. PMP cofactor is illustrated using salmon colored carbons, and resiudes around the active site are shown using green carbons. The contour sitemap points are generated to illustrate hydrophobic (yellow) and hydrophilic regions that are further divided into donor (blue) and acceptor (red) sites.

Illustration of sampled coordinates native substrate adopts in the bound configuration.

16 SI 16 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. R415 R415 W57 W57 K285 K285 Pyruvate Pyruvate PMP PMP (a) (b) Figure S8. Illustration of sampled coordinates native substrate adopts in the bound configuration. 30 ns metadynamics simulations was clustered based on PMP and pyruvate RMSD and cluster centers were extracted. Bound configurations were screened based on PMP amine nitrogen distance to pyrvuate ketone carbon.

17 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 17 (a) (b) Figure S9. Ligand interaction diagrams for the (a) native and (b) target substrate in complex with wild- type ω- AT. nly residues within 4 Å of substrate and cofactor are shown. Hydrophobic residues are shown in green, negatively charged in red and positively charged in blue. The grey hue centered on substrate atoms indicates solvent exposure, while the pink arrows indicate hydrogen from donor to acceptor.

18 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 18 Figure S10. Receiver operating characteristic (RC) curve for MM- GBSA predictions at 4 and 24 hours, shown using blue and green respectively. 0- Å- distance cut- off was used in the refinement step, i.e. only the mutated residues were refined

19 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 19 Figure S11. verlay histogram of MM- GBSA, DockingScore, and Z- score (reported here as averaged rank) predictions for active and inactive ω- AT variants. Both MM- GBSA and DockignScore values are reported in units of kcal/mol, where lower value means higher affinity, while a more positive Z- score rank means higher affinity.

20 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 20 1" AUC$and$95$%$Confidence$Limits$ 0.9" 0.8" 0.7" 0.6" AUC$ 0.5" 0.4" 0.3" 0.2" 0.1" 0" MM.GBSA(4hrs)" MM.GBSA(24hrs)" Docking(4hrs)" Docking(24hrs)" Z.score(4hrs)" Z.score(24hrs)" Figure S12. The average AUC values with 95 percent confidence intervals for MM- GBSA, DockignScore, and Z- score. (a) (b) Figure S13. Transition State Model: Receiver operating characteristic (RC) curve for MM- GBSA predictions using (a) 0 and (b) 5 Å cut- off in the refinement step, at 4 and 24 hours, shown using blue and green respectively. Additionally, only mutable residues were refined to ensure the high- energy transition state conformation did not relax into a reactant conformation during the optimization.

21 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 21 Figure S14. ω- AT active site substrate binding activity map: Boltzmann weighted change in DockingScore for single point mutations of residues within 8 Å of the bound target substrate, where a total 61 residues were perturbed. Residues belonging to Chain A are listed first, followed by Chain B residues.

22 Predicting ω- Aminotransferase Catalytic Activity, Sirin et. al. SI 22 REFERENCES 1. Prime, Version 3.4, Schrödinger, LLC: New York, NY, Park, H. H., Jang, T., Crystal Structure of the First mega-transaminase at 2.0a Resolution Midelfort, K. S.; Kumar, R.; Han, S.; Karmilowicz, M. J.; McConnell, K.; Gehlhaar, D. K.; Mistry, A.; Chang, J. S.; Anderson, M.; Villalobos, A., Redesigning and Characterizing the Substrate Specificity and Activity of Vibrio Fluvialis Aminotransferase for the Synthesis of Imagabalin. Protein Eng. Des. Sel. 2013, 26,

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