TY - JOUR
T1 - Directed Computational Evolution of Quorum-Quenching Lactonases from the Amidohydrolase Superfamily
AU - Go, Maybelle Kho
AU - Zhao, Li Na
AU - Xue, Bo
AU - Supekar, Shreyas
AU - Robinson, Robert C.
AU - Fan, Hao
AU - Yew, Wen Shan
N1 - Funding Information:
This work was supported by the National Research Foundation Singapore (to W.S.Y.) and by the Biomedical Research Council of A ∗ STAR (to H.F.). This research was undertaken in part using the MX1 and MX2 beamlines at the Australian Synchrotron, part of ANSTO, and made use of the Australian Cancer Research Foundation detector.
Funding Information:
This work was supported by the National Research Foundation Singapore (to W.S.Y.) and by the Biomedical Research Council of A?STAR (to H.F.). This research was undertaken in part using the MX1 and MX2 beamlines at the Australian Synchrotron, part of ANSTO, and made use of the Australian Cancer Research Foundation detector. Conceptualization, H.F. and W.S.Y.; Formal Analysis, L.N.Z. S.S. and H.F.; Investigation, M.K.G. and B.X.; Writing ? Original Draft, M.K.G. L.N.Z. B.X. S.S. R.C.R. H.F. and W.S.Y.; Writing ? Review & Editing, M.K.G. L.N.Z. B.X. S.S. R.C.R. H.F. and W.S.Y. The authors declare no competing interests.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/6/2
Y1 - 2020/6/2
N2 - In this work, we present a generalizable directed computational evolution protocol to effectively reduce the sequence space to be explored in rational enzyme design. The protocol involves in silico mutation modeling and substrate docking to rapidly identify mutagenesis hotspots that may enhance an enzyme's substrate binding and overall catalysis. By applying this protocol to a quorum-quenching Geobacillus kaustophilus lactonase, GKL, we generated 1,881 single mutants and docked high-energy intermediates of nine acyl homoserine lactones onto them. We found that Phe28 and Tyr99 were two hotspots that produced most of the predicted top 20 mutants. Of the 180 enzyme-substrate combinations (top 20 mutants × 9 substrates), 51 (28%) exhibited enhanced substrate binding and 22 (12%) had better overall activity when compared with wild-type GKL. X-ray crystallographic studies of Y99C and Y99P provided rationalized explanations for the enhancement in enzyme function and corroborated the utility of the protocol. Go et al. use in silico mutagenesis and substrate docking to rapidly identify hotspots on enzymes for enhanced substrate binding and overall catalysis. They demonstrate the utility of the protocol with a quorum-quenching Geobacillus kaustophilus lactonase, GKL, and solve two GKL mutant structures to provide rationalized explanations for the enhancement.
AB - In this work, we present a generalizable directed computational evolution protocol to effectively reduce the sequence space to be explored in rational enzyme design. The protocol involves in silico mutation modeling and substrate docking to rapidly identify mutagenesis hotspots that may enhance an enzyme's substrate binding and overall catalysis. By applying this protocol to a quorum-quenching Geobacillus kaustophilus lactonase, GKL, we generated 1,881 single mutants and docked high-energy intermediates of nine acyl homoserine lactones onto them. We found that Phe28 and Tyr99 were two hotspots that produced most of the predicted top 20 mutants. Of the 180 enzyme-substrate combinations (top 20 mutants × 9 substrates), 51 (28%) exhibited enhanced substrate binding and 22 (12%) had better overall activity when compared with wild-type GKL. X-ray crystallographic studies of Y99C and Y99P provided rationalized explanations for the enhancement in enzyme function and corroborated the utility of the protocol. Go et al. use in silico mutagenesis and substrate docking to rapidly identify hotspots on enzymes for enhanced substrate binding and overall catalysis. They demonstrate the utility of the protocol with a quorum-quenching Geobacillus kaustophilus lactonase, GKL, and solve two GKL mutant structures to provide rationalized explanations for the enhancement.
KW - N-acyl-homoserine lactonase
KW - directed computational evolution
KW - enzyme engineering
KW - structure-based engineering
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U2 - 10.1016/j.str.2020.03.011
DO - 10.1016/j.str.2020.03.011
M3 - Article
C2 - 32320671
AN - SCOPUS:85085391704
VL - 28
SP - 635-642.e3
JO - Structure with Folding & design
JF - Structure with Folding & design
SN - 0969-2126
IS - 6
ER -