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\DOI{10.5802/crchim.409}
\datereceived{2024-11-20}
\daterevised{2025-03-25}
\dateaccepted{2025-05-23}
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\dateposted{2025-06-27}
\begin{document}

\begin{noXML}

\CDRsetmeta{articletype}{review}

\title{Harnessing native enzyme diversity for biocatalysis}

\alttitle{Exploiter la diversit\'{e} des enzymes natives pour la biocatalyse}

\author{\firstname{C\'{e}lestin} \lastname{Gamonet}\CDRorcid{0009-0006-2329-7291}}
\address{G\'{e}nomique M\'{e}tabolique, Genoscope, Institut Fran\c{c}ois Jacob, CEA, CNRS, Univ Evry, Universit\'{e} Paris-Saclay, 91057 Evry, France}
\email[C. Gamonet]{celestin.gamonet@genoscope.cns.fr}

\author{\firstname{Anne} \lastname{Zaparucha}\CDRorcid{0000-0003-2531-4148}}
\addressSameAs{1}{G\'{e}nomique M\'{e}tabolique, Genoscope, Institut Fran\c{c}ois Jacob, CEA, CNRS, Univ Evry, Universit\'{e} Paris-Saclay, 91057 Evry, France}
\email[A. Zaparucha]{anne.zaparucha@genoscope.cns.fr}

\author{\firstname{Carine} \lastname{Vergne-Vaxelaire}\CDRorcid{0000-0002-1202-8110}\IsCorresp}
\addressSameAs{1}{G\'{e}nomique M\'{e}tabolique, Genoscope, Institut Fran\c{c}ois Jacob, CEA, CNRS, Univ Evry, Universit\'{e} Paris-Saclay, 91057 Evry, France}
\email[C. Vergne-Vaxelaire]{carine.vergne@genoscope.cns.fr}

\keywords{\kwd{Sequence-driven enzyme discovery}
\kwd{Biodiversity} 
\kwd{Biocatalysts}
\kwd{Nitrilase}
\kwd{Dioxygenase}
\kwd{CoA ligases}
\kwd{Amine dehydrogenase}}

\altkeywords{\kwd{D\'{e}couverte d'enzymes guid\'{e}e par la s\'{e}quence} 
\kwd{Biodiversit\'{e}} 
\kwd{Biocatalyseurs}  
\kwd{Nitrilase}  
\kwd{Dioxyg\'{e}nase} 
\kwd{CoA ligases} 
\kwd{Amine d\'{e}shydrog\'{e}nase}}

\thanks{Agence Nationale de la Recherche (ANR) through   MODAMDH
(ANR-19-CE07-0007) and ALADIN (ANR-21-ESRE-0021) projects, Commissariat
\`{a} l'\'{e}nergie atomique et aux \'{e}nergies alternatives (CEA),
CNRS, University of Evry Paris-Saclay.}

\shortrunauthors

\begin{abstract}
The use of enzymes for synthetic purposes typically relies on
well-known or commercially available proteins, valued for their
established properties. However, these enzymes may not always be ideal
for specific reactions, prompting researchers to explore the vast
diversity of enzymes within biodiversity. Genome and metagenome mining
offers a rich reservoir of sequences, revealing novel enzymes with
enhanced properties such as thermostability and substrate specificity,
which are crucial for industrial applications. Advances in large-scale
sequencing have exponentially increased available protein sequences,
with over 2.4 billion reported in 2023 compared to 123 million in 2018.

Despite its potential, enzyme discovery from metagenomic data remains
challenging due to the immense volume of sequences. This necessitates
innovative computational tools and bioinformatics workflows to
streamline the identification of biocatalysts. Bioinformatics plays a
pivotal role in predicting enzyme functions, analyzing protein
superfamilies, and selecting key enzymes via biosynthetic gene
clusters. Integration of artificial intelligence (AI) further enhances
enzyme discovery and retrosynthetic pathway design, enabling the
customization of enzymes for specific applications.

Case studies from our laboratory illustrate the efficiency of genome
mining and bioinformatics in discovering enzymes complementary to known
ones, modifying metabolic reactions, and identifying novel scaffolds. 
These methods have expanded the diversity of enzymes available for
synthesis, underscoring the importance of synergizing bioinformatics
with biocatalysis to harness biodiversity and develop a versatile
enzymatic toolbox.
\end{abstract}

\begin{altabstract}
L'utilisation des enzymes en synth\`{e}se repose g\'{e}n\'{e}ralement
sur des prot\'{e}ines connues ou commerciales, employ\'{e}es pour leurs
propri\'{e}t\'{e}s bien \'{e}tablies. Cependant, ces enzymes ne sont
pas toujours adapt\'{e}es \`{a} des r\'{e}actions sp\'{e}cifiques,
incitant les chercheurs \`{a} rechercher des enzymes dans la
biodiversit\'{e}. Les (meta)g\'{e}nomes offrent un riche r\'{e}servoir
et leur exploration permet de r\'{e}v\'{e}ler des enzymes avec de
nouvelles propri\'{e}t\'{e}s, telles que la thermostabilit\'{e} et la
sp\'{e}cificit\'{e} des substrats, essentielles pour les applications
industrielles. Les progr\`{e}s en s\'{e}quen\c{c}age \`{a} grande
\'{e}chelle ont multipli\'{e} de mani\`{e}re exponentielle les
s\'{e}quences disponibles, avec plus de 2,4 milliards
r\'{e}pertori\'{e}es en 2023 contre 123 millions en 2018.

Malgr\'{e} son potentiel, la d\'{e}couverte d'enzymes \`{a} partir de
donn\'{e}es m\'{e}tag\'{e}nomiques reste complexe en raison de
l'immense volume de s\'{e}quences. Cela n\'{e}cessite des outils de
calcul innovants et des s\'{e}quences d'op\'{e}rations
bioinformatiques pour rationaliser l'identification des biocatalyseurs.
La bioinformatique joue un r\^{o}le cl\'{e} dans la pr\'{e}diction des
fonctions enzymatiques, l'analyse des superfamilles de prot\'{e}ines et
la s\'{e}lection d'enzymes via les clusters de g\`{e}nes
biosynth\'{e}tiques. L'int\'{e}gration de l'Intelligence Artificielle
(IA) am\'{e}liore encore la d\'{e}couverte d'enzymes et la conception
de voies r\'{e}trosynth\'{e}tiques, permettant d'adapter des enzymes
pour des applications sp\'{e}cifiques.

Des \'{e}tudes de cas de notre laboratoire illustrent l'efficacit\'{e}
de l'exploration des g\'{e}nomes et de la bioinformatique pour
d\'{e}couvrir de nouvelles enzymes, modifier des r\'{e}actions
m\'{e}taboliques et identifier de nouvelles structures. Ces
m\'{e}thodes ont \'{e}largi la diversit\'{e} des enzymes disponibles
pour la synth\`{e}se, soulignant l'importance de la synergie entre
bioinformatique et biocatalyse pour exploiter la biodiversit\'{e} et
d\'{e}velopper une bo\^{i}te \`{a} outils enzymatique polyvalente.
\end{altabstract}

\maketitle

\twocolumngrid

\end{noXML}


\section{Introduction}

The application of enzymes for synthetic purposes usually relies on a
restricted set of proteins, commonly known by the biocatalytic
community or even commercially available ones. The knowledge of their
features (substrate scope, activity, stability, structure\,{\ldots})
reassures researchers, who tend to use them by default or for
convenience. However, these \mbox{properties} may not always be the most
suitable for specific reactions (or cascade reactions). Every organism
has its own arsenal of enzymes which, from a synthetic application
point of view, constitutes a truly diverse reservoir of sequences and
structures. It is up to us to make the most of it, to offer an
enzymatic toolbox of great diversity. When required, each enzyme can be
adapted to a targeted reaction, which may be very different from that
for which it has naturally evolved, by engineering or directed
evolution. So genome mining for enzyme discovery is still of \mbox{interest}
and must not be opposed to protein engineering. As mentioned by NGuyen
et~al., ``a treasure trove of enzyme chemistry awaits to be uncovered''
\cite{1}. As a matter of fact, the number of available sequences
increases daily, thanks in particular to the development of large-scale
sequencing projects for marine and terrestrial biodiversity. The number
of available protein sequences has increased more than 20-fold in the
last 6 years, with more than 2.4 billion reported in 2023 compared to
approximately 123 million in 2018 \cite{2,3}. According to some
reports, metagenome mining has a high success rate (99\%) in
identifying diverse genes encoding for novel enzymes, which are crucial
for biocatalytic applications \cite{4}. Sequences coding for
characterized protein homologs and many of unknown functions are added
to (meta)genomic databases, already rich in proteins that can be used
for synthesis \cite{5}. The quest for other biocatalysts among
biodiversity is important to enlarge the spectrum of substrates and
reactions but is not restricted to this aspect \cite{6}. For industrial
applications, enzyme stability is an essential parameter. Recent
studies have reported the discovery of unique enzymes, especially in
the ``hidden sequence space'' by metagenomic mining, which exhibit
enhanced properties (e.g., thermostability, substrate specificity),
making them valuable for various industrial applications, including
pharmaceutical ones. The impact of metagenomics on biocatalysis is
undeniable \cite{7,8}.
\looseness=-1

However the prospection for enzymes with enhanced catalytic properties
and specific features is not straightforward. Discovering enzymes
within the extensive array of available metagenomic data is a
significant challenge, requiring the development of innovative
computational and functional screening tools. The integration of
bioinformatics into biocatalysis facilitates a more systematic approach
to the discovery and design of novel biocatalysts. Biocatalysis and
bioinformatics must work in synergy to optimize the identification of
enzymes of interest within biodiversity and minimize the number of
candidates to be screened. Bioinformatics tools enable the
identification of enzyme functions and the prediction of their
properties, mainly from sequence-based and function-based screening and
modeling \cite{9,10}. The analysis of protein superfamilies allows the
rational selection and design of enzymes with improved catalytic
\mbox{properties,} 
\mbox{focusing} on sequence--function relationships and
catalytic residues, 
\mbox{including} their conformational variation
\cite{11,12}. Identification and analysis of biosynthetic gene clusters
is also a powerful approach to select key enzymes \cite{13}. Furthermore,
the entire biocatalytic process, from enzyme discovery to the design of
the retrosynthetic pathway, is increasingly being enhanced and
streamlined through the integration of deep neural networks and
artificial intelligence (AI). All our knowledge of enzyme natural
diversity helps in parallel the development of hybrid generative AI
methods, so that we can progressively customize the dream enzyme for
each application \cite{14}.

Here, we present case studies from our laboratory illustrating the
potential outcomes of genome mining approaches and the input of
bioinformatic workflows in their efficiency to: (1) identify enzymes
complementary to those already characterized; (2) leverage known enzyme
mechanisms to modify natural reactions; (3) discover entirely new enzyme
scaffolds for a specific transformation. The results in terms of
diversity in sequences, structures and synthesis potential are
described. Without going into detail, we present in each case the
(meta)genome mining method used to select candidate enzymes within
biodiversity. 

{\vspace*{-2pt}}

\section{Genome mining to expand biocatalytic\newline properties within
established enzyme\newline families}

{\vspace*{-2pt}}

\subsection{Example with the nitrilase activity}\label{sec2.1}

{\vspace*{-2pt}}

Exploring the biodiversity for native enzymes is an efficient way to
broaden the scope of already known or even commercially available
biocatalysts \cite{15}. It worked especially well for nitrilases (EC
3.5.5), enzymes enabling the hydrolysis of nitriles into their
corresponding carboxylic acids. This biotransformation has been studied
for years due to the benefits it provides over the chemical
transformation \cite{16}. In 2012, when we started this project,
characterized nitrilases were still scarce, especially for regio- and
stereospecific applications. We applied a genome mining approach to
identify new nitrilases within biodiversity and proposed three
preparative scale reactions for applications of this expanded enzymatic
toolbox \cite{17}. 

\begin{figure*}
\includegraphics{fig01}
\caption{\label{fig1}Pipeline for the selection of candidate enzymes by
sequence-driven approach within the FunDivEx platform. Source: Figure
adapted from Zaparucha et~al.~\cite{15}.}
\end{figure*}

In our process, exploring enzyme diversity first requires the careful
selection of experimentally \mbox{validated} enzymes of diverse sequences to
recover the greatest possible diversity. We selected 34 reference
nitrilases, based on their activity toward various nitriles and,
noteworthily, without taking into account their annotation or their
organism of origin. The corresponding protein sequences were used to
fish out prokaryotic enzymes in the UniprotKB database using a basic
local alignment search tool (BLAST) sequence alignment \cite{18}. Only
sequences presenting over 30\% identity on more than 80\% of their
length were kept. We then classified the sequences on the basis of 80\%
sequence identity in order to have isofunctional clusters. By choosing
a few representatives per cluster, or just one, we ensured that we
covered the biodiversity being explored while minimizing the number of
proteins to be screened. The representatives were chosen based on
genome availability in the Genoscope strain collection (700 prokaryotic
species) and in an in-house wastewater treatment plant metagenomic
collection of sequences (``Cloaca maxima''), alongside nucleic sequence
features like guanine-cytosine content. DNA was purchased for cluster
representatives that were unavailable in the internal collection. This
workflow, integrated within the FunDivEx platform, is automated. The
pipeline resulted in the identification of 290 candidate nitrilases,
mostly annotated as hydrolases or carbon-nitrogen hydrolases. After
overexpression in \textit{Escherichia coli} (\textit{E.~coli})
BL21(DE3) cells, we obtained 163 candidate proteins, each tagged with a
hexahistidine tag to simplify subsequent purification
\mbox{(Figure~\ref{fig1}).} \looseness=-1

The 163 candidates were screened against 25 nitrile substrates grouped
into six categories by chemical properties (saturated, unsaturated,
\mbox{arylaceto-,} \mbox{$\upalpha$-hydroxylated,} $\upbeta$-hydroxylated,
and $\upbeta$-aminated) using a sensitive UV-spectrophotometric
assay. This assay, adapted here for the first time for nitrilase
activity screening, leverages glutamate dehydrogenase (GDH) to catalyze
a non-limiting reaction with release of ammonia
(NH$_{3}$), enabling kinetic measurement through NADH
consumption monitored at 340~nm, which correlates with initial NH$_{3}$
concentration and thus with nitrilase activity. We identified 125
nitrilases with varying activity levels across the substrates; while
some exhibited substrate specificity, most were promiscuous,
efficiently hydrolyzing nitriles with aromatic groups (77\%), saturated
nitriles (60\%), and unsaturated nitriles (50\%). Notably, NIT93 from
\textit{Lysinibacillus sphaericus} (UniProtKB: B1HZZ4) and NIT278
from \textit{Syntrophobacter fumaroxidans} (UniProtKB: A0LKP2) were
active across all six substrate groups, displaying marked substrate
promiscuity. The top 37 nitrilases were subsequently purified and
reassessed, yielding 27 enzymes with activity spanning nearly the
entire substrate range, except for 3-aminopentanenitrile. 

\begin{figure*}
\includegraphics{fig02}
\caption{\label{fig2}Distribution of the nitrilase collection based on
their sequence identity with the best sequence hits in the reference
set. For the analysis, we used (A) the percent sequence identity; or
(B) the percent positive substitutions, i.e., amino acids that are
either identical or have similar properties based on the BLOSUM62
scores. Blue bars correspond to the 163 candidate enzymes, yellow bars
to the 125 screening hits, and green bars to the 27 validated enzymes
with the best activities.}
\end{figure*}

As shown in Figure~\ref{fig2}, the selection of homologs with less than
40\% sequence identity resulted in nitrilases active on a structurally
diverse set of nitriles, capturing enzymatic diversity around the
reference sequences. It's interesting to note that most candidates have
more than 50\% amino acid similarity with the reference enzymes
(Figure~\ref{fig2}B), with over 75\% of the top 27 nitrilases
presenting 30--40\% sequence identity, underscoring the advantage of
genome mining across a broad sequence landscape to identify homologs
down to 30\% identity. Of the newly identified nitrilases, nearly 60\%
showed less than 40\% identity to any of the 34 previously
characterized prokaryotic nitrilases, thus expanding the available
catalog of nitrilases for synthetic biocatalysis.

The screening revealed several enzymes with significant activity,
forming a versatile toolkit of catalytic options. We used this toolbox
to identify enzymes active toward specific synthons. We focused on
enzymes exhibiting activity toward the generic substrate structurally
related to targeted synthons in a second screening round on these more
functionalized molecules frequently used in chemical synthesis. This
approach yielded promising results: NIT28 from \textit{Sphingomonas
wittichii} (UniParc  ID: UPI0000E98FFB), NIT278 from
\textit{Syntrophobacter fumaroxidans} (UniProtKB ID: A0LKP2), and
NIT158  from \textit{Sphingomonas wittichii} (UniParc ID: UPI0000E990F3)
successfully converted 3-cyano-2-phenylpropanoate (\textbf{1}),
3-oxocyclopentanecarbonitrile (\textbf{2}), and iminodiacetonitrile   
(\textbf{3}) into (\textit{R})-3-methoxycarbonyl-3-phenylbutanoic acid
(\textbf{4}) (\textit{ee} ${=}$ 46 ${\pm}$ 2\%),
(\textit{S})-3-oxocyclopentanecarboxylic acid (\textbf{5}) (\textit{ee}
${=}$ 48 ${\pm}$ 2\%), and the monoacid 2-[(cyanomethyl)amino]acetic acid
(\textbf{6}), respectively, with moderate to high yields, demonstrating
substantial biocatalytic potential (Figure~\ref{fig3}).
\looseness=-1

\begin{figure*}
\includegraphics{fig03}
\caption{\label{fig3}Genome-mined nitrilase toolbox for preparative
scale hydrolysis of functionalized nitriles. The imbedded table
illustrates results obtained with the set of purified enzymes where
navy blue squares indicate high activities (more than 1~mM converted in
4~h) and light blue squares moderate activity (less than 1~mM converted
in  4~h). Source: Figure adapted from Vergne-Vaxelaire
et~al.~\cite{17}.}
\vspace*{-1pt}
\end{figure*}

Through genome mining, we have been able to identify new nitrilases
with a substrate range 
{complementary} to the initial reference set.
Further analysis highlighted the distinct characteristics of these
enzymes, such as their pH and solvent {tolerance,} along with their
thermoactivity profiles. This work demonstrates that screening diverse
representative enzymes across structurally varied substrates builds an
enzyme toolbox that can be leveraged for further, more targeted
applications in synthesis. 


\subsection{Example with the $\upalpha$-ketoacid-dependent dioxygenase activity}

Another example employing this strategy is our exploration of
$\upalpha$-ketoacid-dependent oxygenases ($\upalpha$KAOs),
enzymes primarily known for catalyzing hydroxylation. We concentrated
on bacterial $\upalpha$KAOs involved in hydroxylating side chains
of free amino acids or peptide-bound amino acids in non-ribosomal
peptide synthesis pathways. Hydroxylated amino acids are valuable
chiral building blocks widely used in synthetic chemistry. Except for
the production of hydroxyprolines which found applications in
pharmaceutical and feed industries, the broader industrial use of
$\upalpha$KAOs in biocatalytic processes remains limited, likely due
to insufficient efficiency and a lack of comprehensive understanding of
this enzyme family's full potential. To address this gap, we
undertook a genome-mining approach to identify new native dioxygenases
with activity toward free amino acids and structurally related
compounds.\looseness=-1


A collection of candidate $\upalpha$KAOs was generated from an
initial set of eleven experimentally validated enzymes. Using the
established workflow described above, we obtained 274 candidate
sequences after clustering. To expand the collection, we also included
homologs containing InterPro motifs associated with the target activity
(proline hydroxylase---IPR007803 and clavaminate synthase-like 
(CSL)---IPR014503), thus adding 56 enzymes representative of clusters
with 80\% identity or more. This brought the total to 331 candidates,
of which 131 were successfully cloned with a His-tag and expressed in
\textit{E.~coli} as previously described \cite{19}.

For a project aiming to capture the full diversity of biocatalysts for
a specific transformation, it is essential to choose the substrates to
encompass a wide range of chemical reactivities and structural
constraints, minimizing the risk of overlooking potential activities. 

To streamline the process, the fourty-six chosen substrates were
organized into twelve pools. Each pool contained known substrates from
the reference set and derivative targets, including non-natural amino
acids, as well as amine and keto derivatives with aliphatic or aromatic
chains, and aliphatic sulfates.

\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Preparative-scale dioxygenase-decarboxylase enzyme
cascade yielding (2\textit{R},3\textit{R})-1,5-diaminopentane-2,3-diol
(\textbf{10}) from \textsc{l}-lysine \textbf{7}.}
\end{figure*}

Dioxygenase activity was monitored using the GDH assay mentioned above,
this time quantifying residual $\upalpha$-ketoglutarate, the $\upalpha$KAO
cosubstrate. Enzyme hits were purified and tested on individual
substrates, with product quantification by HPLC or GC-MS. This
screening led to the identification of previously uncharacterized
enzymes active on both known and novel substrates. Notably, three
enzymes exhibited activity on \textsc{l}-lysine (\textbf{7}), with either C3
regioselectivity (KDO1 from \textit{Catenulispora acidiphila},
UniProtKB ID: C7QJ42) or C4 regioselectivity (KDO2 from
\textit{Chitinophaga pinensis}, UniProtKB ID: C7PLM6; KDO3 from
\textit{Flavobacterium johnsoniae}, UniProtKB ID: A5FF23), and on
\textsc{l}-ornithine (ODO from 
\textit{Catenulispora acidiphila}, UniProtKB ID:
C7Q942). Interestingly, KDO2 and KDO3 were the first $\upalpha$KAOs
observed with C4 regioselectivity on a polar amino acid like lysine,
later supplemented by homologs KDO4 and KDO5 (UniProtKB ID: G8T8D0 and
UniProtKB ID: J3BZS6). Further study of ODO and KDO1,2,3 confirmed
their total regio- and stereoselectivities. ODO achieved up to 68 and
50\% \mbox{conversion} of \textsc{l}-ornithine and \textsc{l}-arginine into
(3\textit{S})-3-hydroxy-\textsc{l}-ornithine and
(3\textit{S})-3-hydroxy-\textsc{l}-arginine, respectively. 
KDO1 and KDO2,3 fully converted 
\textsc{l}-lysine \textbf{7} into 
\mbox{(3\textit{S})-3-hydroxy-\textsc{l}-lysine}
(\textbf{8}) and \mbox{(4\textit{R})-4-hydroxy-\textsc{l}-lysine,} respectively.
Moreover, sequential hydroxylation of 
\textsc{l}-lysine by KDO1 and KDO3 yielded
(3\textit{R},4\textit{R})-3,4-dihydroxy-\textsc{l}-lysine (\textbf{9}), which
was further transformed into\break the lactone
(3\textit{R},4\textit{R},5\textit{R})-3-amino-5-(2-aminoethyl)-4-hydroxyoxolan-2-one, 
an interesting chiral scaffold for synthesis.


By coupling KDOs with PLP-dependent decarboxylases, we designed
straightforward synthesis of chiral hydroxy amines from amino acids
through two- or three-step one-pot cascade reactions. The
decarboxylases were selected based on their genomic context and native
substrates, specifically targeting metabolic diamines such as
cadaverine. Notably, combining KDO1, KDO2, and DCC$_{\mathrm{pin}}$
(a decarboxylase from \textit{Chitinophaga pinensis}, UniProtKB
ID: C7PLM7, genomic context of KDO2) in a three-step, one-pot synthesis
led to the production of
(2\textit{R},3\textit{R})-1,5-diaminopentane-2,3-diol
(\textbf{10}) with high optical purity and 96\% yield, starting from
\textsc{l}-lysine \textbf{7}
\mbox{(Figure~\ref{fig4}).}\looseness=-1

To understand the enzyme selectivity, we performed a structural and
computational analysis of the enzymes. Sequence similarity network
(SSN) analysis, which clusters closely related proteins, revealed that
KDO2-5 homologs form a distinct group, clearly separate from other CSL
dioxygenases, including KDO1 \cite{20}. This classification aligns well
with experimental findings (Figure~\ref{fig5}A). Through genome mining,
we have begun to identify and define subfamilies within this enzyme
family, although many remain experimentally uncharacterized or
potentially misannotated. This underscores the importance of combining
in vitro screening with bioinformatic analysis to comprehensively map
and harness enzymatic diversity.

\begin{figure*}
{\vspace*{-1pt}}
\includegraphics{fig05}
{\vspace*{-1pt}}
\caption{\label{fig5}Sequence and active site-structural diversity of the
clavaminate synthase-like (CSL) family. (A)~Sequence similarity network
(SSN) of the CSL family (IPR014503) from the $\upalpha$KAOs
superfamily. Nodes represent proteins and edges are the links between
those proteins that have at least 50\% sequence identity. Experimental
data retrieved from Swissprot is mapped on the SSN with KDO1 colored in
emerald green, KDO2-5 in dark blue and ODO in orange. The network was
made with the EFI-EST tool and visualized with Gephi \cite{21}. (B)
Active site modeling and clustering (ASMC) hierarchical tree of the CSL
family, encompassing 523 proteins grouped into three main clades (I,
II, III). For each ASMC group (G1 to G12), a linear projection of the
three-dimensional superposition of modeled and crystallographically
resolved active sites is provided, via sequence logos illustrating
residue conservation within each active site pocket of the ASMC group.
A conserved arginine residue, specific to G12, which clusters the
KDO2-5 homologs, is highlighted with a green circle marker.
Source: Figure adapted from Bastard et~al.~\cite{20} under
\href{http://creativecommons.org/licenses/by/4.0/}{Creative
Commons Attribution 4.0 International License}.}
{\vspace*{-1pt}}
\end{figure*}

Combining sequence diversity to active site structural diversity is a
key strategy to build solid hypotheses regarding enzyme mechanism,
substrate selectivity, and regio- and stereoselectivity. In this
regard, we further explored the diversity of the family via structural
studies using the active site modeling and clustering (ASMC) method,
thanks to the resolution of the crystal structures of KDO1 and KDO5 in
complex with their ligands \cite{22}. It clearly enables us to
visualize the high amino acid conservation within some groups of this
hierarchical tree of the family, key residues hypothesized from the
solved structures. In this way, the C4 regioselectivity was explained
by the presence of an arginine in a specific spatial position
responsible for a salt bridge with the carboxylate group of the
\textsc{l}-lysine 
substrate, and not found in C3-regioselective enzymes like
KDO1 classified in another ASMC group (Figure~\ref{fig5}B). A subpocket
was also identified as responsible for the substrate specificity in the
CSL family, stabilizing either hydrophobic or positively or negatively
charged amino acid substrates depending on its surface charge and
hydrophobicity. Extensive characterizations like that are especially
useful to feed back into the genome mining process. Indeed, we can now
look further into dioxygenases using genome mining reinforced with
structure-based searches to look for new KDOs.


These two examples highlight the potential of genome-driven
biodiversity screening, ranging from numerous hits for enzyme families
with wide substrate promiscuity, such as nitrilases, to a more
restricted set of hits for enzyme families tested against specific
substrates and exhibiting high specificity. As databases of enzyme
sequences, including many uncharacterized entries, continue to
expand, genome mining emerges as an indispensable tool for
uncovering this latent potential. It delivers crucial findings that
enhance our understanding of sequence diversity and enrich the
repertoire of biocatalysts available for biotransformations in
synthetic chemistry.


\section{Genome mining to divert an enzyme activity toward an
un-natural synthetic reaction: a use case with coenzyme A (CoA)
ligases}

The transformations catalyzed by native enzymes can inspire new
synthetic strategies for desirable chemical steps. One approach
involves partially leveraging a native enzyme system to benefit from
only the mechanistic steps useful for the targeted transformation. A
notable example, reported in 2017, is the synthesis of amides using
carboxylic acid \mbox{reductases} (CARs), which naturally catalyze the
reduction of carboxylic acids to aldehydes. This reaction proceeds
through a sequence involving the formation of acyl adenylate and
enzyme-thioester intermediates, followed by an NADPH-dependent
reduction to aldehyde. By adding a free amine to the reaction mixture,
these intermediates can instead be intercepted by this nucleophile,
producing amides rather than aldehydes \cite{23}. Further enzyme
engineering allowed the optimization of this promising method for
enzymatic amide synthesis \cite{24}.

\begin{figure*}
\includegraphics{fig06}
\vspace*{2pt}
\caption{\label{fig6}Synthesis of \textit{N}-methylalkylamides by
biocatalysis with CoA ligases diverted from their ATP-dependent
acyl-CoA thioesterification natural activity. Source: Figure adapted
from Capra et~al.~\cite{31} under the
\href{https://creativecommons.org/licenses/by/4.0/}{CC-BY license}.}
\vspace*{4pt}
\end{figure*}

Following the same philosophy, we aimed to utilize other members of the
adenylate-forming enzyme superfamily to achieve efficient amide
synthesis. Amides are crucial precursors for fine 
\mbox{chemical} and
pharmaceutical production \cite{25}. By coupling bacterial coenzyme A
(CoA) ligases, for the activation of carboxylic acids, with amines, we
developed a chemoenzymatic route for diverse amides \cite{26}. CoA
ligases typically catalyze the ATP-dependent acyl/aroyl-CoA thioester
formation from carboxylic acids in two steps: adenylation with ATP
followed by thioesterification in the presence of CoA. To enhance
process efficiency, we made the adenylate intermediate the direct
target of amine addition, avoiding the costly use of CoA for
thioesterification. To drive the nucleophilic attack, we increased the
reaction temperature to 60~\textdegree C. We selected CoA ligases from
thermophilic organisms to ensure stability at this temperature
(Figure~\ref{fig6}). This approach offers significant advantages over
the conventional chemical synthesis of amides, which typically relies
on activating carboxylic acids with reagents of low atom economy, such
as carbodiimides, oxalyl or thionyl chlorides, or
1,1$^\prime$-carbonyldiimidazole \cite{27}.

As a proof of concept, we focused on synthesizing
\textit{N}-methylbutyramide (\textbf{12}) from butyric acid
(\textbf{11}) \cite{26}. Based on literature, we selected carboxylic
acid CoA ligases (ACLs) reported as active on small carboxylic acids
and expressed by thermophilic organisms: \textit{Ms}ACL (UniProtKB ID:
A4YDT1) and \textit{Msed}ACL2 (UniProtKB ID: A4YDR9) from
\textit{Metallosphaera sedula}, \textit{Gthe}ACL (UniProtKB ID: A4INB3)
from \textit{Geobacillus thermodenitrificans}, and \textit{Stok}ACL
(UniProtKB ID: Q973W5) from \textit{Sulfolobus tokodaii}. To address
ATP costs, an enzymatic ATP regeneration system was implemented, using
a thermally stable polyphosphate kinase 2 (class III) to convert the
released AMP directly to ATP, with polyphosphate (PolyP$_{n}$) as the
phosphate donor. After 24~h at 65~\textdegree C in MOPS buffer (pH
8.5), with 0.02 mg/mL \textit{DgeoPPK2-III} from \textit{Deinococcus
geothermalis} (UniProtKB ID: Q1W43), 0.002~mg/mL \textit{PhPPase} from
\textit{Pyrococcus horikoshii} (UniProtKB ID: O59570), 5~mM butyric
acid, 0.5~mM ATP, and 50 equiv. of methylamine, the conversion rate to
\textit{N}-methylbutyrylamide reached 55 to over 99\% across all four
CoA ligases.

\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}Preparative biocatalytic synthesis of (A)
\textit{N}-methylbutyramide (\textbf{12}) by the thermophilic CoA
ligase from \textit{Geobacillus thermodenitrificans} and (B)
seven-membered lactam \textbf{14} from 6-aminohexanoic acid
(\textbf{13}) by the \textit{Ms}ACL (CoA ligase from
\textit{Metallosphaera sedula}) enzyme variant.}
\end{figure*}


Interestingly, \textit{Ms}ACL also accepts a range of carboxylic acids,
primarily short, linear ones, but activity was also observed with
5-oxo-hexanoic acid. Activity levels with propionic to pentanoic acids
(761--1514~mU/mg of purified enzyme) exceeded those for
phenyl derivatives (e.g., 3-phenylpropanoic acid) or longer
chains (e.g., decanoic acid). Notably, \textit{Gthe}ACL demonstrated
the highest activity (3.2~U/mg for butyric and pentanoic acid),
although its substrate scope was narrower, with no activity detected for 
\mbox{decanoic} acid or 3-phenylpropanoic acid. A preparative 10~mL-scale
reaction using a reduced methylamine quantity (10 equiv.) and 10~mM
butyric acid confirmed the feasibility of this approach for synthetic
applications (Figure~\ref{fig7})~\cite{26}.


As with CARs, one of the advantages of this approach is the diversity
of amides that can be obtained, since attack by the nucleophilic amine
is not catalyzed by the enzyme. A priori, all amines or other
nucleophiles are considered, the limitation remaining their
nucleophilicity and solubility under CoA ligase reaction conditions. As
the critical step from a sustainability and energetic point of view is
the acid activation step and not the addition of the amine, this
strategy constitutes an alternative to conventional chemistry.
Recently, this advantage was harnessed in the hybrid synthesis of
5-aminomethyl-2-furancarboxylic acid (AMFC)-derived amides from
alcohols, combining supported gold nanoparticles with these CoA ligases
\cite{28}. It is worth noting that another promising biocatalytic
approach for amide synthesis involves catalysis by amide bond
synthetases, a rare subclass within the ANL superfamily, which also
includes carboxylic acid-CoA ligase \cite{29}. In this case, the enzyme
catalyzes the entire transformation \cite{30}. 


Building on these successful results, we further diverted the native
reaction by looking for CoA ligases active on $\upomega$-amino
acids to enable intramolecular lactam formation \cite{31}. Lactams are
valuable compounds, particularly as precursors to polymers like
$\upvarepsilon$-caprolactam, essential for nylon production. 
Such~enzymes would catalyze the 
formation of 
$\upomega$-amino acyl
adenylates which, for five- to seven-membered chains, could then
undergo spontaneous intramolecular aminolysis of the phosphoester bond.
This reaction would lead to the formation of five- to seven-membered
lactams, specifically $\upgamma$-butyrolactam,
$\updelta$-valerolactam, and $\upvarepsilon$-caprolactam
\textbf{14}, respectively. To do so, we improved the acyl-binding
pockets of the promiscuous propionate-CoA ligase \textit{Ms}ACL,
also reported to be active toward the $\upomega$-functionalized
substrate 4-hydroxybutyric acid, by structure-guided protein
engineering \cite{32}. The thermoactivity and stability of this enzyme
is all the more important for the desired intramolecular aminolysis
reaction step. The three-dimensional structure of \textit{Ms}ACL in
a thioesterification state with bound acetyl-AMP and CoA was determined
in collaboration with the University of Groningen, providing insight
into the key residues shaping the acyl-binding pocket. Structure
analysis, supplemented with docking and mutant design, revealed the
interest of mutating the position W259 in \textit{Ms}ACL located at
the ``pocket floor'' into a smaller residue to generate a deeper pocket
suitable for $\upomega$-amino acid stabilization. Docking
experiments of the intermediates 5-aminopentanoyl-AMP and
6-aminohexanoyl-AMP into the W259G mutated pocket resulted in low
energy binding poses with formation of hydrogen bonds and van der Waals
interactions with neighboring residues (V238 and F350), not obtained in
the wild-type enzyme. Combined with mutations at these latter positions
to enhance interactions with the $\upomega$-amino group of the
substrate, purified mutants generated by directed \mbox{mutagenesis} and
expression in \textit{E.~coli} were tested for the formation of six-
and seven-membered lactams. The variant W259G/V238T/F350V gave rise to
the highest analytical yield in $\updelta$-valerolactam (26\%),
while none of the tested double or triple mutants provided higher yield
in $\upvarepsilon$-caprolactam \textbf{14} than the W259G variant
(30\%). For the formation of $\upgamma$-butyrolactam from
4-aminobutyric acid, the best analytical yield was obtained with the
wild-type enzyme (63\% yield) as expected from the in silico analysis.
It is worth noting that the main beneficial mutation W259G also induced
significantly higher activity for the intermolecular amide formation
with various longer carboxylic acids up to eleven-carbon chain length.


This example highlights the benefit of drawing inspiration from nature
by repurposing its natural functions to support advancements in the
chemical industry, with the understanding that revisiting Nature often
provides further insights. For lactam-forming CoA ligases, ongoing
efforts are focused on identifying candidates within biodiversity using
the genomic approach outlined in Section~\ref{sec2.1}, complemented by
insights gained from X-ray structural data and protein engineering of
\textit{Ms}ACL.

\section{Genome mining for identification of a new family of
biocatalysts: a use case with amine dehydrogenases
(AmDHs)}


In some cases, for a given biocatalyzed transformation, no naturally
occurring enzymes capable of catalyzing the reaction are known; only
engineered enzymes derived from those catalyzing different reactions
can be reported in the literature. This was the case in 2015 for the
reductive amination of carbonyl compounds to synthesize optically
active amines, where NAD(P)-dependent enzymes from \textit{Streptomyces
virginiae} were documented, but their corresponding protein sequences
had not been identified. Given the potential of this biocatalytic route
as an alternative to conventional reductive amination for optically
active amine synthesis, we sought to identify this activity across
biological diversity. However, the selection of reference protein
sequences to find homologs proved more complex than for previously
studied activities (see above), as no sequences for this specific
activity were available. To address this, we mined metabolic databases
to identify reactions that convert ketones to amines using ammonia and
NAD(P)H, specifically excluding $\upalpha/\upbeta$-amino acid
dehydrogenases from our search criteria. Homologous sequences of
(2\textit{R},4\textit{S})-2,4-diaminopentanoate dehydrogenase
(2,4-DAPDH), found in ornithine-fermenting bacteria, were selected via
the \mbox{FunDivEx} platform using the previously described pipeline
\cite{33}. The selected hits, heterologously produced in 
\textit{E.~coli}, including AmDH4 and others, were active toward the native
substrate lacking an amino group at the $\upalpha$-position but retaining
the necessary carboxylic acid (i.e., 4-oxopentanoic acid). These hits
were used in a subsequent iteration, ultimately leading to the
discovery of the first gene sequences encoding native AmDHs that
catalyze amination of ketones without requiring any carboxylic acid
group (specifically, \textit{Cfus}AmDH and \textit{Msme}AmDH).
Interestingly, these sequences were evolutionarily distinct from both
engineered AmDHs (eng-AmDHs) and imine reductases (IREDs), including
reductive aminases (RedAms), other NAD(P)-dependent enzymes that
typically utilize primary amines rather than ammonia. The sequence
space for this novel activity was further explored through a sequence
similarity network constructed from 5313 protein homologs of AmDH4,
\textit{Cfus}AmDH, and \textit{Msme}AmDH, limited to sequences found in
the UniProtKB database and an in-house metagenomic dataset \cite{34}.

\begin{figure*}
\includegraphics{fig08}
\caption{\label{fig8}Different screenings carried out to identify
native amine dehydrogenases (nat-AmDHs). Substrates of some
characterized enzymes are drawn (list not exhaustive).} 
\end{figure*}

A search limited to the closest homologs of previously identified
native AmDHs (nat-AmDHs) in metagenomic databases specific to marine
environments (OM-RGC and MATOUv1) and human microbiomes (Integrated
Gene Catalog) confirmed the presence of this activity across multiple
biomes \cite{35}. Recently, we proposed an innovative approach
leveraging advances in bioinformatics to gain a more detailed
understanding of the entire family across biodiversity. This involved
an extensive in silico screening of billions of sequences, covering a
substantial portion of publicly available metagenomic data \cite{36}.
We designed an efficient bioinformatic workflow to capture remote
homologs---proteins with similar structures and functions that exhibit
low sequence similarity and are challenging to detect using traditional
sequence-to-sequence or sequence-to-profile methods. This approach
involved screening Hidden Markov Models (HMMs) profiles of an expanded
nat-AmDH family against HMM profiles of NAD(P)-dependent enzymes built
within a vast dataset of 2.6 billion sequences from ten genomic and
metagenomic sequence databases 
\mbox{(2020~update).} While~this strategy has
been applied within the metabolic biology community to annotate
proteins with unknown functions, it has yet to be widely adopted in the
biocatalysis research community. This approach \mbox{expanded} the nat-AmDH
family from 2011 to 17,959 sequences, with no additional phylogenetic
or structural groups identified, indicating that we captured the full
enzymatic diversity for this transformation within biodiversity.
Notably, we combined this extensive in silico enzyme capture with in
vitro experiments to validate the function of selected representatives
and to uncover nat-AmDHs with previously unreported substrate scopes.
The previously straightforward search within the UniProtKB database
revealed (\textit{S})-stereoselective nat-AmDHs active on carbonyl
compounds limited to short aliphatic aldehydes and methyl ketones (five
carbon atoms or less), favoring ammonia over primary amines. In
contrast, this large-scale metagenomic screening identified members
active on bulkier carbonyls and ethyl ketones, capable of utilizing
methylamine, cyclopropylamine, and displaying increased
(\textit{R})-selectivity, along with close homologs of previously
identified nat-AmDHs (Figure~\ref{fig8}).

\begin{figure*}
{\vspace*{1pt}}
\includegraphics{fig09}
{\vspace*{1pt}}
\caption{\label{fig9}Example of biocatalytic reactions performed with
native amine dehydrogenases (nat-AmDHs). FDH ${=}$ formate
dehydrogenase, GDH ${=}$ glucose dehydrogenase;  TN ${=}$ Turnover
Number; STY ${=}$ Space Time Yield.}
{\vspace*{1pt}}
\end{figure*}

With the crystallographic structures of AmDH4, \textit{Cfus}AmDH,
\textit{Msme}AmDH, and MATOUAmDH2, we conducted further analysis
using the Active Site Modeling and Clustering (ASMC) method~\cite{34,37}. 
This resulted in a hierarchical tree for the nat-AmDH family, organized 
into five groups (G1--G5) based on active site residue patterns
and frequency. Groups G3 and G4 include AmDHs like
\textit{Msme}AmDH and its homologs, which are active on simple
ketones and aldehydes, while the more distant G2 contains AmDH4-type
enzymes, active on $\upgamma$-keto acids and 2,4-diaminopentanoate.
It should be noted that the reductive amination activity of
G3--G4 members toward the substrates tested may not be the
metabolic function that has yet to be elucidated. Enzymes in G1 and G5
remain functionally undefined. This classification provides a valuable
tool for identifying unusual members by visualizing both conserved and
divergent residues at specific 3D positions within the active site.
Coupled with docking experiments, this method allows clearer
differentiation of active sites suited to more hindered or uniquely
functionalized substrates.

Notably, understanding the diversity of residues at specific spatial
positions is invaluable to design active site variants. Considering
naturally occurring diversity enables the prediction of impactful,
viable mutations within the protein context, enhancing the efficiency
of protein engineering efforts. This approach has proven beneficial in
our work on \textit{Cfus}AmDH and nine additional nat-AmDHs, where
biodiversity mining and protein engineering guided the development of
variants capable of catalyzing the amination of sterically hindered
\textit{n}-alkyl aldehydes and \textit{n}-alkyl ketones from C6 to C9
\cite{38}. 

Some members of this family were further examined for enzyme kinetics,
stability, and biocatalytic potential. Their $k_{\mathrm{cat}}$ values
ranged from 0.1 to 10~s$^{-1}$, with $K_{\mathrm{M}}$ values for carbonyl
compounds between 0.5 and 100~mM. Similar to eng-AmDHs, the $K_{\mathrm{M}}$ for
NH$_{3}$ was high (100--400~mM), while the preferred nicotinamide
cofactor had a $K_{\mathrm{M}}$ between 10 and $50~\upmu\mathrm{M}$
\cite{34,35}. Although each characterized AmDH showed a cofactor
preference, most favored NADP; however, certain substrate/enzyme
couples displayed preference for NAD. Structural analysis pinpointed
key residues responsible for this specificity \cite{36}. Interestingly,
some nat-AmDHs functioned efficiently as ketoreductases in the absence
of an ammonia source; they exhibited minimal activity in the reverse
reaction for alcohol or amine oxidation \cite{39}. Stability studies
included assessments of thermal tolerance and buffer compatibility. For
instance, AmDH4 from \textit{Petrotoga mobilis}, a thermophilic
bacterium, proved exceptionally stable with a half-life of 65~h at 
60~\textdegree C and retained 90\% of its initial activity after 14
days at 30~\textdegree C \cite{33}.

\begin{figure*}
{\vspace*{-2pt}}
\includegraphics{fig10}
{\vspace*{-2pt}}
\caption{\label{fig10}Iterative strategy to decipher a new group of
enzymes for biocatalysis. Example with the identification of the native
amine dehydrogenase (nat-AmDH) family.  Source: Figure adapted from
Zaparucha et~al.~\cite{15},  Mayol et~al.~\cite{34}, Caparco
et~al.~\cite{35}, and Elisee et~al.~\cite{36},  the last under 
\href{http://creativecommons.org/licenses/by/4.0/}{Creative Commons
Attribution 4.0 International License}.}
{\vspace*{-2pt}}
\end{figure*}

Regarding conversion yields and productivity when a cofactor recycling
system was implemented, moderate to high conversions were obtained with
high enantiomeric excess of the resulting amines (\textit{ee} from 98
to over 99.9\%) for most of them. In the case of 1-methoxypropan-2-one
(\textbf{15}), a semi-preparative synthesis at 7.5~mmol scale (150
mM) was performed with 0.5~mg/mL of \textit{Msme}AmDH to access, with
88.3\% yield and 98.6\% \textit{ee}, to the (\textit{S})-MOIPA
\textbf{16}, a key chiral element within the Outlook$^{\text{\textregistered}}$
herbicide (BASF) (Figure~\ref{fig9})~\cite{40}.

The kinetics as well as the stability and biocatalytic potential can
now be improved by protein engineering and/or directed evolution, these
enzymes still being native ones heterologously overexpressed in
\textit{E.~coli}. The success of some preliminary site-directed
mutations carried out on some of them to modify their substrate range
makes us confident that modification of these enzymes will lead to
soluble and effective enzymes.

This iterative strategy for identifying new enzyme groups suitable for
biocatalysis is highly effective and broadly applicable to other enzyme
families that remain underexplored in biocatalysis (Figure~\ref{fig10}).
Searching for distant homologs within the vast pool of publicly
available metagenomic databases is a key approach for efficiently and
comprehensively exploring what Nature offers for biocatalytic
applications, as demonstrated in this study with the exploration of
reductive amination activity.

\section{Conclusion and perspectives}


Through these examples, we highlight the benefits of genome mining for
expanding the enzyme toolbox for synthetic applications. Analyzing
sequence diversity provides a crucial foundation, supporting not only
targeted searches within existing and \mbox{upcoming} metagenomic datasets,
but also the engineering of customized enzymes tailored to specific
industrial needs. This approach is bolstered by a broad array of
emerging methods for generating mutant libraries, allowing researchers
to explore sequence diversity beyond what Nature alone offers
\cite{41}. Biocatalysis is increasingly driven by advanced
\mbox{bioinformatics} workflows, which are essential for addressing the
demands of sustainable chemistry with speed and precision. Machine
learning and artificial intelligence are becoming pivotal in analyzing
sequence diversity and predicting protein structures \cite{42,43,44}.
When integrated with innovations in automation and high-throughput
screening, these technologies 
significantly enhance our capacity to
discover, modify, and design enzymes with tailored functions.

Although labor-intensive, harnessing native enzyme diversity for
biocatalysis is essential and promises significant long-term benefits,
especially with the implementation of robust databases and standardized
data practices. Initiatives like RHEA, EnzChemRED, EnzymeML, SABIO-RK
must operate within a user-friendly findable, accessible,
interoperable, reusable (FAIR) environment to encourage widespread
adoption \cite{45,46,47,48,49}. By integrating these tools into research
workflows, the full potential of genome mining for synthetic
applications can be unlocked, driving significant advancements in
\mbox{biocatalysis.}

{\vspace*{-2pt}}

\section*{Declaration of interests}

{\vspace*{-2pt}}

The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.

{\vspace*{-2pt}}

\section*{Funding}

{\vspace*{-2pt}}

Part of the work described in this mini-review was supported by the
Agence Nationale de la Recherche (ANR) through the 
MODAMDH (ANR-19-CE07-0007) and ALADIN (ANR-21-ESRE-0021) projects, and
by Commissariat \`{a} l'\'{e}nergie atomique et aux \'{e}nergies
alternatives (CEA), the CNRS and the University of Evry Paris-Saclay.

\CDRGrant[ANR]{ANR-19-CE07-0007}
\CDRGrant[ANR]{ANR-21-ESRE-0021}

{\vspace*{-2pt}}

\section*{Acknowledgements}

{\vspace*{-2pt}}

The authors thank Jean-Louis Petit for input in this mini-review and
all the authors of the articles of the UMR8030 ``Genomic Metabolics''
research unit at Genoscope described in detail in this mini-review. 

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{\vspace*{-2pt}}

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