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Computational Structure-Based Redesign of Enzyme Activity

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

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Computational Structure-Based Redesign of Enzyme Activity

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  1. 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

  2. - 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

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

  4. 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 • Ensemble-based protein redesign Filters: sequence-space filter, k-point, volume filter Active site mutation Multiple pruning methods K* algorithm: search and score Experimental verification Rank + Select Self-Consistent Mean Field entropy-based method Bolstering Mutation MinDEE/A* algorithm: search and score Experimental verification

  5. K* algorithm • For a given protein-substrate complex, K* computes a provably-accurate ε-approximation to the binding constant KA • K*= [Σexp(-Eb/RT)] / [Σexp(-El/RT)·Σexp(-Ef/RT)] b∈Bl∈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.

  6. 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.

  7. SCMF entropy-based method • Si = - ∑p(a︱i) ln p(a︱i) • a∈Ai • p(a︱i) = ∑ p(r︱i) • r∈Ra • - 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.

  8. Example of GrsA-PheA’sspecificity 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.

  9. -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<7>) • -After pruning, the number of sequences evaluated by K*: 505 (1.12 x 10<7>) • -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.

  10. Example of T278L/A301G

  11. 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

  12. Comparison in efficiency, accuracy • ensemble based vs. non-ensemble based • searching for best conformation • Searching for best mutation with best conformation • Other redesign • Other than redesign • structure-based design vs. other computational design/ evolution

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

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