Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol.
The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.
SANOFI R&D, Centre de Recherche de Vitry/Alfortville, LGCR/SDI, 13 quai Jules Guesde-BP 14-94403 Vitry-sur-Seine Cedex, France.
Michael
M
Oberlin
Romano
R
Kroemer
Vincent
V
Mikol
Hervé
H
Minoux
Erdogan
E
Tastan
Nicolas
N
Baurin
D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D017354:Point Mutation; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism* / Q000627:therapeutic use*; D013816:Thermodynamics; D014584:User-Computer Interface
D011506:Proteins