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@ARTICLE{Nutschel:872624,
author = {Nutschel, Christina and Fulton, Alexander and Zimmermann,
Olav and Schwaneberg, Ulrich and Jaeger, Karl-Erich and
Gohlke, Holger},
title = {{S}ystematically scrutinizing the impact of substitution
sites on thermostability and detergent tolerance for
{B}acillus subtilis lipase {A}},
journal = {Journal of chemical information and modeling},
volume = {60},
number = {3},
issn = {1549-960X},
address = {Washington, DC},
publisher = {American Chemical Society64160},
reportid = {FZJ-2020-00116},
pages = {1568-1584},
year = {2020},
abstract = {Improving an enzyme’s (thermo )stability or tolerance
against solvents and detergents is highly relevant in
protein engineering and biotechnology. Recent developments
have tended towards data-driven approaches, where available
knowledge about the protein is used to identify substitution
sites with high potential to yield protein variants with
improved stability and, subsequently, substitutions are
engineered by site directed or site saturation (SSM)
mutagenesis. However, the development and validation of
algorithms for data-driven approaches has been hampered by
the lack of availability of large-scale data measured in a
uniform way and being unbiased with respect to substitution
types and locations. Here, we extend our knowledge on
guidelines for protein engineering following a data-driven
approach by scrutinizing the impact of substitution sites on
thermostability or / and detergent tolerance for Bacillus
subtilis lipase A (BsLipA) at very large-scale. We
systematically analyze a complete experimental SSM library
of BsLipA containing all 3439 possible single variants,
which was evaluated as to thermostability and tolerances
against four detergents under respectively uniform
conditions. Our results provide systematic and unbiased
reference data at unprecedented scale for a
biotechnologically important protein, identify consistently
defined hot spot types for evaluating the performance of
data-driven protein engineering approaches, and show that
the rigidity theory and ensemble-based approach Constraint
Network Analysis yields (CNA) hot spot predictions with an
up to 9-fold gain in precision over random classification.},
cin = {JSC / NIC / ICS-6 / IMET},
ddc = {540},
cid = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406 /
I:(DE-Juel1)ICS-6-20110106 / I:(DE-Juel1)IMET-20090612},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511) / Forschergruppe Gohlke $(hkf7_20170501)$ / PhD
no Grant - Doktorand ohne besondere Förderung
(PHD-NO-GRANT-20170405)},
pid = {G:(DE-HGF)POF3-511 / $G:(DE-Juel1)hkf7_20170501$ /
G:(DE-Juel1)PHD-NO-GRANT-20170405},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:31905288},
UT = {WOS:000526390800046},
doi = {10.1021/acs.jcim.9b00954},
url = {https://juser.fz-juelich.de/record/872624},
}