Computational Structure-Based Redesign of Enzyme Activity

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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

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• - 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

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Simplified protocol of redesign using GMEC
Generate sequence space select residue position
for mutation define types of AA that are allowed
in mutation
Backbone dependent library, side-chain
conformation library, rotamer library, fragment
library
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?
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• 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
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
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K algorithm

• For a given protein-substrate complex, K
  computes a provably-accurate e-approximation to
  the binding constant KA
• K Sexp(-Eb/RT) / Sexp(-El/RT)Sexp(-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.

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• 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.

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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.

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Example of GrsA-PheAsspecificity 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.

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• -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 10lt7gt)
• -After pruning, the number of sequences evaluated
  by K 505 (1.12 x 10lt7gt)
• -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.

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Example of T278L/A301G
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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
WTenzyemWTsubstrate activity
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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

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