Microbially guided discovery and biosynthesis of biologically active natural products

Abstract

The design of small molecules that inhibit disease-relevant proteins represents a longstanding challenge of medicinal chemistry. Here, we describe an approach for encoding this challenge—the inhibition of a human drug target—into a microbial host and using it to guide the discovery and biosynthesis of targeted, biologically active natural products. This approach identified two previously unknown terpenoid inhibitors of protein tyrosine phosphatase 1B (PTP1B), an elusive therapeutic target for the treatment of diabetes and cancer. Both inhibitors target an allosteric site, which confers unusual selectivity, and can inhibit PTP1B in living cells. A screen of 24 uncharacterized terpene synthases from a pool of 4,464 genes uncovered additional hits, demonstrating a scalable discovery approach, and the incorporation of different PTPs into the microbial host yielded alternative PTP-specific detection systems. Findings illustrate the potential for using microbes to discover and build natural products that exhibit precisely defined biochemical activities yet possess unanticipated structures and/or binding sites.

Full text

Page 1/21
Microbially guided discovery and biosynthesis of
biologically active natural products
Ankur Sarkar
University of Colorado Boulder
Edward Kim
National Cancer Institute, NIH
Taehwan Jang
Korea Advanced Institute of Science and Technology
Akarawin Hongdusit
University of Colorado Boulder
Hyungjun Kim
Korea Advanced Institute of Science and Technology https://orcid.org/0000-0001-8261-9381
Jeong-Mo Choi
Korea Advanced Institute of Science and Technology https://orcid.org/0000-0003-2656-4851
Jerome Fox (  jerome.fox@colorado.edu )
University of Colorado Boulder https://orcid.org/0000-0002-3739-1899
Article
Keywords: biologically active natural products, terpenoid inhibitors, protein tyrosine phosphatase 1B
(PTP1B)
DOI: https://doi.org/10.21203/rs.3.rs-92116/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

Page 2/21
Abstract
The design of small molecules that inhibit disease-relevant proteins represents a longstanding challenge
of medicinal chemistry. Here, we describe an approach for encoding this challenge—the inhibition of a
human drug target—into a microbial host and using it to guide the discovery and biosynthesis of targeted,
biologically active natural products. This approach identied two previously unknown terpenoid inhibitors
of protein tyrosine phosphatase 1B (PTP1B), an elusive therapeutic target for the treatment of diabetes
and cancer. Both inhibitors target an allosteric site, which confers unusual selectivity, and can inhibit
PTP1B in living cells. A screen of 24 uncharacterized terpene synthases from a pool of 4,464 genes
uncovered additional hits, demonstrating a scalable discovery approach, and the incorporation of
different PTPs into the microbial host yielded alternative PTP-specic detection systems. Findings
illustrate the potential for using microbes to discover and build natural products that exhibit precisely
dened biochemical activities yet possess unanticipated structures and/or binding sites.
Introduction
Despite advances in structural biology and computational chemistry, the design of small molecules that
bind tightly and selectively to disease-relevant proteins remains exceptionally dicult1. The free energetic
contributions of rearrangements in the molecules of water that solvate binding partners and structural
changes in the binding partners themselves are particularly challenging to predict and, thus, to
incorporate into molecular design2,3. Drug development, as a result, often begins with screens of large
compound libraries4.
Nature has endowed living systems with the catalytic machinery to build an enormous variety of
biologically active molecules—a diverse natural library5. These molecules evolved to carry out important
metabolic and ecological functions (e.g., the phytochemical recruitment of predators of herbivorous
insects6) but often also exhibit useful medicinal properties. Over the years, screens of environmental
extracts and natural product libraries—augmented, on occasion, with combinatorial (bio)chemistry7–9—
have uncovered a diverse set of therapeutics, from aspirin to paclitaxel10. Unfortunately, these screens
tend to be resource intensive11, limited by low natural titers12, and largely subject to serendipity13.
Bioinformatic tools, in turn, have permitted the identication of biosynthetic gene clusters14,15, where co-
localized resistance genes can reveal the biochemical function of their products16,17. The therapeutic
applications of many natural products, however, differ from their native functions18, and many
biosynthetic pathways can, when appropriately recongured, produce entirely new and, perhaps, more
effective therapeutic molecules19,20. Methods for eciently identifying and building natural products that
inhibit specic disease-relevant proteins remain largely undeveloped.
Protein tyrosine phosphatases (PTPs) are an important class of drug targets that could benet from new
approaches to inhibitor discovery. These enzymes catalyze the hydrolytic dephosphorylation of tyrosine
residues and, together with protein tyrosine kinases (PTKs), contribute to an enormous number of

Page 3/21
diseases (e.g., cancer, autoimmune disorders, and heart disease, to name a few)21,22. The last several
decades have witnessed the construction of many potent inhibitors of PTKs, which are targets for over 30
approved drugs23. Therapeutic inhibitors of PTPs, by contrast, have proven dicult to develop. These
enzymes possess well conserved, positively charged active sites that make them dicult to inhibit with
selective, membrane-permeable molecules24; they lack targeted therapeutics of any kind.
In this study, we describe an approach for using microbial systems to nd natural products that inhibit
dicult-to-drug proteins. We focused on protein tyrosine phosphatase 1B (PTP1B), a therapeutic target
for the treatment of type 2 diabetes, obesity, and HER2-positive breast cancer25. PTP1B possesses
structural characteristics that are generally representative of the PTP family26 and regulates a diverse set
of physiological processes (e.g., energy expenditure27, inammation28, and neural specication in
embryonic stem cells29). In brief, we assembled a strain of
Escherichia coli
with two genetic modules—(i)
one that links cell survival to the inhibition of PTP1B and (ii) one that enables the biosynthesis of
structurally varied terpenoids. In a study of ve well-characterized terpene synthases, this strain identied
two previously unknown terpenoid inhibitors of PTP1B. Both inhibitors were selective for PTP1B,
exhibited distinct binding mechanisms, and increased insulin receptor phosphorylation in mammalian
cells. A screen of 24 uncharacterized terpene synthases from eight phylogenetically diverse clades
uncovered additional hits, demonstrating a scalable approach for nding inhibitor-synthesizing genes. A
simple exchange of PTP genes, in turn, permitted the facile extension of our genetically encoded
detection system to new targets. Our ndings illustrate a versatile approach for using microbial systems
to nd targeted, readily synthesizable inhibitors of disease-relevant enzymes.
Results
Development of a genetically encoded objective
E. coli
is a versatile platform for building natural products from unculturable or low-yielding
organisms30,31. We hypothesized that a strain of
E. coli
programmed to detect the inactivation of PTP1B
(i.e., a genetically encoded objective) might enable the discovery of natural products that inhibit it (i.e.,
molecular solutions to the objective). To program such a strain, we assembled a bacterial two-hybrid
(B2H) system in which PTP1B and Src kinase control gene expression (Fig. 1a). In this system, Src
phosphorylates a substrate domain, enabling a protein-protein interaction that activates transcription of a
gene of interest (GOI). PTP1B dephosphorylates the substrate domain, preventing that interaction, and
the inactivation of PTP1B re-enables it.
E. coli
is a particularly good host for this detection system
because its proteome is suciently orthogonal to the proteome of
H. sapiens
to minimize off-target
growth defects that can result from the regulatory activities of Src and PTP1B (SI Note 1)32.
We carried out B2H development in several steps. To begin, we assembled a luminescent “base” system
in which Src modulates the binding of a substrate domain to an Src homology 2 (SH2) domain (Fig. 1b);
this system, which includes a chaperone that helps Src to fold (Cdc37)33, is similar to other B2H designs

Page 4/21
that detect protein-protein binding34. Unfortunately, our initial system did not yield a phosphorylation-
dependent transcriptional response, so we complemented it with inducible plasmids—each harboring a
different system component—to identify proteins with suboptimal expression levels (Fig. 1b).
Interestingly, secondary induction of Src increased luminescence, an indication that insucient substrate
phosphorylation and/or weak substrate-SH2 binding depressed GOI expression in our base system. We
modied this system by swapping in different substrate domains, by adding mutations to the SH2
domain thatenhance its anity for phosphopeptides35, and by removing the gene for Src—a modication
that allowed us to control expression exclusively from a second plasmid. With this conguration,
induction of Src increased luminescence most prominently for the MidT substrate (Fig. 1c), and
simultaneous induction of both Src and PTP1B prevented that increase—an indication of intracellular
PTP1B activity (Fig. 1d). We nalized the MidT system by incorporating genes for PTP1B and Src, by
adjusting promoters and ribosome binding sites to amplify its transcriptional response further (Fig. 1d;
Supplementary Figs. 1-2), and by adding a gene for spectinomcyin resistance (SpecR) as the GOI. The
nal plasmid-borne detection system required the inactivation of PTP1B to permit growth at high
concentrations of antibiotic (Fig. 1e).
Biosynthesis of PTP1B inhibitors
To search for inhibitors of PTP1B that bind outside of its active site, we coupled the B2H system with
metabolic pathways for terpenoids, a structurally diverse class of secondary metabolites with largely
nonpolar structures (Fig. 2a). Terpenoids include over 80,000 known compounds and represent nearly
one-third of all characterized natural products36 (the basis of approximately 50% of clinically approved
drugs37). To begin, we focused on a handful of structurally diverse terpenoids without established
inhibitory effects (Fig. 2b): amorphadiene, -humulene, -bisabolene, abietadiene, and taxadiene. Each
terpenoid pathway consisted of two plasmid-borne modules: (i) the mevalonate-dependent isoprenoid
pathway from
S. cerevisiae
(optimized for expression in
E. coli
38) and (ii) a terpene synthase
supplemented, when necessary for diterpenoid production, with a geranylgeranyl diphosphate synthase.
These modules generated terpenoids at titers of 0.3-18 mg/L in
E. coli
(Supplementary Fig. 3a).
We screened each pathway for its ability to produce inhibitors of PTP1B by transforming
E. coli
with
plasmids harboring both the pathway of interest and the B2H system (Fig. 2c). To our surprise, pathways
for amorphadiene and -bisabolene permitted survival at high concentrations of antibiotic. Critically, GC-
MS traces conrmed that all pathways generated terpenoids in the presence of the B2H system (Fig. 2d,
Supplementary Fig. 3b,c), and maximal resistance of the amorphadiene- and -bisabolene-producing
strains required both an active terpene synthase and a functional B2H system (Supplementary Fig. 3d).
We conrmed the inhibitory effects of puried terpenoids by examining their inuence on PTP1B-
catalyzed hydrolysis of p-nitrophenyl phosphate (pNPP; Fig. 2e, Supplementary Table 13). The IC50’s for
amorphadiene and -bisabolene were 53 ± 8 μM and 13 ± 2 μM, respectively, in 10% DMSO (Fig. 2f).
These IC50’s are surprisingly strong for unfunctionalized hydrocarbons (i.e., they are similar to the IC50’s
of inhibitors that form hydrogen bonds and other stabilizing interactions with PTP1B21,39) and,

Page 5/21
importantly, resemble terpenoid concentrations in liquid culture (Fig. 2g), a nding consistent with
in vivo
inhibition. We note: Our estimates of potency and concentration are probably conservative. DMSO, which
we used as a cosolvent for kinetic assays, increases the solubility of nonpolar inhibitors but tends to
reduce their apparent potency (likely by increasing free energetic cost of desolvation40), and terpenoids
tend to accumulate intracellularly, where their concentrations can exceed extracellular levels by an order
of magnitude41. Our growth-coupled assays, kinetic assays, and production measurements, taken
together, indicate that amorphadiene and -bisabolene activate the B2H system by inhibiting PTP1B
inside the cell.
A scalable approach to molecular discovery
Our microbial strain provides a powerful tool for screening genes for their ability to generate novel PTP1B
inhibitors. Most terpenoids, as a case study, are not commercially available, and even when their
metabolic pathways are known, their biosynthesis, purication, and
in vitro
analysis is a resource-
intensive process that is dicult to parallelize with existing methods42. Our B2H system offers a potential
solution: It can identify inhibitor-synthesizing genes with a simple growth-coupled assay. We explored its
application to discovery efforts by using it to screen a diverse set of uncharacterized biosynthetic genes.
In brief, we carried out a bioinformatic analysis of the largest terpene synthase family (PF03936) by
building and annotating a cladogram of its 4,464 constituent members (Supplementary Figs. 4,5); from
here, we synthesized three uncharacterized genes from each of eight clades: six with no characterized
genes and two with some characterized genes (Fig. 3a). We reasoned that these 24 phylogenetically
diverse genes might encode enzymes with different product proles.
Guided by our initial screen, we searched for sesquiterpene inhibitors by pairing each of the
uncharacterized genes with the FPP pathway. To our surprise, six genes conferred a signicant survival
advantage (Fig. 3B), and maximal resistance required an active B2H system (Supplementary Fig. 6). Each
hit generated distinct product proles (Supplementary Fig. 7); we focused our analysis on A0A0C9VSL7,
which produced mostly (+)-1(10),4-cadinadiene as a major product (Fig. 3c, d). This terpenoid is a
structural analog of amorphadiene but has a weaker potency (IC50 = 165+33 uM); a titer is 33+18 uM
suggests that intracellular accumulation may allow it to inhibit PTP1B inside the cell. Our ability to detect
a weak inhibitor suggests that the B2H system can capture a broad set of scaffolds in molecular
discovery efforts. The purication and analysis of additional hits, the incorporation of isoprenoid
substrates of different sizes (e.g., GGPP), and the inclusion of more uncharacterized genes could expand
the scope of such efforts.
Biophysical analysis of PTP1B inhibitors
Allosteric inhibitors of PTPs are valuable starting points for drug development. These molecules bind
outside of the well conserved, positively charged active sites of PTPs and tend to have improved
selectivities and membrane permeabilities over substrate analogs21. Motivated by these considerations,
an early screen identied a benzbromarone derivative that inhibited PTP1B weakly (IC50 = 350 μM)

Page 6/21
without competing with substrates; subsequent optimization of this compound led to two improved
inhibitors (IC50’s = 8 and 22 μM) that bind to an allosteric site39 (Fig. 4a). Over the next 15 years, efforts to
nd new inhibitors that bind to this site—or other allosteric regions on the catalytic domain—have been
largely unsuccessful43. Benzbromarone derivatives are the only allosteric inhibitors with
crystallographically veried binding sites. (Although, an allosteric inhibitor that binds to a disordered
region of the full-length protein has been characterized with NMR25). New approaches for nding
allosteric inhibitors are clearly needed.
Our microbial system could grant access to new compounds that bind in unexpected ways.
Amorphadiene and -bisabolene provide examples. They are highly nonpolar and, thus, incapable of
engaging in the hydrogen bonds and electrostatic interactions on which most other PTP inhibitors
rely21,39. To examine their binding mechanisms in detail, we collected X-ray crystal structures of PTP1B
bound to amorphadiene and -bisabolol, a soluble analogue of -bisabolene that can be soaked into
protein crystals. Intriguingly, both molecules bind to the allosteric site targeted by benzbromarone
derivatives (Fig. 4a). Amorphadiene, however, causes the 7 helix to reorganize to create a hydrophobic
cleft (Fig. 4b); this type of reorganization is interesting because it is typically slow (micro- to
millisecond)44 and dicult to incorporate into computational ligand design45. By contrast, -bisabolol
wraps around F280 with an orientation orthogonal to that of the crystallized benzobromarone derivatives
(Fig. 4c). We note: -bisabolol is a ~ 20-fold weaker inhibitor than -bisabolene and may adopt a different
bound pose (Supplementary Fig. 9m); unfortunately, the low solubility of -bisabolene hindered direct
structural studies.
Our crystal structures suggest that both terpenoids adopt multiple bound conformations (i.e., the electron
density indicates regions of disorder; Supplementary Fig. 8 a,b). Molecular dynamics simulations provide
additional support for this behavior (Supplementary Fig. 8 c-4e) which, we note, is dicult to incorporate
into computational screens46. To probe the binding of amorphadiene and -bisabolene further, we carried
out several additional analyses. First, we examined the inhibition of PTP1B by dihydroartemisinic acid.
This structural analogue of amorphadiene has a carboxyl group that, according to our crystal structures,
should interfere with binding to the hydrophobic cleft created by the 7 helix (Fig. 4d). The IC50 of this
molecule was eight-fold higher than that of amorphadiene, a reduction in potency consistent with its
crystallographic pose (Fig. 4e, Supplementary Fig. 9l). Second, we assessed the inhibitory effects of
amorphadiene and -bisabolene against TC-PTP, the most closely related phosphatase to PTP1B. These
molecules inhibited TC-PTP ve- to six-fold less potently than PTP1B (Fig. 4f, Supplementary Fig. 9a-9k)
—a nding consistent with binding to the poorly conserved allosteric site. Importantly, this selectivity may
seem modest, but it matches or exceeds the selectivities of most pre-optimized inhibitors (including
benzobromarone derivatives) and is exceedingly rare for unfunctionalized hydrocarbons (particularly, in
light of their comparatively modest molecular weights)47. We assessed the contribution of the 7 helix to
selectivity by removing the equivalent region from both PTP1B and TC-PTP (Fig. 4f). This modication
caused a four-fold reduction in the selectivity of amorphadiene, but not -bisabolene—an effect consistent
with the unique involvement of the 7 helix in the binding of amorphadiene.

Page 7/21
Amorphadiene and -bisabolene are lipophilic molecules that could be valuable for their ability to pass
through the membranes of mammalian cells. To examine the biological activity of these molecules, we
incubated them with HEK293T/17 cells and used an enzyme-linked immunosorbent assay to measure
shifts in insulin receptor (IR) phosphorylation. IR is a receptor tyrosine kinase that undergoes PTP1B-
mediated dephosphorylation from the cytosolic side of the plasma membrane (PTP1B, in turn, localizes
to the endoplasmic reticulum of the cell). Both molecules increased IR phosphorylation over a negative
control (Fig. 4g, Supplementary Fig. 10). We checked for off-target contributions to this signal, in turn, by
repeating the ELISA with equivalent concentrations of dihydroartemisinic acid and -bisabolol. To our
satisfaction, both molecules led to a reduction in signal consistent with their reduced potencies.
Design of alternative PTP-specic objectives
We explored the versatility of our B2H system by assessing its ability to detect the inactivation of several
other diseases-relevant PTPs. In short, we swapped out the gene for PTP1B with genes for PTPN2,
PTPN6, or PTPN12; these enzymes are targets for immunotherapeutic enhancement48, the treatment of
ovarian cancer49, and acute myocardial infarction50, respectively. Their catalytic domains share 31-65%
sequence identity with the catalytic domain of PTP1B. Interestingly, the new B2H systems were
immediately functional; PTP inactivation permitted growth at high concentrations of spectinomycin (Fig.
5a). This nding suggests that our detection system can be easily extended to other members of the PTP
family.
PTP-specic B2H systems could facilitate the identication of natural products that selectively inhibit one
PTP over another. We explored this application by comparing the antibiotic resistance conferred by
PTP1B- and TC-PTP-specic systems in response to metabolic pathways for amorphadiene and -
bisabolene (Fig. 5b). As expected, the PTP1B-specic system permitted growth at higher concentrations
of antibiotic, a result consistent with the selectivity of both terpenoids for PTP1B. Indistinguishable
terpenoid titers between the two strains suggest that this survival advantage does not result from
difference in intracellular concentration (Fig. 5c). Findings thus indicate that a simple comparison of B2H
systems—a potential secondary screen—offers a simple approach for evaluating the selectivity PTP-
inhibiting gene products. Notably, high concentrations of inhibitors in two strains could swamp out
selective effects; in such cases, terpenoid levels could be reduced with lower mevalonate concentrations.
Discussion
This study addresses an important challenge of medicinal chemistry—the design of molecular structures
that inhibit disease-relevant enzymes—by using a desired biochemical activity (i.e., an objective) as a
genetically encoded constraint to guide molecular biosynthesis. This approach enabled the identication
of two selective, biologically active inhibitors of PTP1B, an elusive drug target54. These molecules are not
drugs, but they are promising scaffolds for lead development. Their mechanisms of modulation—which
elicit allosteric conformational changes yet appear to rely on loose, conformationally exible binding—are
unusual (and computationally elusive 55), and demonstrate the ability of microbial systems to nd new

Page 8/21
solutions to dicult challenges in molecular design. Our identication of unusual inhibitors in relatively
small libraries, in turn, suggests that microbial systems can access a rich molecular landscape that is not
eciently explored by existing approaches to molecular discovery.
The B2H system at the core of our approach is a valuable tool for identifying biologically active natural
products, which are structurally complex, dicult to synthesize, and often hidden in cryptic gene
clusters56. It has several key advantages over contemporary approaches to inhibitor discovery: (i) It
incorporates synthesizability as a search criterion—an important attribute of drug leads57. (ii) It is
scalable. We used a growth-coupled assay to screen 24 uncharacterized terpene synthases; this type of
assay is compatible with very large libraries (e.g., 1010 )58. (iii) It can use cellular machinery to stabilize
proteins (e.g., CDC37 for Src); this capability could facilitate the integration of unstable and/or disordered
targets. Future efforts to exploit these advantages by incorporating large libraries of mutated and/or
recongured pathways, alternative biosynthetic enzymes (e.g., cytochromes P450, halogenases, and
methyltransferases), or new classes of disease-relevant enzymes would be informative.
The B2H system also has important limits. When used alongside metabolic pathways, it links survival not
only to the potency of metabolites, but also to their titers, off-target effects, and pathway toxicities. These
limitations can be benecial; they bias the discovery process toward potent, readily synthesizable
inhibitors and could, thus, facilitate post-discovery efforts to improve the titers of interesting molecules59.
Nonetheless, they will exclude some types of structurally complex molecules that are dicult to
synthesize in
E. coli
. The use of similar activity-based screens in other organisms (e.g.,
Streptomyces
)
could be interesting.
The compatibility of our discovery approach with different PTPs is valuable in light of their increasingly
well validated potential as a rich—and essentially untapped—source of new therapeutic targets60. We
anticipate that some PTPs will require the use of chaperones and/or transcriptional adjustments to be
incorporated into B2H systems. Our systematic optimization of the PTP1B-based system provides an
experimental framework for exploring these modications. Side-by-side comparisons of B2H systems, in
turn, offer a promising strategy for evaluating inhibitor selectivity in secondary screens. In future work,
new varieties of objectives (e.g., B2H systems or genetic circuits that detect the selective inhibition—or,
perhaps, activation—of one PTP over another) could facilitate the discovery of molecules with
sophisticated mechanisms of modulation in primary screens. The versatility of genetically encoded
objectives highlights the power of using microbial systems to nd targeted, biologically active molecules.
Methods
Bacterial strains. We used
E. coli
DH10B, chemically competent NEB Turbo, or electrocompetent One Shot
Top10 (Invitrogen) to carry out molecular cloning and to perform preliminary analyses of terpenoid
production; we used
E. coli
BL2-DE31 to express proteins for
in vitro
studies; and we used
E. coli
s103061
for our luminescence studies and for all experiments involving terpenoid-mediated growth (i.e., evolution
studies).

Page 9/21
For all strains, we generated chemically competent cells by carrying out the following steps: (i) We plated
each strain on LB agar plates with the required antibiotics. (ii) We used one colony of each strain to
inoculate 1 mL of LB media (25 g/L LB with appropriate antibiotics listed in Table S2) in a glass culture
tube, and we grew this culture overnight (37℃, 225 RPM). (iii) We used the 1-mL culture to inoculate 100-
300 mL of LB media (as above) in a glass shake ask, and we grew this culture for several hours (37℃,
225 RPM). (iv) When the culture reached an OD of 0.3-0.6, we centrifuged the cells (4,000 x g for 10
minutes at 4℃), removed the supernatant, resuspended them in 30 mL of ice cold TFB1 buffer (30 mM
potassium acetate, 10 mM CaCl2, 50 mM MnCl2, 100 mM RbCl, 15% v/v glycerol, water to 200 mL,
pH=5.8, sterile ltered), and incubated the suspension at 4℃ for 90 min. (v) We repeated step iv, but
resuspended in 4 mL of ice cold TFB2 buffer (10 mM MOPS, 75 mM CaCl2, 10 mM RbCl2, 15% glycerol,
water to 50 mL, pH=6.5, sterile ltered). (iv) We split the nal suspension into 100 μL aliquots and froze
them at -80℃ until further use.
We generated electrocompetent cells by following an approach similar to the one above. In step iv,
however, we resuspended the cells in 50 mL of ice cold MilliQ water and repeated this step twice—rst
with 50 mL of 20% sterile glycerol (ice cold) and, then, with 1 mL of 20% sterile glycerol (ice cold). We
froze the pellets as before.
Materials. We purchased methyl abietate from Santa Cruz Biotechnology; trans-caryophyllene, tris(2-
carboxyethyl)phosphine (TCEP), bovine serum albumin (BSA), M9 minimal salts, phenylmethylsulfonyl
uoride (PMSF), and DMSO (dimethyl sulfoxide) from Millipore Sigma; glycerol, bacterial protein
extraction reagent II (B-PERII), and lysozyme from VWR; cloning reagents from New England Biolabs;
amorphadiene from Ambeed, Inc.; and all other reagents (e.g., antibiotics and media components) from
Thermo Fisher. Taxadiene was a kind gift from Phil Baran of the The Scripps Research Institute. We
prepared mevalonate by mixing 1 volume of 2 M DL-mevalanolactone with 1.05 volumes of 2 M KOH and
incubating this mixture at 37°C for 30 minutes.
Cloning and molecular biology. We constructed all plasmids by using standard methods (i.e., restriction
digest and ligation, Golden Gate and Gibson assembly, Quikchange mutagenesis, and circular
polymerase extension cloning). Table S1 describes the source of each gene; Tables S2 and S3 describe
the composition of all nal plasmids.
We began construction of the B2H system by integrating the gene for HA4-RpoZ from pAB094a into
pAB078d and by replacing the ampicillin resistance marker of pAB078d with a kanamycin resistance
marker (Gibson Assembly). We modied the resulting “combined” plasmid, in turn, by replacing the HA4
and SH2 domains with kinase substrate and substrate recognition (i.e., SH2) domains, respectively
(Gibson assembly), and by integrating genes for Src kinase, CDC37, and PTP1B in various combinations
(Gibson assembly). We nalized the functional B2H system by modifying the SH2 domain with several
mutations known to enhance its anity for phosphopeptides (K15L, T8V, and C10A, numbered as in
Kaneko et. al.35), by exchanging the GOI for luminescence (LuxAB) with one for spectinomycin resistance
(SpecR), and by toggling promoters and ribosome binding sites to enhance the transcriptional response

Page 10/21
(Gibson assembly and Quickchange Mutagenesis, Agilent Inc.). We note: For the last step, we also
converted Pro1 to ProD by using the Quikchange protocol. When necessary, we constructed plasmids with
arabinose-inducible components by cloning a single component from the B2H system into pBAD (Golden
Gate assembly). Tables S4-S6 list the primers and DNA fragments used to construct each plasmid.
We assembled pathways for terpenoid biosynthesis by purchasing plasmids encoding the rst module
(pMBIS) and various sesquiterpene synthases (ADS or GHS in pTRC99a) from Addgene, and by building
the remaining plasmids. We replaced the tetracycline resistance in pMBIS with a gene for
chloramphenicol resistance to create pMBISCmR. We integrated genes for ABS, TXS, ABA, and GGPPS into
pTRC99t (i.e., pTRC99a without BsaI sites). Tables S4-S6 list the primers and DNA fragments used to
construct each plasmid.
Luminescence assays. We characterized preliminary B2H systems (which contained LuxAB as the GOI)
with luminescence assays. In brief, we transformed necessary plasmids into
E. coli
s1030 (Table S2),
plated the transformed cells onto LB agar plates (20 g/L agar, 10 g/L tryptone, 10 g/L sodium chloride,
and 5 g/L yeast extract with antibiotics described in Table S2), and incubated all plates overnight at 37°C.
We used individual colonies to inoculate 1 ml of terric both (TB at 2%, or 12 g/L tryptone, 24 g/L yeast
extract, 12 mL/L 100% glycerol, 2.28 g/L KH2PO4, 12.53 g/L K2HPO4, pH = 7.3, and antibiotics described
in Table S2), and we incubated these cultures overnight (37°C and 225 RPM). The following morning, we
diluted each culture by 100-fold into 1 ml of TB media (above), and we incubated these cultures in
individual wells of a deep 96-well plate for 5.5 hours (37°C, 225 RPM). (We note: When pBAD was present,
we supplemented the TB media with 0-0.02 w/v % arabinose). We transferred 100μL of each culture into
a single well of a standard 96-well clear plate and measured both OD600 and luminescence on a Biotek
Synergy plate reader (gain: 135, integration time: 1 second, read height: 1 mm). Analogous
measurements of cell-free media allowed us to measure background signals, which we subtracted from
each measurement prior to calculating OD-normalized luminescence (i.e., Lum / OD600).
Analysis of antibiotic resistance. We evaluated the spectinomycin resistance conferred by various B2H
systems in the absence of terpenoid pathways by carrying out the following steps: (i) We transformed
E.
coli
with the necessary plasmids (Table S2) and plated the transformed cells onto LB agar plates (20 g/L
agar, 10 g/L tryptone, 10 g/L sodium chloride, 5 g/L yeast extract, 50 μg/ml kanamycin, 10 μg/ml
tetracycline). (ii) We used individual colonies to inoculate 1-2 ml of TB media (12 g/L tryptone, 24 g/L
yeast extract, 12 mL/L 100% glycerol, 2.28 g/L KH2PO4, 12.53 g/L K2HPO4, 50 μg/ml kanamycin, 10
μg/ml tetracycline, pH = 7.3), and we incubated these cultures overnight (37°C, 225 RPM). In the morning,
we diluted each culture by 100-fold into 4 ml of TB media (as above) with 0-500 μg/ml spectinomycin
(we used spectinomycin in the liquid culture only for Figure S2), and we incubated these cultures in deep
24-well plates until wells containing 0 μg/ml spectinomycin reached an OD600 of 0.9-1.1. (iv) We diluted
each 4-ml culture by 10-fold into TB media with no antibiotics and plated 10-μL drops of the diluent onto
agar plates with various concentrations of spectinomycin. (v) We incubated plates overnight (37°C) and
photographed them the following day.

Page 11/21
To examine terpenoid-mediated resistance, we began with steps i and ii as described above with the
addition of 34 μg/ml chloramphenicol and 50 μg/ml carbenicillin in all liquid/solid media. We then
proceeded with the following steps: (iii) We diluted samples from 1-ml cultures to an OD600 of 0.05 in 4.5
ml of TB media (supplemented with 12 g/L tryptone, 24 g/L yeast extract, 12 mL/L 100% glycerol, 2.28
g/L KH2PO4, 12.53 g/L K2HPO4, 50 μg/ml kanamycin, 10 μg/ml tetracycline, 34 μg/ml chloramphenicol,
and 50 μg/ml carbenicillin), which we incubated in deep 24-well plates (37°C, 225 RPM). (iv) At an OD600
of 0.3-0.6, we transferred 4 ml of each culture to a new well of a deep 24-well plate, added 500 μM
isopropyl β-D-1-thiogalactopyranoside (IPTG) and 20 mM of mevalonate, and incubated for 20 hours
(22°C, 225 RPM). (v) We diluted each 4-ml culture to an OD600 of 0.1 with TB media and plated 10 μL of
the diluent onto either LB or TB plates supplemented with 500 μM IPTG, 20 mM mevalonate, 50 μg/ml
kanamycin, 10 μg/ml tetracycline, 34 μg/ml chloramphenicol, 50 μg/ml carbenicillin, and 0-1200 μg/ml
spectinomycin (for both plates, we used 20 g/L agar with media and buffer components described
above).
Terpenoid biosynthesis. We prepared
E. coli
for terpenoid production by transforming cells with plasmids
harboring requisite pathway components (Table S2) and plating them onto LB agar plates (20 g/L agar,
10 g/L tryptone, 10 g/L sodium chloride, and 5 g/L yeast extract with antibiotics described in Table S2).
We used one colony from each strain to inoculate 2 ml TB (12 g/L tryptone, 24 g/L yeast extract, 12 mL/L
100% glycerol, 2.28 g/L KH2PO4, 12.53 g/L K2HPO4, pH = 7.0, and antibiotics described in Table S2) in a
glass culture tube for ~16 hours (37°C and 225 RPM). We diluted these cultures by 75-fold into 10 ml of
TB media and incubated the new cultures in 125 mL glass shake asks (37°C and 225 RPM). At an OD600
of 0.3-0.6, we added 500 μM IPTG and 20 mM mevalonate. After 72-88 hours of growth (22°C and 225
RPM), we extracted terpenoids from each culture as outlined below. Table S9 lists exact sample sizes,
culture volumes, and fermentation times.
Protein expression and purication. We expressed and puried PTPs as described previously (Hjortness et
al., 2018). Briey, we transformed
E. coli
BL21(DE3) cells with pET16b or pET21b vectors (see Table S2
for details), and we induced with 500 μM IPTG at 22°C for 20 hours. We puried PTPs from cell lysate by
using desalting, nickel anity, and anion exchange chromatography (HiPrep 26/10, HisTrap HP, and
HiPrep Q HP, respectively; GE Healthcare). We stored the nal protein (30–50 μM) in HEPES buffer (50
mM, pH 7.5, 0.5 mM TCEP) in 20% glycerol at −80°C.
Extraction and purication of terpenoids. We used hexane to extract terpenoids generated in liquid
culture. For 10-mL cultures, we added 14 mL of hexane to 10 ml of culture broth in 125-mL glass shake
asks, shook the mixture (100 RPM) for 30 minutes, centrifuged it (4000 x g), and withdrew 10 mL of the
hexane layer for further analysis. For 4-mL cultures, we added 600 μL hexane to 1 mL of culture broth in a
microcentrifuge tube, vortexed the tubes for 3 minutes, centrifuged the tubes for 1 minute (17000 x g),
and saved 300-400 μL of the hexane layer for further analysis.
To purify amorphadiene, -bisabolene, and (+)-1(10),4-cadinadiene, we supplemented 500-1000 mL
culture broth with hexane (16.7% v/v), shook the mixture for 30 minutes (100 RPM), isolated the hexane

Page 12/21
layer with a separatory funnel, centrifuged the isolated organic phase (4000 x g), and withdrew the
hexane layer. To concentrate the terpenoid products, we evaporated excess hexane in a rotary evaporator
to bring the nal volume to 500 μL, and we passed the resulting mixture over a silica gel 1-3 times
(Sigma-Aldrich; high purity grade, 60 Å pore size, 230-400 mesh particle size). We analyzed elution
fractions (100% hexane) on the GC/MS and pooled fractions with the compound of interest
(amorphadiene). Once puried, we dried pooled fractions under a gentle stream of air, resuspended the
terpenoid solids in DMSO, and quantied the nal samples as outlined below. We repeated the
purication process until samples (in DMSO) were >95% pure by GC/MS unless otherwise noted.
GC-MS analysis of terpenoids. We measured terpenoids generated in liquid culture with a gas
chromatograph / mass spectrometer (GC-MS; a Trace 1310 GC tted with a TG5-SilMS column and an
ISQ 7000 MS; Thermo Fisher Scientic). We prepared all samples in hexane (directly or through a 1:100
dilution of DMSO) with 20 μg/ml of caryophyllene or methyl abietate as an internal standard. Highly
concentrated samples were diluted 10-20x prior to preparation to bring concentrations within the MS
detection limit. When the peak area of an internal standard exceeded ± 40% of the average area of all
samples containing that standard, we re-analyzed the corresponding samples. For all runs, we used the
following GC method: hold at 80°C (3min), increase to 250°C (15°C/min), hold at 250°C (6 min), increase
to 280°C (30°C/min), and hold at 280°C (3 min). To identify various analytes, we scanned m/z ratios from
50 to 550.
We examined sesquiterpenes generated by variants of ADS by using select ion mode (SIM) to scan for
the molecular ion (m/z =204). For quantication, we used Eq. 1: where Ai
is the area of the peak produced by analyte i, Astd is the area of the peak produced by Cstd of
caryophyllene in the sample, and R is the ratio of response factors for caryophyllene and amorphadiene
in a reference sample. Tables S12-14 provide the concentrations of all standards and reference
compounds used in this analysis.
We quantied sesquiterpenes generated by variants of GHS by using the aforementioned procedure with
several modications: We used methyl abietate as an internal standard (several mutants of GHS generate
caryophyllene as a product); we scanned for both m/z = 204 and m/z = 121, a common ion between
sesquiterpenes and methyl abietate; we used a ratio of response factors for amorphadiene and methyl
abietate at m/z = 121 for R; and we calculated peak areas at m/z = 121. We focused our analysis on
peaks with areas that exceeded 1% of the total area of all peaks at m/z=204.

Page 13/21
We quantied diterpenoids by, once again, accompanying our general procedure with several
modications: We scanned for a different molecular ion (m/z = 272) and an ion common to both
diterpenoids and caryophyllene (m/z=93); we used a ratio of response factors for pure taxadiene (a kind
gift from Phil Baran) and caryophyllene at m/z = 93; and we calculated peak areas m/z = 93. For all
analyses, we examined only peaks with areas that exceeded 1% of the total area of all peaks at m/z=272.
We identied molecules by using the NIST MS library and, when necessary, conrmed this identication
with analytical standards or mass spectra reported in the literature. We note: The assumption of a
constant response factor for different terpenoids (that is, the assumption that all sesquiterpenes and
diterpenes ionize like amorphadiene and taxadiene, respectively) can certainly yield error in estimates of
their concentrations; our analyses, which are consistent with those of other studies of terpenoid
production in microbial systems62,63, supply rough estimates of concentrations for all compounds except
amorphadiene and taxadiene (which had analytical standards).
Bioinformatics. We used a bioinformatic analysis to identify a phylogenetically diverse set of terpene
synthases. Briey, we downloaded (i) all constituent genes of PF03936 (the largest terpene synthase
family grouped by a C-terminal domain) from the PFAM Database and (ii) all enzymes with Enzyme
Commission (EC) number of 4.2.3.# from the Uniprot Database; this string, which denes carbon oxygen
lyases that act on phosphates, includes terpene synthases. We cleaned both datasets in Excel (i.e., we
ensured that every identier had only one row), and we used a custom R script to designate each
PF03936 member as characterized (i.e., in possession of a Uniprot-based EC number) or uncharacterized.
Finally, we used FastTree64 to create a phylogenetic tree of the PF03936 family and the R-package
ggtree65 to visualize the resulting tree and function data as a cladogram and heatmap.
After annotating the cladogram by hand, we selected three genes from each of six clades: six with no
characterized genes and two with some characterized genes. We avoided clades proximal to known
monoterpene synthases or diterpene synthases known to act on GGPP isomers absent in our system
(e.g., ent-copalyl diphosphate); these enzymes are unlikely to act on FPP, the primary product of
pMBISCmR. When selecting enzymes within clades, we biased our choice towards bacterial/fungal
species and selected genes with a minimal number of common ancestors within the clade. The selected
genes were synthesized and cloned into the pTrc99a vector by Twist Biosciences and assayed for
antibiotic resistance as described above.
Enzyme kinetics. To examine terpenoid-mediated inhibition, we measured PTP1B or TCPTP-catalyzed
hydrolysis of p-nitrophenyl phosphate (pNPP) in the presence of various concentrations of terpenoids.
Each reaction included PTP (0.05 μM in 50 mM HEPES, 0.5 mM TCEP, 50 µg/ml BSA), pNPP (0.33, 0.67, 2,
5, 10, and 15 mM), inhibitor (with concentrations listed in the gures), buffer (50 mM HEPES pH=7.3, 50
µg/ml BSA), and DMSO at 10% v/v. We monitored the formation of p-nitrophenol by measuring
absorbance at 405 nm every 10 seconds for 5 minutes on a SpectraMax M2 plate reader. We report exact
sample sizes (i.e., the number of independently prepared reactions) in Supplementary Table 10.

Page 14/21
We used a custom MATLAB script to process all raw kinetic data. This script removed all concentration
values that fell outside of either (i) the range of our standard curve (absorbance vs. μM; Supplementary
Fig. 18) or (ii) the initial rate regime (>10% of the pNPP concentration used in the assay). When this step
reduced kinetic dataset to fewer than ten points, we re-measured those datasets to collect at least ten. We
t nal datasets, in turn, with a linear regression model (using Matlab’s backslash operator). 
We evaluated kinetic models in three steps: (i) We t initial-rate measurements collected in the absence
and presence of inhibitors to Michaelis-Menten and inhibition models, respectively (here, we used the
nlint
and
fminsearch
functions from MATLAB; Supplementary Table 13). (ii) We used an F-test to
compare the mixed model to the single-parameter model with the least sum squared error (here, we used
the
fcdf
function from MATLAB to assign p-values), and we accepted the mixed model when p < 0.05. (iii)
We used the Akaike's Information Criterion (AIC) to compare the best-t single parameter model to each
alternative single parameter model, and we accepted the “best-t” model when the difference in AIC (Δi)
exceed 5 for all comparisons.66 We note: For amorphadiene, -bisabolene, and (+)1-(10),4-cadinadiene
this criterion was not met; both noncompetitive and uncompetitive models, however, yielded
indistinguishable IC50’s.
We estimated the half maximal inhibitory concentration (IC50) of inhibitors by using the best-t kinetic
models to determine the concentration of inhibitor required to reduce initial rates of PTP-catalyzed
hydrolysis of 15 mM of pNPP by 50%. We used the MATLAB function “nlparci” to determine the
condence intervals of kinetic parameters, and we propagated those intervals to estimate corresponding
condence intervals for each IC50.
X-ray crystallography. We prepared crystals of PTP1B by using hanging drop vapor diffusion. In brief, we
added 2 μL of PTP1B (~600 μM PTP1B, 50 mM HEPES, pH 7.3) to 6 μL of crystallization solution (100
mM HEPES, 200 mM magnesium acetate, and 14% polyethylene glycol 8000, pH 7.5) and incubated the
resulting droplets over crystallization solution for one week at 4°C (EasyXtal CrystalSupport, Qiagen). We
soaked crystals with ligand by transferring them to droplets formed with 6 μL of crystallization solution
and 1 μL of ligand solution (10 mM in DMSO), which we incubated for 2-5 days at 4°C. We prepared all
ligands for freezing by soaking them in cryoprotectant formed from a 70/30 (v/v) mixture of buffer (100
mM HEPES, 200 mM magnesium acetate, and 25% polyethylene glycol 8000, pH 7.5) and glycerol.
We collected X-ray diffraction data through the Collaborative Crystallography Program at Lawrence
Berkeley National Lab (ALS ENABLE, beamline 8.2.1, 100 K, 1.00003 Å). We performed integration,
scaling, and merging of X-ray diffraction data using the xia2 software package67, and we carried out
molecular replacement and structure renement with the PHENIX graphical interface,68 supplemented
with manual model adjustment in COOT69 and one round of PDB-REDO70 (the latter, only for the PTP1B-
amorphadiene complex).
Molecular dynamics (MD) simulations. We performed MD simulations using GROMACS 202071. Briey,
we used the CHARMM36m protein force eld72, a CHARMM-modied TIP3P water model73, and ligand

Page 15/21
parameters generated by CGenFF74,75. We solvated each PTP1B-ligand complex (initialized from the
corresponding crystal structure) in a dodecahedral box with edges positioned ≥ 10 Å from the surface of
the complex, and we added sodium ions (three for amorphadiene and one for -bisabolol) to neutralize
each system. We used the LINCS algorithm76 to constrain all bonds involving hydrogen atoms, the Verlet
leapfrog algorithm to numerically integrate equations of motion with a 2-fs time step, and the particle-
mesh Ewald summation77 (cubic interpolation with a grid spacing of 0.16 nm) to calculate long-range
electrostatic interactions; we used a cutoff of 1.2 nm, in turn, for short-range electrostatic and Lennard-
Jones interactions. We independently coupled the protein-ligand complex and solvent molecules to a
temperature bath (300K) using a modied Berendsen thermostat78 with a relaxation time of 0.1 ps, and
we xed pressure coupling to 1 bar using the Parrinello–Rahman algorithm79 with a relaxation time of 2
ps and isothermal compressibility of 4.5 × 10−5 bar-1.
For each system, we carried out 30 independent MD simulations to reduce sampling bias. For each MD
trajectory, we minimized energy using the steepest decent method followed by 100-ps solvent relaxation
in the NVT ensemble and 100-ps solvent relaxation in the NPT ensemble. After an additional 1-ns NPT
equilibration, we carried out production runs for 1 ns in the NPT ensemble and registered coordinate data
every 10 ps.
Analysis of PTP1B inhibition in HEK293TCells . We prepared HEK293T/17 cells for an enzyme-linked
immunosorbent assay (ELISA) by growing them in 75 cm2culture asks (Corning) with DMEM media
supplemented with 10% FBS, 100 units/ml penicillin, and 100 units/ml streptomycin. We replaced the
media every day for 3-5 days until the cells reached 80-100% conuency.
We measured the inuence of inhibitors on insulin receptor (IR) phosphorylation by using an IR-specic
ELISA (Supplementary Fig. 10a). Briey, we starved cells for 48 hours in FBS-free media and incubated
the with inhibitors (all at 3% DMSO) for 10 minutes. After incubation, we lysed cells with lysis buffer
(9803, Cell Signaling Technology) supplemented with 1X halt phosphatase inhibitor cocktail and 1X halt
protease inhibitor cocktail (Thermo Fisher Scientic) for 10 min, pelleted the cell debris, and used the lysis
buffer to dilute each sample to 60 mg/ml total protein. We measured IR phosphorylation in subsequent
dilutions of the 60 mg/ml samples with the PathScan® Phospho-Insulin Receptor β (panTyr) Sandwich
ELISA Kit (Cell Signaling Technology; #7082). We note: To identify biologically active concentrations of -
bisabolene and amorphadiene, we screened several concentrations and chose those that gave the
highest signal (405 μM for -bisabolene and 930 μM for amorphadiene); similar concentrations of weak
inhibitors did not yield a detectable signal (Supplementary Fig. 10b,c).
Statistical analysis and reproducibility. We determined statistical signicance (Figs 3g) with a two-tailed
Student’s t-test (details in Supplementary Tables 11 and 15), and we used an F-test to compare one- and
two-parameter models of inhibition (Supplementary Table 13).
Reporting Summary. Further information on research design is available in the Nature Research Reporting
Summary linked to this data.

Page 16/21
Data availability. The plasmids generated in this study are available on Addgene
(https://www.addgene.org/) or from the authors. All code generated for data analysis is available upon
request. Source data for our gures is available as follows: Supplementary Table 7 (Fig. 1b-d,
Supplementary Fig. 1), Supplementary Table 8 (Figs. 1e, 2c, 4b; Supplementary Figs. 2, 3d, 9,11,12),
Supplementary Table 9 (Fig. 2g, 4c, 5c; Supplementary Fig. 4a), Supplementary Table 10 (Fig. 2e, 3g, 4e;
Supplementary Fig. 5d-o), Supplementary Table 11 (Fig. 4g; Supplementary Fig. 6b-c). The crystal
structures determined in this study are available from the RCSB Protein Data Bank (PDB entry 6w30,
6w31). Table 14 provides renement statistics for both structures.
Declarations
ACKNOWLEDGEMENTS
This work was supported by funds provided by the National Science Foundation (A.S., E.Y.K, and J.M.F.,
award 1750244) and (A.H., award 1804897), and by the National Research Foundation of Korea (J.M.C. –
Basic Science Research Program, award 2019R1A6A1A10073887; T.J. and H.K. – Creative Materials
Discovery Program, award 2017M3D1A1039378). We thank Banumathi Sankaran for assistance with
data collection (Advanced Light Source; beamline 8.2.1). The Berkeley Center for Structural Biology is
supported in part by the National Institutes of Health, National Institute of General Medical Sciences, and
the Howard Hughes Medical Institute. The Advanced Light Source is supported by the Director, Oce of
Science, Oce of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-
05CH11231. The Pilatus detector was funded under NIH grant S10OD021832. The ALS-ENABLE
beamlines are supported in part by the National Institutes of Health, National Institute of General Medical
Sciences, grant P30 GM124169).
AUTHOR CONTRIBUTIONS
A.S. and J.M.F. conceived of research. A.S., E.Y.K, J.M.F, and A.H. designed experiments. A.S. and E.Y.K
constructed plasmids, mutant libraries, and
E. coli
strains. A.S. carried out metabolic engineering,
evolution studies, kinetic measurements, and GC/MS analyses. A.H. performed ELISA experiments and
assisted with protein purication. A.S. grew crystals, and J.M.F. assisted with structural renement. T.J.,
H.K., and J.M.C. carried out molecular dynamics simulations. All authors analyzed data. A.S. and J.M.F.
wrote the paper.
COMPETING INTERESTS
A.S., A.H., and J.M.F. are inventors on patent applications that include data from this manuscript. J.M.F. is
a founder of Think Bioscience. The remaining authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information available in the online version of this paper

Page 17/21
Correspondence and requests for materials should be addressed to J.M.F
References
1. Olsson, T. S. G., Williams, M. a., Pitt, W. R. & Ladbury, J. E. The Thermodynamics of Protein-Ligand
Interaction and Solvation: Insights for Ligand Design.
J. Mol. Biol.
384, 1002–1017 (2008).
2. Fox, J. M., Zhao, M., Fink, M. J., Kang, K. & Whitesides, G. M. The Molecular Origin of
Enthalpy/Entropy Compensation in Biomolecular Recognition.
Annu. Rev. Biophys.
47, (2018).
3. Mobley, D. L. & Gilson, M. K. Predicting Binding Free Energies: Frontiers and Benchmarks.
Annu. Rev.
Biophys.
46, 531–558 (2017).
4. Hert, J., Irwin, J. J., Laggner, C., Keiser, M. J. & Shoichet, B. K. Quantifying biogenic bias in screening
libraries.
Nat. Chem. Biol.
5, pages479–483 (2009).
5. Smanski, M. J.
et al.
Synthetic biology to access and expand nature’s chemical diversity.
Nature
Reviews Microbiology
14, 135–149 (2016).
6. Fürstenberg-Hägg, J., Zagrobelny, M. & Bak, S. Plant defense against insect herbivores.
International
Journal of Molecular Sciences
14, 10242–10297 (2013).
7. Maier, M. E. Design and synthesis of analogues of natural products.
Organic and Biomolecular
Chemistry
13, 5302–5343 (2015).
8. Chen, M. S. & White, M. C. A predictably selective aliphatic C-H oxidation reaction for complex
molecule synthesis.
Science (80-. ).
318, 783–787 (2007).
9. Cho, I., Jia, Z. J. & Arnold, F. H. Site-selective enzymatic C-H amidation for synthesis of diverse
lactams.
Science (80-. ).
364, 575–578 (2019).
10. Atanasov, A. G.
et al.
A Historical overview of natural products in drug discovery.
Metabolites
33,
1582–1614 (2012).
11. Paul, S. M.
et al.
How to improve RD productivity: The pharmaceutical industry’s grand challenge.
Nature Reviews Drug Discovery
9, 203–214 (2010).
12. Li, J. W. H. & Vederas, J. C. Drug discovery and natural products: End of era or an endless frontier?
Biomeditsinskaya Khimiya
57, 148–160 (2011).
13. Jensen, P. R., Chavarria, K. L., Fenical, W., Moore, B. S. & Ziemert, N. Challenges and triumphs to
genomics-based natural product discovery.
Journal of Industrial Microbiology and Biotechnology
41,
203–209 (2014).
14. Medema, M. H.
et al.
AntiSMASH: Rapid identication, annotation and analysis of secondary
metabolite biosynthesis gene clusters in bacterial and fungal genome sequences.
Nucleic Acids Res.
39, W339–W346 (2011).
15. Jensen, P. R. Natural Products and the Gene Cluster Revolution.
Trends Microbiol.
24, 968–977
(2016).
16. Yan, Y.
et al.
Resistance-gene-directed discovery of a natural-product herbicide with a new mode of
action.
Nature
559, 415–418 (2018).

Page 18/21
17. Culp, E. J.
et al.
Evolution-guided discovery of antibiotics that inhibit peptidoglycan remodelling.
Nature
578, 582–587 (2020).
18. Zhabinskii, V. N., Khripach, N. B. & Khripach, V. A. Steroid plant hormones: Effects outside plant
kingdom.
Steroids
97, 87–97 (2015).
19. Li, Y.
et al.
Complete biosynthesis of noscapine and halogenated alkaloids in yeast.
Proc. Natl. Acad.
Sci. U. S. A.
115, E3922–E3931 (2018).
20. Luo, X.
et al.
Complete biosynthesis of cannabinoids and their unnatural analogues in yeast.
Nature
567, 123–126 (2019).
21. He, R., Yu, Z., Zhang, R. & Zhang, Z. Protein tyrosine phosphatases as potential therapeutic targets.
Acta Pharmacol. Sin.
35, 1227–1246 (2014).
22. Paul, M. K. & Mukhopadhyay, A. K. Tyrosine kinase – Role and signicance in Cancer.
Int. J. Med. Sci.
1, 101–115 (2012).
23. Ferguson, F. M. & Gray, N. S. Kinase inhibitors: The road ahead.
Nature Reviews Drug Discovery
17,
353–376 (2018).
24. Stanford, S. M. & Bottini, N. Targeting Tyrosine Phosphatases: Time to End the Stigma.
Trends in
Pharmacological Sciences
38, 524–540 (2017).
25. Krishnan, N.
et al.
Targeting the disordered C terminus of PTP1B with an allosteric inhibitor.
Nat.
Chem. Biol.
10, 558–566 (2014).
26. Barr, A. J.
et al.
Large-Scale Structural Analysis of the Classical Human Protein Tyrosine
Phosphatome.
Cell
136, 352–363 (2009).
27. Banno, R.
et al.
PTP1B and SHP2 in POMC neurons reciprocally regulate energy balance in mice.
J.
Clin. Invest.
120, 720–734 (2010).
28. Zabolotny, J. M.
et al.
Protein-tyrosine phosphatase 1B expression is induced by inammation in
vivo.
J. Biol. Chem.
283, 14230–14241 (2008).
29. Matulka, K.
et al.
PTP1B is an effector of activin signaling and regulates neural specication of
embryonic stem cells.
Cell Stem Cell
13, 706–719 (2013).
30. Zhang, H., Wang, Y., Wu, J., Skalina, K. & Pfeifer, B. A. Complete biosynthesis of erythromycin A and
designed analogs using E. coli as a heterologous host.
Chem. Biol.
17, 1232–1240 (2010).
31. Antosch, J., Schaefers, F. & Gulder, T. A. M. Heterologous Reconstitution of Ikarugamycin
Biosynthesis in
E. coli
.
Angew. Chemie Int. Ed.
53, 3011–3014 (2014).
32. Montalibet, J. & Kennedy, B. P. Using yeast to screen for inhibitors of protein tyrosine phosphatase
1B.
Biochem. Pharmacol.
68, 1807–1814 (2004).
33. Piserchio, A., Cowburn, D. & Ghose, R. Expression and purication of Src-family kinases for solution
NMR studies.
Methods Mol. Biol.
831, 111–131 (2012).
34. Badran, A. H.
et al.
Continuous evolution of Bacillus thuringiensis toxins overcomes insect
resistance.
Nature
533, 58–63 (2016).
35. Kaneko, T.
et al.
Superbinder SH2 domains act as antagonists of cell signaling.
Sci. Signal.
5, (2012).

Page 19/21
36. Christianson, D. W. Structural and Chemical Biology of Terpenoid Cyclases.
Chem. Rev.
117, 11570–
11648 (2017).
37. Newman, D. J. & Cragg, G. M. Natural Products as Sources of New Drugs from 1981 to 2014.
Journal
of Natural Products
79, 629–661 (2016).
38. Martin, V. J. J., Pitera, D. J., Withers, S. T., Newman, J. D. & Keasling, J. D. Engineering a mevalonate
pathway in Escherichia coli for production of terpenoids.
Nat. Biotechnol.
21, 796–802 (2003).
39. Wiesmann, C.
et al.
Allosteric inhibition of protein tyrosine phosphatase 1B.
Nat. Struct. Mol. Biol.
11,
730–737 (2004).
40. Hjortness, M. K.
et al.
Abietane-Type Diterpenoids Inhibit Protein Tyrosine Phosphatases by
Stabilizing an Inactive Enzyme Conformation.
Biochemistry
57, 5886–5896 (2018).
41. Zhang, C., Chen, X., Stephanopoulos, G. & Too, H. P. Eux transporter engineering markedly improves
amorphadiene production in Escherichia coli.
Biotechnol. Bioeng.
113, 1755–1763 (2016).
42. Hubert, J., Nuzillard, J. M. & Renault, J. H. Dereplication strategies in natural product research: How
many tools and methodologies behind the same concept?
Phytochemistry Reviews
16, 55–95
(2017).
43. Keedy, D. A.
et al.
An expanded allosteric network in PTP1B by multitemperature crystallography,
fragment screening, and covalent tethering.
Elife
7, doi: 10.7554/eLife.36307 (2018).
44. Vallurupalli, P., Bouvignies, G. & Kay, L. E. Studying ‘invisible’ excited protein states in slow exchange
with a major state conformation.
J. Am. Chem. Soc.
134, 8148–8161 (2012).
45. Amamuddy, O. S.
et al.
Integrated computational approaches and tools for allosteric drug discovery.
Int. J. Mol. Sci.
21, 847 (2020).
46. Menchon, G., Maveyraud, L. & Czaplicki, G. Molecular dynamics as a tool for virtual ligand screening.
in
Methods in Molecular Biology
1762, 145–178 (Humana Press Inc., 2018).
47. Shimada, T.
et al.
Selectivity of Polycyclic Inhibitors for Human Cytochrome P450s 1A1, 1A2, and
1B1.
Chem. Res. Toxicol.
11, 1048–1056 (1998).
48. Manguso, R. T.
et al.
In vivo CRISPR screening identies Ptpn2 as a cancer immunotherapy target.
Nature
547, 413–418 (2017).
49. Varone, A., Spano, D. & Corda, D. Shp1 in Solid Cancers and Their Therapy.
Frontiers in Oncology
10,
935 (2020).
50. Yang, C. F.
et al.
Targeting protein tyrosine phosphatase PTP-PEST (PTPN12) for therapeutic
intervention in acute myocardial infarction.
Cardiovasc. Res.
116, 1032–1046 (2020).
51. Li, H.
et al.
Crystal Structure and Substrate Specicity of PTPN12.
Cell Rep.
15, 1345–1358 (2016).
52. Paling, N. R. D. & Welham, M. J. Role of the protein tyrosine phosphatase SHP-1 (Src homology
phosphatase-1) in the regulation of interleukin-3-induced survival, proliferation and signalling.
Biochem. J.
368, 885–894 (2002).
53. Van Vliet, C.
et al.
Selective regulation of tumor necrosis factor-induced Erk signaling by Src family
kinases and the T cell protein tyrosine phosphatase.
Nat. Immunol.
6, 253–260 (2005).

Page 20/21
54. Zhang, S. & Zhang, Z. Y. PTP1B as a drug target: recent developments in PTP1B inhibitor discovery.
Drug Discov. Today
12, 373–381 (2007).
55. Oleinikovas, V., Saladino, G., Cossins, B. P. & Gervasio, F. L. Understanding Cryptic Pocket Formation
in Protein Targets by Enhanced Sampling Simulations.
J. Am. Chem. Soc.
138, 14257–14263 (2016).
56. Rutledge, P. J. & Challis, G. L. Discovery of microbial natural products by activation of silent
biosynthetic gene clusters.
Nature Reviews Microbiology
13, 509–523 (2015).
57. Hartenfeller, M. & Schneider, G. De novo drug design.
Methods in molecular biology (Clifton, N.J.)
672, 299–323 (2011).
58. Packer, M. S. & Liu, D. R. Methods for the directed evolution of proteins.
Nat. Rev. Genet.
16, 379–394
(2015).
59. Johnston, C. W., Badran, A. H. & Collins, J. J. Continuous bioactivity-dependent evolution of an
antibiotic biosynthetic pathway.
Nat. Commun.
11, (2020).
60. Chen, M. J., Dixon, J. E. & Manning, G. Genomics and evolution of protein phosphatases.
Sci. Signal.
10, 1–17 (2017).
61. Carlson, J. C., Badran, A. H., Guggiana-Nilo, D. A. & Liu, D. R. Negative selection and stringency
modulation in phage-assisted continuous evolution.
Nat. Chem. Biol.
10, 216–222 (2014).
62. Chen, X.
et al.
Statistical experimental design guided optimization of a one-pot biphasic multienzyme
total synthesis of amorpha-4,11-diene.
PLoS One
8, e79650 (2013).
63. Edgar, S.
et al.
Mechanistic Insights into Taxadiene Epoxidation by Taxadiene-5α-Hydroxylase.
ACS
Chem. Biol.
11, 460–469 (2016).
64. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 - Approximately maximum-likelihood trees for large
alignments.
PLoS One
5, e9490 (2010).
65. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. ggtree: an r package for visualization and
annotation of phylogenetic trees with their covariates and other associated data.
Methods Ecol. Evol.
8, 28–36 (2017).
66. Burnham, K. P. & Anderson, D. R.
Model Selection and Multimodel Inference: a Practical Information-
theoretic Approach, 2nd edn. Springer-Verlag, New York
.
New York Springer
60, (2002).
67. Winter, G. Xia2: An expert system for macromolecular crystallography data reduction.
J. Appl.
Crystallogr.
43, 186–190 (2010).
68. Afonine, P. V
et al.
Towards automated crystallographic structure renement with phenix.rene.
Acta
Crystallogr. D. Biol. Crystallogr.
68, 352–67 (2012).
69. Emsley, P. & Cowtan, K. Coot: Model-building tools for molecular graphics.
Acta Crystallogr. Sect. D
Biol. Crystallogr.
60, 2126–2132 (2004).
70. Joosten, R. P., Long, F., Murshudov, G. N. & Perrakis, A. The
PDB_REDO
server for macromolecular
structure model optimization.
IUCrJ
1, 213–220 (2014).
71. Abraham, M. J.
et al.
Gromacs: High performance molecular simulations through multi-level
parallelism from laptops to supercomputers.
SoftwareX
1–2, 19–25 (2015).

Page 21/21
72. Huang, J.
et al.
CHARMM36m: An improved force eld for folded and intrinsically disordered
proteins.
Nat. Methods
14, 71–73 (2016).
73. MacKerell, A. D.
et al.
All-atom empirical potential for molecular modeling and dynamics studies of
proteins.
J. Phys. Chem. B
102, 3586–3616 (1998).
74. Vanommeslaeghe, K.
et al.
CHARMM general force eld: A force eld for drug-like molecules
compatible with the CHARMM all-atom additive biological force elds.
J. Comput. Chem.
31, 671–
690 (2010).
75. Yu, W., He, X., Vanommeslaeghe, K. & MacKerell, A. D. Extension of the CHARMM general force eld
to sulfonyl-containing compounds and its utility in biomolecular simulations.
J. Comput. Chem.
33,
2451–2468 (2012).
76. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for
molecular simulations.
J. Comput. Chem.
18, 1463–1472 (1997).
77. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N·log(N) method for Ewald sums in large
systems.
J. Chem. Phys.
98, 10089 (1993).
78. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling.
J. Chem. Phys.
126, 014101 (2007).
79. Parrinello, M. Polymorphic transitions in single crystals: A new molecular dynamics method.
J. Appl.
Phys.
52, 7182 (1981).