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 redesignGMEC (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/filter Rank

Select

Further refinement?

Another iterative cycle?

Other procedure?Experimental test

Ensemble-based protein redesignBackbone 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* compute

s a provably-accurate ε-approximation to the binding constant KA

- K*= [Σexp(-Eb/RT)] / [Σexp(-El/RT)·Σexp(-Ef/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

Si = - ∑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’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.

-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.

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?