of 1
Correction
to “DeCOIL:
Optimization
of Degenerate
Codon
Libraries
for Machine
Learning-Assisted
Protein
Engineering”
Jason
Yang,
Julie Ducharme,
Kadina
E. Johnston,
Francesca-Zhoufan
Li, Yisong
Yue,
and Frances
H. Arnold
*
ACS
Synth.
Biol.
2023
,
12
(8),
2444
2454.
DOI:10.1021/acssynbio.3c00301.
Cite This:
ACS Synth.
Biol.
2024,
13, 692−692
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Article
Recommendations
Introduction,
third
paragraph,
replace
“Other
studies
take
different
approaches
to bias
libraries
toward
distributions
of
favorable
variants,
ranging
from
theoretical
to more
ap-
plied.16,51
55
Each
study
approaches
the
problem
from
a
different
angle,
but none
have
been
made
both
suitable
and
practical
for the possible
use cases
encountered
during
MLPE
(Figure
1A)”
with
“Other
studies
take
different
approaches
to
bias
libraries
toward
distributions
of favorable
variants,
ranging
from
theoretical
to more
applied.16,51
55
The
most
relevant
to our
work
is Zhu
et al.,16
which
presents
a method
to
maximize
the diversity
of a library,
subject
to a constraint
on
predicted
variant
fitness.
In the future,
it would
be interesting
to conduct
a more
detailed
comparison
to understand
how
suitable
and
practical
each
method
is for different
MLPE
use
cases
(Figure
1A).
Replace
ref 16 with:
(16)
Zhu,
D.;
Brookes,
D. H.;
Busia,
A.; Carneiro,
A.;
Fannjiang,
C.; Popova,
G.; Shin,
D.; Donohue,
K. C.; Chang,
E. F.; Nowakowski,
T. J.; Listgarten,
J.; Schaffer,
D. V. Optimal
Trade-off
Control
in Machine
Learning-Based
Library
Design,
with
Application
to Adeno-Associated
Virus
(AAV)
for Gene
Therapy.
Sci. Adv.
,
in press.
Published:
January
17,
2024
Correction
pubs.acs.org/synthbio
© 2024
American
Chemical
Society
692
https://doi.org/10.1021/acssynbio.3c00751
ACS Synth.
Biol.
2024,
13, 692
692
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