Chemoinformatic-Guided Engineering of Polyketide Synthases

Article abstract of DOI:10.1021/jacs.0c02549

Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge is re-engineering a partially reductive PKS module to produce a saturated β-carbon through a r

Full text of DOI:10.1021/jacs.0c02549

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Communication
Chemoinformatic-guided engineering of polyketide synthases
Amin Zargar, Ravi Lal, Luis E. Valencia, Jessica Wang, Tyler William H. Backman, Pablo Cruz-
Morales, Ankita Kothari, Miranda Werts, Andrew Wong, Constance B. Bailey, Arthur Loubat,
Yuzhong Liu, Yan Chen, Samantha Chang, Veronica T. Benites, Amanda Hernandez, Jesus F.
Barajas, Mitchell G Thompson, Carolina Barcelos, Rasha Anayah, Hector Garcia Martin, Aindrila
Mukhopadhyay, Christopher J. Petzold, Edward E.K. Baidoo, Leonard Katz, and Jay D Keasling
J. Am. Chem. Soc., Just Accepted Manuscript • Publication Date (Web): 15 May 2020
Downloaded from pubs.acs.org on May 15, 2020
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Chemoinformatic-guided engineering of polyketide synthases
Amin Zargar,1 Ravi Lal , Luis Valencia , Jessica Wang , Tyler William H. Backman , Pablo
,2,4
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Cruz-Morales , Ankita Kothari , Miranda Werts , Andrew R. Wong , Constance B. Bailey
,
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Arthur Loubat , Yuzhong Liu , Yan Chen , Samantha Chang , Veronica T. Benites , Amanda C.
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,2
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Hernández , Jesus F. Barajas , Mitchell G. Thompson , Carolina Barcelos , Rasha
Anayah , Hector Garcia Martin , Aindrila Mukhopadhyay , Christopher J. Petzold1,2,3, Edward
1,2
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1-2,4-8
E.K. Baidoo , Leonard Katz , Jay D. Keasling
1
Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, United States
Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Department of Energy Agile BioFoundry, Emeryville, CA 94608, USA
2
3
4
5
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7
8
QB3 Institute, University of California-Berkeley, 5885 Hollis Street, 4th Floor, Emeryville, CA 94608, United States
Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA 94720, United States
Department of Bioengineering, University of California, Berkeley, CA 94720, United States
Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, DK2970-Horsholm, Denmark
Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen,
China
9
BCAM, Basque Center for Applied Mathematics, 48009 Bilbao, Spain
ABSTRACT: Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine
and specialty chemicals. A significant challenge in PKS design is engineering a partially reductive module to produce a saturated β-
carbon through a reductive loop exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in
drug discovery, offers a viable strategy for reductive loop exchanges. We first introduced a set of donor reductive loops of diverse
genetic origin and chemical substrate structures into the first extension module of the lipomycin PKS (LipPKS1). Product titers of
these engineered unimodular PKSs correlated with chemical similarity between the substrate of the donor reductive loops and
recipient LipPKS1, reaching a titer of 165 mg/L of short chain fatty acids produced by Streptomyces albus J1074 harboring these
engineered PKSs. Expanding this method to larger intermediates requiring bimodular communication, we introduced reductive loops
of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically
significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid
in the engineering of PKSs to produce desired, unnatural products.
Rational reprogramming of PKS enzymes for the biosynthesis
of new polyketides has been a major research thrust over the
past three decades. PKSs load a malonyl-CoA analog onto
the acyl carrier protein (ACP) using the acyltansferase (AT)
domain and extend the growing chain from the ketosynthase
modules remain elusive. In this work, we compare
bioinformatic and chemoinformatic approaches to guide
reductive loop (RL) exchanges and develop a new method for
RL exchanges based on the chemical similarity of the RL
substrate. Chemoinformatics, an interdisciplinary field
blending computational chemistry, molecular modeling and
statistics to analyze structure-activity relationships, was first
1
–3
(KS) domain through a decarboxylative Claisen condensation
reaction. After chain extension, the β-carbonyl reduction state
is determined by the module’s reductive domains, namely the
ketoreductase (KR), dehydratase (DH), and enoylreductase
(ER), which generate the β-hydroxyl, -β alkene, or saturated
β-carbons respectively, when progressively combined. Unlike
fatty acid synthases, which faithfully produce saturated fatty
acids, PKSs have this variability in β-carbonyl reduction.
Consequently, multiple studies have reported PKS module
9
established for drug discovery. Recently, we suggested that a
chemoinformatic approach to PKS engineering could be
valuable, particularly in RL exchanges where the KR and DH
1
domains are substrate-dependent : acyl chain length has
1
0
critically affected dehydration in stand-alone DH and full
7
,13
PKS module studies.
Chemoinformatic methods such as atom pair (AP) similarity,
which characterizes atom pairs (e.g. length of bond path,
number of  electrons), and maximum common substructure
4
–8
engineering for various β-carbon oxidation states.
However, design strategies for introduction of reductive loop
exchanges (i.e. KR-DH-ER domains) into partially reductive
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Scheme 1. Experimental design of RL swaps. Conserved residues are identified through multiple sequence alignment surrounding
the reductive domains (“A”, “B” and “C”). Donor RLs are inserted into the native lipomycin module 1, and the attached DEBS
thioesterase hydrolyzes the product.
(
MCS) similarity, which identifies the largest common
resulting truncated PKS produced a β-hydroxy acid.22 In this
work, we selected N-terminal junctions (“A” and “B”) located
immediately after the post-AT linker, which is important for
KS-AT domain architecture, and the C-terminal junction
(“C”) directly before the ACP domain (see TableS1 for
1
4
substructure between two molecules, could beneficially
describe substrate profiles. While divergent in chemical
characterization, both similarity methods translate to a
Tanimoto coefficient with a range of 0 (least similar) to 1
23
14
(most similar). We hypothesized that chemosimilarity
sequences) based on previous work with the first module of
7
between the substrates of donor and acceptor modules in RL
exchanges may correlate with production levels, thereby
leading to engineered modules that better control the reductive
state of the β carbon.
borreledin.
We identified four donor RLs (IdmO, indanomycin, S.
antibioticus; SpnB, spinosyn, S. spinosa; AurB, aureothin, S.
aureofaciens; NanA2, nanchangamycin, S. nanchangensis;
final products in FigureS5) to swap into LipPKS1. A pairwise
comparison of phylogenetic distance and amino acid sequence
identity determined that IdmO, AurB, and SpnB have the
highest KR similarities to LipPKS1 (Figure1A). A similar
trend holds in the analysis of these donor modules upstream
and downstream KS domains (FigureS6). In contrast, the
NanA2 substrate has the highest chemical similarity based on
AP and MCS similarity to LipPKS1, followed by SpnB
(Figure1B). With the introduction of RL swaps, the chimeric
enzymes should produce 2,4-dimethyl pentanoic acid. As in
vitro PKS studies have shown divergence from in vivo
Bioinformatic studies of PKS evolution have guided
engineering efforts in closely related biosynthetic gene
clusters (BGCs).
analysis of the reductive domain common to all RLs, the
ketoreductase (KR). The KR not only reduces the β-keto
group to a β-hydroxyl, but also sets the stereochemistry of the
β-group and, if a branched extender is used, sets the ɑ-carbon
stereochemistry resulting in subtypes A1, A2, B1, B2
1
5,16
We therefore undertook a phylogenetic
(FigureS1A). We generated a phylogenetic tree from all
manually curated ketoreductases and ketosynthases in
ClusterCAD, an online database and toolkit for Type I PKSs,
totaling 72 biosynthetic gene clusters (BGCs) and 1077
2
4,25
results
due to underestimation of factors including limiting
1
7
18,19
26
modules. As in previous investigations,
clustered by subtype (FigureS1B-S2). In contrast, the RL type
e.g. KR, KR-DH, KR-DH-ER) did not phylogenetically
cluster with its upstream or downstream KS domain
the KR domains
substrate, crowding, and solubility, we cloned eight
chimeric modules and a control expressing red fluorescent
protein (RFP), into an E. coli -Streptomyces albus shuttle
(
2
7
vector and conjugated into S. albus J1074 (TableS1).
1
8
(FigureS3-S4). This suggests a link between KR evolution
Following ten-day production runs in a rich medium in
biological triplicate, cultures of S. albus harboring each of the
constructs were harvested and analyzed for product
(Supplemental Methods).
and product specificity, analogous to the evolution of KS
domains of cis-AT and trans-AT PKS modules
18
20,21
towards
substrate specificity. As KRs from KR-DH-ER modules
evolved distinctly from KR-only modules, we hypothesized
that neither KR sequence identity nor phylogenetic distance,
a pairwise comparison of phylogenetic tree members, between
the donor loops and acceptor module were likely to correlate
with RL exchange production levels.
Consistent with our hypothesis, we found a perfect correlation
between titers of the desired product and the AP/MCS
chemosimilarities between donor and LipPKS1 module
substrates (R =1.00 and p=0.00) (Figure1C). On the other
s
hand, no significant correlation between product titer and
phylogenetic distance or sequence similarity of the KR
To evaluate the importance of chemical similarity and
phylogenetic distance in RL exchanges, we swapped diverse,
full RLs into the first module of the lipomycin PKS
s
domain (R =0.04, p=0.60) was found. The lack of
phylogenetic correlation was not surprising based on our
bioinformatics analysis since the lipomycin KR is an A2-type,
evolving separately from KRs with full RLs. This trend held
in both junctions, though junction B chimeras generally
resulted in higher product titers, consistent with a previous
(LipPKS1) using conserved residues as exchange sites
7
(Scheme1). In our previous work, we introduced a
heterologous thioesterase from 6-deoxyerythronolide B
synthase (DEBS) into the C-terminus of LipPKS1; the
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study of RL exchanges as the extra residues in junction A are “A” and junction “B” in biological triplicate (error bars denote
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distal to the ACP docking interface and active site.
standard deviation).
Substituting the donor loop most chemically similar to
LipPKS1, NanA2, resulted in the highest titers of desired
product, 2,4-dimethyl pentanoic acid, reaching 165 mg/L
which we hypothesize is due to a comparatively lower rate of
turnover at the energetically intensive DH domain, allowing
for premature cleavage of the stalled product by non-
enzymatic or TE-mediated hydrolysis. Like our previous
study of
28
(Supplemental Methods). Low titers of the intermediate 2,4-
dimethyl-3-hydroxypentanoic acid were produced,
0
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Figure 2. A chemoinformatic approach to reductive loop
exchanges. A) ClusterCad search revealed the closest
substrates to LipPKS1 containing full RLs B) Production
levels of junction “B” RL exchanges ordered from highest KR
substrate similarity with LipPKS1 (MonA2, LaidS2 and
NanA2) to progressively less similarity (IdmO, AurB, SpnB)
in biological triplicate (error bars denote standard deviation).
in vitro production of adipic acid, we did not detect alkene or
7
keto acid stalled products ; non-functional KRs produce short
chain β-keto acids that spontaneously decarboxylate to form
ketones, which was also not observed, and ERs rapidly reduce
2
8
trans double bonds.
Based on these results, we took a chemoinformatic approach
to further test our hypothesis that chemosimilarity of RL
substrates is critical to PKS engineering. Using the
1
7
ClusterCAD database, we identified donor RLs from
laidlomycin and monensin that use a KR substrate (identical
to the NanA2 KR substrate) with the highest chemically
similarity to LipPKS1 (Figure2A). As junction B resulted in
superior levels of production, the RLs of LaidS2 and MonA2
were cloned into junction B of lipomycin. Like NanA2,
LaidS2 loops produced high titers of desired product, while
MonA2 performed similarly to SpnB and AurB (Figure2B).
As protein levels may influence product titers, we determined
the quantitative levels of all LipPKS1 constructs using
targeted proteomics at the conclusion of the production run
and observed no correlation between PKS protein levels and
Figure 1. Phylogenetic and chemical similarity effects on
reductive loop exchanges. A) Phylogenetic distance of the
native LipPKS1 KR domain to each donor KR. The value
above each bar denotes KR sequence identity comparison. B)
AP (bar) and MCS (dots) chemical similarity between the
native LipPKS1 KR domain and each donor KR. Chemical
structures display native KR substrate in each module C)
Polyketide production of engineered PKSs at both junction
s
product titers (R = -0.15 and p=0.77) (FigureS7). Reduced
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protein levels in the MonA2 swap could partially explain the
Phylogenetic distance, KR sequence identity, AP and MCS
similarity between reductive loop donors and LipPKS2 C)
Chromatograms of RFP, LipPKS2 with donor loops SpnB and
NanA2, and a structurally similar standard spiked into RFP
cultures D) Production levels of desired lactone in biological
triplicate (error bars denote standard deviation).
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lower levels of production in the MonA2 swap compared to
LaidS2 and NanA2. However, targeted proteomics of three
peptide peaks across the PKS does not eliminate the
possibility of proteolytic degradation or
variability in protein quality. AP Tanimoto and MCS
chemosimilarity had equivalent Spearman rank correlation to
product titers (R of 0.82, p = 0.045).
s
To better demonstrate the utility of this approach, we further
evaluated RL exchanges where AP and MCS chemosimilarity
diverge and tested this method in modules located at the center
of assembly lines, thus requiring docking domain interactions
and larger substrates. We therefore performed RL swaps on
the second module of lipomycin, LipPKS2 (Figure3A), to
generate triketide lactones. Donor loops from SpnB and
NanA2 were selected, as NanA2 has higher AP
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chemosimilarity
while
SpnB
has
higher
MCS
chemosimilarity (Figure3B). As in our single-module swaps,
KR phylogenetic similarity and sequence identity did not
correlate with product titers. We found higher correlation with
AP chemosimilarity due to higher product levels with NanA2
(Figure3C-D). Proteomics on each PKS of these bimodular
systems was not performed to rule out the effect of variable
protein levels. AP chemosimilarity more heavily weights
substructures, so NanA2 and LipPKS2 have higher similarity
levels because both select methylmalonyl-CoA in the first two
modules. In contrast, MCS chemosimilarity simply considers
the largest common substructure, which ignores the influence
of commonality at the growing chain by methyl groups. While
extension of this phenomenon to account for variances in
chemical similarity metrics (e.g. AP, MCS) requires further
study, we hypothesize that chemosimilarity metrics that best
match PKS enzymatic processing may prove most successful.
Overall, in our reductive loop exchanges in both LipPKS1 and
LipPKS2 we determined a Spearman correlation between AP
Tanimoto chemosimilarity and product titer to have an R
s
of
0
.88 and a p-value of 0.004 (Supplemental Methods).
Based on previous literature regarding the importance of
substrate size in reductive domains, in this study we
hypothesized that the field of chemoinformatics, traditionally
used in drug discovery, could be applied to PKS engineering.
Using different RLs of varying phylogenetic and chemical
similarity, we determined that chemosimilarity between donor
KRs and recipient KRs correlated with production, in contrast
to phylogenetic distance and sequence similarity. Extending
our method into multi-modular systems that use larger
substrates and communication domains, we performed RL
swaps in LipPKS2 and found that AP chemosimilarity
correlates with production. While our approach did not find a
correlation between genetic similarity and production in these
diverse RL swaps, it has been shown that within highly similar
BGCs, the downstream KS groups with the upstream RL type
18
(
e.g. KR, KR-DH, KR-DH-ER) . In this study, the donor
modules do not share close homology with the lipomycin
recipient module, but donor loops with high chemosimilarity
located within a BGC may prove more compatible than
chemosimilarity alone. Overall, our results determined
statistical significance in the correlation between production
and the chemosimilarity of the substrate between the donor
and recipient modules. More generally, chemoinformatics
may provide guideposts for other engineering goals (e.g. KR
Figure 3. Bimodular reductive loop exchange A) Schematic
of reductive loop exchanges in LipPKS2 with substrates B)
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domain subtype swaps to switch stereochemistry). With our
incomplete understanding of PKS processing, design
principles may accelerate the combinatorial approach
currently used for de novo biosynthesis and help provide a
framework to more rapidly produce valuable biochemicals.
approach to reductive loop swaps in modular polyketide synthases.
Chembiochem 2008, 9, 2740–2749.
7) Hagen, A.; Poust, S.; Rond, T. de; Fortman, J. L.; Katz, L.;
Petzold, C. J.; Keasling, J. D. Engineering a polyketide synthase for
in vitro production of adipic acid. ACS Synth. Biol. 2016, 5, 21–27.
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(
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drugs after domain exchange in the avermectin polyketide synthase
of Streptomyces avermitilis ATCC31272. Org. Biomol. Chem. 2003,
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ASSOCIATED CONTENT
Supporting Information
The Supporting Information is available free of charge on the
ACS Publications website.
(9)
Maldonado, A. G.; Doucet, J. P.; Petitjean, M.; Fan, B.-T.
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Molecular similarity and diversity in chemoinformatics: from theory
to applications. Mol Divers 2006, 10, 39–79.
Supplementary tables and figures, plasmids, strains, and detailed
experimental procedures
(10) Faille, A.; Gavalda, S.; Slama, N.; Lherbet, C.;
Maveyraud, L.; Guillet, V.; Laval, F.; Quémard, A.; Mourey, L.;
Pedelacq, J.-D. Insights into Substrate Modification by Dehydratases
from Type I Polyketide Synthases. J. Mol. Biol. 2017, 429, 1554–
AUTHOR INFORMATION
Corresponding Author
1
569.
11) Herbst, D. A.; Jakob, R. P.; Zähringer, F.; Maier, T.
(
*Corresponding author
Jay D. Keasling
jdkeasling@lbl.gov
Mycocerosic acid synthase exemplifies the architecture of reducing
polyketide synthases. Nature 2016, 531, 533–537.
(12) Barajas, J. F.; McAndrew, R. P.; Thompson, M. G.;
Backman, T. W. H.; Pang, B.; de Rond, T.; Pereira, J. H.; Benites, V.
T.; Martín, H. G.; Baidoo, E. E. K.; Hillson, N. J.; Adams, P. D.;
Keasling, J. D. Structural insights into dehydratase substrate selection
for the borrelidin and fluvirucin polyketide synthases. J. Ind.
Microbiol. Biotechnol. 2019, 46, 1225–1235.
ACKNOWLEDGMENT
This work was funded by the DOE Joint BioEnergy Institute
(http://www.jbei.org) supported by the U.S. Department of
Energy, Office of Science, Office of Biological and
Environmental Research between Lawrence Berkeley National
Laboratory and the U.S. Department of Energy, the Agile
Biofoundry sponsored by the U.S. DOE Office of Energy
Efficiency and Renewable Energy, Bioenergy Technologies and
Vehicle Technologies Offices, under Contract DEAC02-
(13) McDaniel, R.; Thamchaipenet, A.; Gustafsson, C.; Fu, H.;
Betlach, M.; Ashley, G. Multiple genetic modifications of the
erythromycin polyketide synthase to produce a library of novel
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unnatural” natural products. Proc. Natl. Acad. Sci. USA 1999, 96,
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(14) Chen, X.; Reynolds, C. H. Performance of similarity
0
5CH11231 between DOE and Lawrence Berkeley National
measures in 2D fragment-based similarity searching: comparison of
structural descriptors and similarity coefficients. J Chem Inf Comput
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(15) Peng, H.; Ishida, K.; Sugimoto, Y.; Jenke-Kodama, H.;
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type modular polyketide synthases and associated oxygenases. Nat.
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Laboratory, and the National Institute of Health Awards
F32GM125179, F32GM125166. H.G.M. was also supported by
the Basque Government through the BERC 2018-2021 program
and by Spanish Ministry of Economy and Competitiveness
MINECO: BCAM Severo Ochoa excellence accreditation SEV-
2
017- 0718.
(16) Awakawa, T.; Fujioka, T.; Zhang, L.; Hoshino, S.; Hu, Z.;
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Reprogramming of the antimycin NRPS-PKS assembly lines inspired
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ABBREVIATIONS
PKS polyketide synthase; KS ketosynthase; AT acyltransferase;
ACP acyl carrier protein; KR ketoreductase; DH dehydratase; ER
enoylreductase; RL reductive loop; AP atom pair; MCS maxium
common substructure; BGC biosynthetic gene cluster
(17) Eng, C. H.; Backman, T. W. H.; Bailey, C. B.; Magnan,
C.; García Martín, H.; Katz, L.; Baldi, P.; Keasling, J. D.
ClusterCAD: a computational platform for type I modular polyketide
synthase design. Nucleic Acids Res. 2018, 46, D509–D515.
(18) Zhang, L.; Hashimoto, T.; Qin, B.; Hashimoto, J.; Kozone,
I.; Kawahara, T.; Okada, M.; Awakawa, T.; Ito, T.; Asakawa, Y.;
Ueki, M.; Takahashi, S.; Osada, H.; Wakimoto, T.; Ikeda, H.; Shin-
Ya, K.; Abe, I. Characterization of Giant Modular PKSs Provides
Insight into Genetic Mechanism for Structural Diversification of
Aminopolyol Polyketides. Angew. Chem. Int. Ed. Engl. 2017, 56,
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