Computational Structure-Based Redesign of Enzyme Activity Cheng-Yu Chen, Ivelin Georgiev, Amy C.Anderson, Bruce R.Donald A Different computational redesign strategy Yizhou Yin Mar06, 2009

- Protein design: straightforward design vs. Directed mutation De Novo vs. redesign - Computational structure-based redesign GMEC (global minimum energy confirmation) - ROSETTA (RosettaDesign, …) 1) Energy Function 2) Conformational Sampling

Simplified protocol of redesign using GMEC Generate sequence space: select residue position for mutation; define types of AA that are allowed in mutation Constraint: volume, steric filter, etc Backbone dependent library, side-chain conformation library, rotamer library, fragment library… Searching for global minimum energy conformation throughout the whole sequence and conformation space (multistep) Starting Structure Screen/filterRank Select Further refinement? Another iterative cycle? Other procedure? Experimental test

Ensemble-based protein redesign Backbone dependent library, side-chain conformation library, rotamer library, fragment library… Generate sequence space: select residue position for mutation (steric shell); define types of AA that are allowed in mutation Starting Structure, targeted substrate, cofactor Active site mutation Filters: sequence-space filter, k-point, volume filter K* algorithm: search and score Rank + Select Bolstering Mutation Self-Consistent Mean Field entropy-based method Experimental verification MinDEE/A* algorithm: search and score Experimental verification Multiple pruning methods

K* algorithm -For a given protein-substrate complex, K* computes a provably-accurate ε-approximation to the binding constant K A -K*= [Σexp(-E b /RT)] / [Σexp(-E l /RT)·Σexp(-E f /RT)] b ∈ B l ∈ L f ∈ F B, L, F are rotamer-based ensembles; E is the conformation energy -Several algorithms are used to prune the candidate sequences at different steps so that the searching in the sequence space will be more efficient.

For each allowable mutated sequence: Step1 Molecular ensemble is generated, then pruned by steric, volume filters. Step2 After constrained energy minimization, the conformation is enumerated by A*. Step3 The scores from step2 are used to compute there separate partition functions, which is then combined to compute K* score.

SCMF entropy-based method S i = - ∑p(a ︱ i) ln p(a ︱ i) a ∈ A i p(a ︱ i) = ∑ p(r ︱ i) r ∈ R a -Ai is the set of AA types allowed at position i; p(a) is the probability of having AA type a at i. Ra is the set of rotamers for AA type a and p(r) is the probability of having rotamer r for AA type a at i. -Higher entropy implies higher probability of multiple AA types, hence higher tolerance to mutation at position i.

Example of GrsA-PheA’s specificity switched from Phe to Leu -GrsA-PheA is the phenylalanine adenylation domain of the nonribosomal peptide synthetase (NRPS) enzyme gramicidin S synthetase A, whose cognate substrate is Phe.

-7 residues at the active site are allowed to mutate to (G, A, V, L, I, W, F, Y, M) -only sequences with up to two mutations were considered, give the number candidates: 1450 (6.44 x 10 ) -After pruning, the number of sequences evaluated by K*: 505 (1.12 x 10 ) -Top ten sequences were experimentally verified. -7 residues were selected by SCMF and were allowed to mutate to different subset of AA. -Up to 3-point mutations were considered.

Example of T278L/A301G

T278/A301G ≈512 fold switch in specificity from Phe to Leu V187L/T278L/A301G ≈2168 fold switch in specificity from Phe to Leu, 1/6 of the WTenzyem:WTsubstrate activity

ensemble based vs. non-ensemble based 1)searching for best conformation 2)Searching for best mutation with best conformation 3)Other redesign 4)Other than redesign structure-based design vs. other computational design/ evolution Comparison in efficiency, accuracy

-Will there be any better “hybrid” methods? -How to appropriately decide the sampling size based on the redesign methods? -Any other new strategy?