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\begin{document}

\title{Microproteins - discovery, structure and function}



\author[1]{Jessica Mohsen}%
\author[1]{Alina Martel}%
\author[1]{Sarah Slavoff}%
\affil[1]{Yale University}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Advances in proteogenomic technologies have revealed hundreds to
thousands of translated small open reading frames (sORFs) that encode
microproteins in genomes across evolutionary space. While many
microproteins have now been shown to play critical roles in biology and
human disease, a majority of recently identified microproteins have
little or no experimental evidence regarding their functionality.
Computational tools have some limitations for analysis of short, poorly
conserved microprotein sequences, so additional tools are needed to
determine the role of each member of this recently discovered
polypeptide class. A currently underexplored avenue in the study of
microproteins is structure prediction and determination, which delivers
a depth of functional information. In this review, we provide a brief
overview of microprotein discovery methods, then examine examples of
microprotein structures (and, conversely, intrinsic disorder) that have
been experimentally determined using crystallography, cryo-electron
microscopy, and NMR, which provide insight into their molecular
functions and mechanisms. Additionally, we discuss examples of predicted
microprotein structures that have provided insight or context regarding
their function. Analysis of microprotein structure at the angstrom
level, and confirmation of predicted structures, therefore, has
potential to identify translated microproteins that are of biological
importance and to provide molecular mechanism for their in vivo roles.%
\end{abstract}%



\sloppy


\textbf{Title:} Microproteins---Discovery, Structure, and Function

\textbf{Authors:} Jessica J. Mohsen\textsuperscript{1,2}, Alina A.
Martel\textsuperscript{2}, Sarah A. Slavoff\textsuperscript{1,2,3}

\begin{enumerate}
\tightlist
\item
  Department of Chemistry, Yale University, New Haven, CT, USA
\item
  Institute of Biomolecular Design and Discovery, Yale University, West
  Haven, CT, USA
\item
  Department of Molecular Biophysics and Biochemistry, Yale University,
  New Haven, CT, USA
\end{enumerate}

\textbf{Corresponding Author} : Associate Professor of Chemistry Sarah
A. Slavoff\textsuperscript{1,2,3}

sarah.slavoff@yale.edu

Yale University West Campus

Institute of Biomolecular Design and Discovery

Molecular Innovations Center 232

600 West Campus Drive

PO Box 27392

West Haven, CT 06516

Phone: 203-737-8670

\textbf{Abbreviations:} small open reading frame (sORF), sORF-encoded
polypeptides (SEPs), long noncoding RNA (lncRNA), untranslated region
(UTR), upstream ORF (uORF), overlapping uORF (o.uORF), downstream ORF
(dORF), alternative ORF (alt-ORF), ribosome profiling (Ribo-seq),
ribosome-protected fragment (RPF), translation initiation sequencing
(TI-seq), cryo-electron microscopy (cryo-EM), sarco/endoplasmic
reticulum (SR/ER) calcium ATPase (SERCA), ribosomal RNA (rRNA), short
linear interaction motifs (SLIMs), non-homologous end joining (NHEJ),
prefoldin-like (PFDL)

\textbf{Keywords:} sORF, Microprotein, Structure, Mass Spectrometry --
LC-MS/MS, Genome



\textbf{Abstract:}

Advances in proteogenomic technologies have revealed hundreds to
thousands of translated small open reading frames (sORFs) that encode
microproteins in genomes across evolutionary space. While many
microproteins have now been shown to play critical roles in biology and
human disease, a majority of recently identified microproteins have
little or no experimental evidence regarding their functionality.
Computational tools have some limitations for analysis of short, poorly
conserved microprotein sequences, so additional tools are needed to
determine the role of each member of this recently discovered
polypeptide class. A currently underexplored avenue in the study of
microproteins is structure prediction and determination, which delivers
a depth of functional information. In this review, we provide a brief
overview of microprotein discovery methods, then examine examples of
microprotein structures (and, conversely, intrinsic disorder) that have
been experimentally determined using crystallography, cryo-electron
microscopy, and NMR, which provide insight into their molecular
functions and mechanisms. Additionally, we discuss examples of predicted
microprotein structures that have provided insight or context regarding
their function. Analysis of microprotein structure at the angstrom
level, and confirmation of predicted structures, therefore, has
potential to identify translated microproteins that are of biological
importance and to provide molecular mechanism for their in vivo roles.



\textbf{1 \textbar{} Introduction:}

Small open reading frames (sORFs, also termed smORFs) below 100 codons
were excluded by the FANTOM genome annotation consortium to filter out
the high rate of false positive sORFs that were detected under this size
in eukaryotic long noncoding RNAs. A similar small size cutoff of 50
codons was applied for prokaryotic gene annotation. These cutoffs were
expedient, since the number of known genes pales in comparison to the
number of background ORF-like sequences within a genome, most of which
are not expressed, but resulted in systematic under-detection of
functional sORFs. Many expressed sORFs have now been discovered: recent
studies have converged on hundreds of previously unannotated sORFs in
bacteria and thousands in human, and multiple CRISPR screens have
suggested that hundreds of human sORFs are required for cell survival
and proliferation. The emerging relevance of sORFs to infectious
disease, the microbiome, and human disease opens the possibility of new
therapeutic strategies, and, as such, consortium efforts to enter
translated sORFs into the genome annotation are underway.

Early discoveries of functional sORF-encoded polypeptides, such as
humanin in human, tarsal-less/polished rice in Drosophila and SgrT in
bacteria, occurred individually. As a result, the global nature of sORF
translation was not recognized until the seminal demonstration of
ubiquitous translating ribosome occupancy outside canonical reading
frames by Ingolia \emph{et al.} and subsequent confirmation of the
presence of a large number of unannotated sORF translation products with
mass spectrometry. The products of sORF translation have been termed
small proteins, microproteins, micropeptides, sORF-encoded polypeptides
(SEPs) and, evocatively, ghost proteins; we will utilize the term
microprotein throughout this review. In addition, longer, non-annotated
proteins, in some cases referred to as alternative proteins,
particularly when they overlap canonical proteins, have also been
identified, but they will not be specifically discussed herein. For the
purpose of this review, our definition of a eukaryotic microprotein will
extend to previously unannotated proteins below 130 amino acids, as many
previously undetected ORFs of this length have been reported in human
cells. Prokaryotic microproteins are typically categorized as less than
or equal to 50 amino acids in length; however, our definition in this
work will extend to 70 amino acids since many unannotated microproteins
of this size have been detected in multiple bacterial species.

Multiple classes and regions of RNA, both coding and noncoding, have
been shown to harbor sORFs in prokaryotes and eukaryotes (Figure 1).
Functional sORFs have been discovered in small and long noncoding RNAs
(ncRNAs and lncRNAs), antisense lncRNAs, microRNA precursors, and
circular RNAs{[}7,43{]} in bacteria, plants and other eukaryotes.
Interestingly, an increasing number of genes have been shown to exert
functions both at the level of the RNA and of the encoded microprotein,
such as \emph{sgrST} , \emph{azuCR} , Spot42/SpfP, and some micro RNAs
(miRNAs). Additional classes of sORFs have been identified in
multicistronic mRNAs alongside canonical protein coding sequences (CDS)
in both prokaryotes and, surprisingly, eukaryotes. sORFs in 5'
untranslated regions (UTRs) relative to an annotated CDS are referred to
as upstream ORFs (uORFs). Importantly, while eukaryotic uORFs have long
been regarded as cis-translational regulators that generally decrease
translation efficiency of the downstream CDS, in some instances, uORFs
encode microproteins with independent cellular functions in trans, such
as MIEF1-MP, which regulates mitochondrial protein translation, and
ASDURF, which is a previously unidentified component of the
prefoldin-like module of the PAQosome. Some sORFs that initiate in the
5' leader extend into and overlap the CDS in an alternative reading
frame, and can be termed overlapping uORFs (o.uORFs), such as human
alt-RPL36, which overlaps ribosomal protein L36 and regulates the
phospholipid transporter TMEM24. It is important to note that, because
they are translated in a different reading frame, o.uORF polypeptide
amino acid sequence is completely different from that of the downstream,
overlapping annotated protein. At the other end of the mRNA, the 3' UTR
has also been found to encode microproteins from downstream ORFs (dORF),
which may also regulate CDS translation. An emerging class of
frameshifted sORFs occur internally within a protein CDS. These nested
sORFs lie completely within the main ORF with translation initiating
downstream of the main ORF start codon, and translation terminating
upstream of the main ORF stop codon. Nested sORFs can occur in the +2 or
+3 (same-strand, frameshifted) reading frames (Figure 2), such
as\emph{E. coli} GndA and human alt-FUS. Surprisingly, these findings
point to the fact that mammalian mRNAs may be multicistronic or dual
coding. While prokaryotic organisms are known to express polycistronic
mRNA transcripts termed operons, and compact viral genomes have long
been known to contain overlapping open reading frames, eukaryotic
transcripts have long been thought to be monocistronic as a result of
the scanning model of translation initiation. Importantly, microproteins
and longer alternative proteins encoded in each of these classes of
sORFs have been shown to be functional. In summary, coding and noncoding
regions of both prokaryotic and eukaryotic genomes encode functional
sORFs in loci that are denser and more complex than previously presumed.

\textbf{2 \textbar{} Microprotein Discovery}

\emph{2.1 \textbar{} Computation}

Accurate annotation of sORFs using computational tools is challenging
not only due to their short lengths that impede statistical analyses,
but also because they exhibit intermediate conservation relative to
longer genes, which has been interpreted as evidence for the \emph{de
novo} evolution of some microproteins. Notwithstanding these challenges,
algorithms and machine learning strategies are currently being developed
to better find sORFs within genomes. Some computational efforts rely on
phylogeny, nucleotide and amino acid homology, and secondary structure
to identify unannotated sORFs with sequence or structural similarities
to canonical proteins; examples include PhyloCSF and miPFinder.
Additional dimensions of predictive information, including the presence
of a ribosome binding site upstream of bacterial sORF start codons or a
Kozak consensus sequence surrounding a eukaryotic sORF start codon, have
been applied to sORF prediction. Ambitiously, OpenProt predicts all
AUG-initiated sORFs and alternative ORFs (alt-ORFs) within all known
mRNAs for several organisms, and curates experimental evidence (or lack
thereof) for their expression. Finally, deep forest and deep learning
models have been applied to sORF prediction, with application to
individual microbial genomes, as well as the microbiome and metagenomes.
These methods have highlighted new sORFs in intergenic regions,
noncoding RNAs and in multicistronic/dual coding mRNAs.

\emph{2.2 \textbar{} Ribosome Profiling}

Deep sequencing of the protected mRNA footprints of actively translating
ribosomes (ribosome profiling or Ribo-seq) has been extensively reviewed
and provides a powerful technology for detection of translated sORFs.
Ribo-seq is carried out by isolating translating ribosomes associated
with mRNA transcripts, using either elongation inhibitors like
cycloheximide or rapid freezing. Because translating 80S ribosomes
protect bound mRNA fragments from digestion by RNase, sequencing the
ribosome-protected fragments (RPFs) reports on translated regions of
mRNA. Furthermore, the codon-by-codon elongation of 80S ribosome gives
RPFs a characteristic 3-nucleotide periodic distribution, which can be
used to infer the reading frame and confidently differentiate translated
ORFs from noise. Furthermore, translation efficiency can be assessed by
comparing the frequency of ribosome footprint reads to mRNA transcript
levels. Rigorous data analysis, high-resolution datasets, and analysis
of replicates are essential for calling sORF translation using Ribo-seq,
because their short lengths and translation by monosomes lead to lower
signal-to-noise in sORF-mapped reads relative to longer canonical
protein coding sequences.

While Ribo-seq is powerful in profiling the footprints of elongating
ribosomes and identifying novel coding regions, elongation inhibitors
like cycloheximide are not well-suited to deconvolute some translation
initiation sites, especially for ORFs with multiple start sites or
overlapping reading frames. As a result, a specialized method called
translation initiation sequencing (TI-seq) has been developed for
inhibition and profiling of the footprints of initiating ribosomes that
leverage molecules like puromycin, harringtonine and lactimidomycin in
eukaryotes, and retapamulin, tetracycline and Onc112 in prokaryotes. The
enrichment of ribosome footprints at canonical and non-canonical start
codons in TI-seq datasets generates peaks at the beginning of putative
sORFs as well as canonical protein coding sequences. This allows
deconvolution of sORF translation initiation from larger main ORFs in
multicistronic mRNAs, and is especially important for detection of
nested and out of frame sORFs. TI-Seq can also be combined with Ribo-Seq
to call translated ORFs with higher confidence.

\emph{2.3 \textbar{} Mass Spectrometry}

Mass spectrometry proteomics is able to detect translational products of
sORFs directly in biological samples using either bottom-up (from
peptide fragments) or top-down (intact precursor) modalities. However,
specialized sample preparation and computational methods must be applied
for high-sensitivity detection of small, unannotated microproteins. For
example, a standard bottom-up proteomics experiment begins with
isolation of the proteome, during which small molecules and proteolytic
fragments are typically removed by SDS-PAGE or filter-aided sample
preparation. Furthermore, most peptide and protein identification from
proteomics data is accomplished via spectral matching against the
annotated proteome database. For these reasons, sORF-encoded
polypeptides are both de-enriched from proteomic samples, and absent
from databases, and therefore cannot be detected with standard proteomic
workflows and searches.

Multiple recent reviews and protocols describing microprotein
identification via proteomics are available, so we provide a brief
overview highlighting only the key concerns here. Microprotein discovery
methods are built on the same technologies used for standard shotgun
proteomics, with several modifications (Figure 3). First, because
sORF-encoded microproteins are small, most are identified by only a
single proteotypic or fingerprint tryptic fragment in a typical
proteomics experiment. A major factor complicating detection of
microproteins is coelution and/or cofragmentation of the one or two
detectable tryptic peptides derived from a given microprotein with
abundant tryptic and/or proteolytic fragments of larger proteins.
Resulting ion suppression and/or complex spectra preclude detection
and/or identification of the microprotein fragment, regardless of its
abundance; this consideration is less severe for larger, canonical
proteins, which generate many tryptic peptides and thus detection of any
individual fragment is not required. Therefore, the first critical step
of any sORF proteomic experiment is to achieve proteome extraction in
the absence of proteolysis of canonical proteins (e.g., via boiling in
acidic solution or application of protease inhibitors) to minimize
sample complexity, followed by or concomitant with enrichment of the
small proteome and exclusion of large proteins. Small protein enrichment
can be achieved via multiple chemical and biophysical methods, such as
solid phase extraction, peptide gels, GELFrEE resolution, and organic
solvent or surfactant extraction. When they have been compared
head-to-head, these methods have typically been shown to offer
comparable numbers, but non-overlapping sets, of microproteins detected.
Depending on the experimental goals, the size selection approach for
microprotein proteomics can therefore be optimally chosen: for the
deepest coverage, multiple methods should be employed on replicate
samples and the results combined; for a rapid, robust and economical
approach, organic solvent extraction may prove attractive.

Subsequent to small proteome isolation, most microprotein studies to
date have employed bottom-up proteomic analysis, in which microproteins
are enzymatically digested into peptide fragments (typically with
trypsin, though multienzyme digests have been shown to improve small
proteome coverage), followed by liquid chromatography-tandem mass
spectrometry, often with multi-dimensional separation. This experiment
provides thousands of raw peptide fragmentation spectra corresponding
both to known canonical small proteins and microproteins, which must
then be identified and distinguished. This is typically accomplished via
peptide-spectral matching against expanded databases comprising the
canonical proteome as well as candidate sORF sequences. For eukaryotes,
databases can be derived from three-frame transcriptome translations,
ribosome profiling-derived translatomes, or publicly available
noncanonical ORF databases such as OpenProt and sORFs.org; six-frame
genomic translation can be employed for prokaryotes. Peptide-spectral
matching against any of these databases affords identifications of both
canonical small proteins and unannotated microproteins. It is important
to note that discrimination of false-positive identifications that arise
from searching expanded databases is critical. One important
consideration is use of a contaminants database to prevent aberrant
matching of artefactual peptides (e.g., fragments of trypsin or keratin
in dust) to sORF sequences. Another method commonly applied for this
purpose is application of a stringent false-discovery rate of less than
or equal to 1\%, estimated by querying hits to a decoy database
constructed from reversed amino acid sequences of the search database
entries. However, the expansion of the decoy database also decreases
sensitivity for true positive matches, as documented in work from
Fournier and colleagues . An alternative approach is to employ
permissive false discovery rates, followed by either manual inspection
of fragmentation spectra or a secondary algorithm like PepQuery to
exclude false positive spectra better explained by peptides arising from
canonical, mutant or post-translationally modified proteins. After
exclusion of peptides matching (or near-matching) annotated proteins,
the resulting list of identifications represent candidate unannotated
microproteins, which can be computationally mapped to the sORFs that
encode them and experimentally validated.

Mass spectrometry typically detects one to two orders of magnitude fewer
microproteins in a given experiment than ribosome profiling. This may be
due to the abovementioned challenge in detecting single
microprotein-derived fingerprint peptides; the relative insensitivity of
mass spectrometry to some classes of microproteins, including
membrane-localized, positively charged, and low-abundance species; the
instability of some sORF translation products; reduced sensitivity for
true-positive detections as a result of expanded decoy databases applied
for stringent false discovery rate estimation; or all of these factors.
Nonetheless, mass spectrometry offers several advantages. First,
enrichment strategies, such as membrane fractionation and chemical
labeling, can enable identification of microproteins that are refractory
to shotgun analysis of whole-cell tryptic digests, thus beginning to
address one of the major limitations of microprotein proteomics while at
the same time affording functional information about microproteins
(e.g., chemical reactivity, subcellular localization) that is
inaccessible to sequencing methods. Second, without specialized analysis
pipelines, ribosome profiling with elongation inhibitors is refractory
to confident detection of sORFs that overlap canonical protein coding
sequences in alternative reading frames, due to the requirement for
three-nucleotide periodicity for ORF calling. In contrast, mass
spectrometry can readily detect and identify microproteins derived from
overlapping ORFs, which can represent as much as 30\% of microproteins
identified in a proteomic experiment. Given the complementary nature of
genomics, ribosome profiling and mass spectrometry, it is likely that
the combination of these methods offers the greatest power for
large-scale, high-confidence microprotein identification.

\textbf{3 \textbar{} Microprotein Structure and Function}

Dozens of human microproteins, and many more in model organisms, have
now been assigned function at the molecular, cellular, and/or organismal
level. CRISPR screens have implicated hundreds of sORFs in cell survival
and proliferation. Experimental approaches are yielding insights into
the roles of microproteins in biological processes and disease, which
have been extensively reviewed. Recently emerging trends in microprotein
function include roles in immunity and inflammation, mitochondrial
functions and energetics, adiposity, microbial carbon metabolism, and
cancer initiation and progression, among others. Nonetheless, the vast
majority of recently discovered microproteins remain entirely
uncharacterized in mechanistic detail. This is in large part because
bioinformatic predictions of sORF function are challenging---even when
they exhibit signatures of conservation in multiple species,
microproteins tend to lack primary sequence homology to proteins of
known function. While three-dimensional structure prediction and
elucidation are likely to provide important insights into microprotein
functions, structures of microproteins have not yet been examined on
scale. However, the number of experimentally determined structures of
microproteins, in isolation or in complex with their effectors, is
growing, and general trends have begun to emerge, which we will describe
in this section. First, we discuss a subclass of single-pass
alpha-helical transmembrane microproteins, many of which are
evolutionarily novel, and some of which bind to and regulate important
transporters. Next, we consider examples of microproteins with solved or
predicted structures and the potential relevance to their functions.
Last, we will examine several intrinsically disordered microproteins
that undergo regulatory protein-protein interactions.

\emph{3.1 \textbar{} Alpha-helical transmembrane microproteins}

Intergenic regions of eukaryotic genomes are rich in A/T residues
relative to genes, which are G/C rich. When microproteins are expressed
from ``noncoding'' regions, they therefore tend to contain predicted
transmembrane helices arising from the preponderance of T/U residues
within codons that correspond to hydrophobic and aromatic amino acids.
This intergenic sequence bias therefore affects the amino acid
composition of evolutionarily young, species-specific microproteins,
that arise \emph{de novo} from previously noncoding regions of the
genome. A recent study demonstrated that C-terminal hydrophobic patches
tend to target evolutionarily young microproteins to the BAG6 membrane
protein triage complex, resulting either in membrane insertion or, if
mislocalized or improperly folded, proteasomal degradation.
Interestingly, species-specific transmembrane microproteins that exhibit
low expression can nonetheless contribute fitness advantage to cells,
and examples have been shown to function in processes such as yeast
mating. Not all membrane-associated microproteins are evolutionarily
novel; a large and growing number of well-characterized, conserved
transmembrane microproteins are predicted to contain transmembrane
helices, such as the lysosomal membrane-localized polypeptide regulator
of mTORC1, SPAR, and the plasma membrane localized micropeptide
Myomixer, which is required for myoblast fusion during skeletal muscle
development. The class of alpha-helical transmembrane microproteins is
therefore large, and of outsize biological importance. We turn our
attention in this section to those membrane-associated microproteins
that have been subjected to experimental structure determination.

AcrZ, previously named YbhT, was reported in a seminal study identifying
unannotated small protein genes in \emph{E. coli} utilizing
computational tools that incorporate ribosome binding site prediction.
AcrZ is a 49-amino acid microprotein that is conserved in many
Gram-negative bacteria and localizes to the \emph{E. coli} inner and
outer membranes by virtue of an N-terminal transmembrane helix. AcrZ
binds to the AcrB subunit of the AcrAB/\emph{tolC} multidrug efflux
pump, increasing the efficiency of transport of (and, thus, resistance
of \emph{E. coli} to) a subset of its substrates. Multiple structures of
AcrZ in complex with the AcrB homotrimer have been solved, including
crystal structures of detergent-solubilized complexes, as well as a
cryo-electron microscopy (cryo-EM) structure of the complex
reconstituted in lipid discs (Figure 4A). AcrZ binds to a transmembrane
groove within each molecule of AcrB. The cryo-EM structure revealed that
AcrZ exhibits a profound bend between positions 10-15, conferred by a
helix-breaking proline residue. Mutagenesis studies revealed that the
proline is required for interaction of AcrZ with AcrB. At the same time,
proline, or an equally helix-breaking glycine residue, can be moved to
any position within the AcrZ interaction motif while retaining its
association with AcrB. Several of these mutations that retain AcrB
binding also recapitulate the selective drug transport-promoting
phenotype of wild-type AcrZ. While the precise effects of AcrZ binding
on cargo occupancy and transport are not fully clear, allosteric
modulation of binding sites in AcrB is evident by comparing the AcrB vs.
AcrBZ structures. Furthermore, AcrZ promotes cardiolipin association
with AcrB, likely contributing to allosteric modulation of cargo binding
pockets in the transporter. Taken together, these results indicate that
the bend in the transmembrane helical shape of AcrZ, and not its amino
acid sequence, is essential for interaction and modulation of AcrB.

\emph{E. coli} CydX was originally identified as YbgT, a predicted
37-amino acid microprotein encoded downstream of the cytochrome bd
oxidase operon genes \emph{cydA} and \emph{cydB} . Cytochrome bd
oxidases operate as terminal electron acceptors in the electron
transport chain under hypoxic conditions due to their high oxygen
affinity. The two canonical subunits, CydA and CydB, form a
pseudosymmetric heterodimer, of which the CydA subunit contains all
three heme residues responsible for reduction of molecular oxygen to
water, as well as the Q loop that is responsible for binding an electron
donor quinol. CydX is a single-pass alpha helical transmembrane protein
that copurifies with the CydAB complex and is required for the assembly,
stability, and/or activity of cytochrome bd oxidase in multiple species.
Several atomic structures of cytochrome bd oxidases have revealed the
role of CydX homologs in the complex (Figure 4B). First, the presence of
an unannotated, CydX homolog, CydS, was serendipitously discovered in a
crystal structure of cytochrome bd oxidase purified from the
gram-positive bacterium \emph{Geobacillus thermodenitrificans} . CydS
forms an alpha helix that binds between helices 5 and 6 of CydA, leading
the authors to speculate that it may stabilize the heme cofactor when
the Q loop undergoes dynamic movement during catalysis. A subsequent
cryo-EM structure of the \emph{E. coli} cytochrome BD oxidase revealed
CydX bound to CydA between helices 1 and 6, again suggesting a
structural role. Interestingly, the \emph{E. coli} CydAB unexpectedly
revealed the presence of another single-pass transmembrane microprotein,
CydH, which is encoded in the \emph{ynhF} gene that is not located
within the cytochrome bd oxidase operon. CydH binds between
transmembrane helices 1 and 8 of CydA, on the opposite face of CydA
relative to CydX. CydH is proposed to occlude the proposed
oxygen-conducting channel from the \emph{Geobacillus} complex structure,
which has been replaced with a hydrophobic channel that traverses CydB
directly to the heme d site. The CydH oxygen channel rearrangement was
proposed to be required due to the swapped positions of two heme
cofactors in the \emph{E. coli} enzyme relative to the\emph{Geobacillus}
structure, and, accordingly, CydH homologs are found in Proteobacteria.
Overall, cytochrome bd oxidase is a unique system in which microproteins
are required for activity, structure and stability of a critical complex
of proteins.

In another well-characterized example, a class of microproteins (also
called micropeptides) termed ``regulins'' regulate the activity of the
sarco/endoplasmic reticulum (SR/ER) calcium ATPase (SERCA). During
muscle contraction, including the contraction of the heart and
calcium-dependent signaling processes, calcium is released from the
SR/ER into the cytosol; then, to terminate signaling or contraction,
calcium is pumped back into the SR/ER against its concentration gradient
using the energy of ATP hydrolysis by SERCA. Regulins colocalize with
SERCA in the SR/ER membrane, and each micropeptide is expressed in the
same, specific tissue as the SERCA isoform that it regulates. The first
known regulins, phospholamban and sarcolipin, were identified as
inhibitors of SERCA in cardiac and skeletal muscle, respectively.
Structural analysis of these canonical regulins, both of which are
\textless{}100 amino acids, reveals that they are small, single-pass
membrane proteins bearing a single transmembrane alpha-helix. The
crystal structure of the SERCA-sarcolipin complex reveals that the
micropeptide binds in a transmembrane groove in the SERCA channel
between helices 2, 6 and 9, where it allosterically alters the
conformation of SERCA to decrease its apparent calcium affinity.
Phospholamban binds to the same regulatory groove (Figure 4C). One
seminal discovery of novel SERCA regulating micropeptides came from a
study in \emph{Drosophila.} In this work, Couso and colleagues analyzed
putative lncRNAs associated with polysomes, suggesting that they are
translated. Of these lncRNAs, one contained an sORF encoding a peptide
predicted to be homologous to phospholamban and sarcolipin, which was
accordingly given the name sarcolamban. Sarcolamban may have arisen via
duplication of an ancestral phospholamban/sarcolipin gene in insects,
which subsequently diverged to the sarcolamban sequence. Sarcolamban was
demonstrated to bind SERCA in flies and its deletion caused heart
arrythmias, consistent with a role in regulating SERCA. Docking the
predicted structure of sarcolamban onto SERCA was consistent with a
similar binding mode as that observed for phospholamban and sarcolipin.
Just as importantly, additional novel regulins have also been discovered
in mammals. In analyses of mammalian lncRNAs to identify potential
micropeptides expressed in skeletal muscle and other tissues lacking
known regulin expression, translated sORFs were identified that encode
the novel SERCA binding micropeptides myoregulin, endoregulin, and
another-regulin, all of which bind to the same transmembrane groove of
SERCA, exhibit similar inhibition of SERCA to phospholamban, and are
predicted to have similar single-pass transmembrane alpha-helical
structures. Interestingly, an unannotated,
SERCA-\emph{activating}micropeptide, DWORF, was identified in yet
another long noncoding RNA in mouse. DWORF is expressed in skeletal
muscle, and ectopic over-expression of DWORF in heart tissue enhances
contractility and reverses heart failure in a model of heart failure.
However, the mechanism by which DWORF activates SERCA was unclear, since
it is predicted to bear a similar alpha-helical transmembrane domain and
binds to the same SERCA groove as previously characterized regulins,
which are all inhibitory. Some evidence from fluorescence resonance
energy transfer suggests that DWORF binding can directly activate SERCA.
A recent NMR structural study demonstrated that the alpha helix of DWORF
is kinked at a unique proline residue, creating a significant bend in
the transmembrane region without disrupting its binding to SERCA (Figure
4C). Mutating this proline residue diminished the bend angle between the
two alpha helical regions of DWORF, and not only prevented its
activation of SERCA, but converted it into a SERCA inhibitor. Therefore,
activation of SERCA by DWORF appears to require its proline-induced
kink, and, by extension, inhibition of SERCA by phospholamban,
sarcolipin, myoregulin, endoregulin and another-regulin may be
hypothesized to require binding of their uninterrupted transmembrane
helices to the regulatory groove of SERCA. It is also fascinating to
note the parallels between DWORF and AcrZ (see above), both of which
utilize kinked transmembrane alpha-helices to allosterically regulate
the membrane transporters SERCA and AcrB, respectively.

\emph{3.2 \textbar{} Humanin and its disorder-to-order transition}

Humanin is a secreted 24-amino acid polypeptide found in human serum
that protects neurons from cell death in the presence of familial early
onset-Alzheimer's disease-associated mutants of amyloid precursor
protein. Interestingly, the humanin coding sequence was mapped to a
polyadenylated cDNA that was expressed in the surviving brain tissue of
an Alzheimer's disease patient, and is derived from the mitochondrial
16S ribosomal RNA (rRNA). Given that another mitochondrial peptide,
MOTS-C, is encoded in a region overlapping the mitochondrial 12S rRNA,
this raises the intriguing possibility that the mitochondrial rRNA genes
may be polycistronic, though the molecular mechanisms by which
microprotein-encoding transcripts are generated or processed are not yet
defined. Humanin's neuroprotective effects have been proposed to occur
through multiple intracellular and cell-surface interaction partners,
including BAX, IGFBP3, FPRL1, and CNTF Receptor \selectlanguage{greek}α/\selectlanguage{english}WSX-1/gp130, though
the relative contributions of these pathways to its in vivo activity
remain to be determined. A circular dichroism and NMR study of humanin
revealed that it does not adopt a stable secondary structure in aqueous
solution, although through-space interactions consistent with turns at
the N- and C-termini of the peptide were observed. In contrast, in 30\%
organic solvent, humanin forms an alpha helix spanning residues G5 to
L18 (Figure 4D). This suggests that humanin may fold in hydrophobic
environments such as cell membranes or in complex with interaction
partners. Testing this hypothesis could provide deeper insight into its
localization and associations with functional interaction partner(s).

\emph{3.3 \textbar{} Ubiquitin-like microproteins}

Several groups recently reported the discovery of ubiquitin-like
microproteins. In one example, the ubiquitin pseudogene \emph{UBBP4} was
reported to be translated. Interestingly, \emph{UBBP4} encodes three
ubiquitin variants within two independent open reading frames, and mass
spectrometric evidence uniquely identifying all three have been
previously obtained. The \emph{UBBP4} ubiquitin-like proteins exhibit
high sequence similarity to canonical ubiquitin, with 1 (variant
Ubbp4\textsuperscript{A1}), 4 (Ubbp4\textsuperscript{B1} or
Ub\textsuperscript{KEKS}), or 8 amino acid substitutions
(Ubbp4\textsuperscript{A2}). Ubbp4\textsuperscript{A2} and
Ub\textsuperscript{KEKS} retain a functional C-terminal diglycine motif
and can be covalently conjugated to high molecular weight cellular
proteins, while Ubbp\textsuperscript{A1} was predominantly observed as a
monomer. Despite being \textasciitilde{}700-fold less abundant than
canonical ubiquitin, Ub\textsuperscript{KEKS} modifies a specific subset
of cellular proteins including lamins, and, rather than promoting
proteasomal degradation, may be important for regulating target protein
localization and/or function.

In 2020 the \emph{TINCR} RNA, which was previously classified as
noncoding, was shown to encode an 87-amino acid microprotein with 85\%
sequence homology to ubiquitin. The microprotein translated
from\emph{TINCR} RNA, termed pTINCR or TUBL, was predicted to adopt a
ubiquitin-like fold (Figure 4D). This prediction was confirmed in a
recent crystal structure of pTINCR, which revealed an overall
ubiquitin-like fold with a positively charged N-terminal domain
hypothesized to enable interaction with other biomolecules (Figure 4E).
Due to the lack of a C-terminal diglycine motif, pTINCR is a type II
ubiquitin-like protein that associates with ubiquitin-binding proteins
rather than being covalently attached to proteins. pTINCR is expressed
in skin, and mice lacking pTINCR exhibit a mild delay in wound healing.
Importantly, two reports have identified pTINCR as a tumor suppressor in
cutaneous squamous cell carcinoma and other epithelial cancers. pTINCR
is upregulated after DNA damage-induced p53 activation, and it is
frequently lost or mutated in squamous cell carcinoma. It normally
promotes differentiation of keratinocytes and other epithelial cell
types via its interaction with SUMOylated Cdc42. Consequently, mouse
embryonic stem cell-derived teratomas overexpressing pTINCR exhibit
decreased growth and increased keratin deposition consistent with
involvement in differentiation of skin cells. Along the same lines,
pTINCR overexpression inhibits the proliferation of squamous cell
carcinoma cells in culture and in xenografts. Additionally, mice
heterozygous for \emph{Xpc} that lack pTINCR are DNA damage
repair-deficient and exhibit increased formation of invasive skin
papillomas and squamous cell carcinomas relative to
\emph{Xpc}heterozygous/pTINCR wild-type mice upon UV exposure. Overall,
pTINCR is a type II ubiquitin-like microprotein that is required for
keratinocyte differentiation and acts as a tumor suppressor in squamous
cell carcinoma.

\emph{3.4 \textbar{} Microproteins with predicted structures}

With the advent of three-dimensional macromolecular structure prediction
tools such as Rosetta, iTasser, Phyre, and, most recently, AlphaFold,
many recently discovered, now-annotated microproteins have been
subjected to computational structure prediction, and these structural
models are publicly available. For microproteins that remain
unannotated, computational tools can be used to generate testable
structural predictions. For example, analysis of the recently
identified\emph{E. coli} cold-shock microprotein YmcF using iTasser led
to the hypothesis that YmcF may adopt a folded structure consisting of
an alpha helix and 2-3 beta strands separated by a turn, homologous to a
zinc-binding domain of aspartate transcarbamoylase (Figure 4G). While no
functional data for YmcF yet exists, this predicted structural model, if
correct, may have implications in the cold shock response, which
requires RNA binding proteins---some of which coordinate zinc---to
chaperone RNA secondary structures that become hyper-stable at low
temperature. In another example, plant microProteins are specifically
defined as proteins predicted to fold into single domains that bind to
and generally antagonize the functions of their effectors, such as
transcription factors.

Predicted structures of microproteins have already begun to aid in
determining their molecular and cellular functions. A translated
upstream ORF (uORF) encoding a 96-amino acid microprotein within the 5'
untranslated region (UTR) of the human \emph{ASNSD1} gene was reported
by Oyama et al. in 2007 and in subsequent proteomic analyses, leading to
the annotation of the microprotein as ASDURF (ASNSD1 upstream open
reading frame). As discussed above, evidence is accumulating that uORF
microproteins can function \emph{in trans.} Remarkably, Coulombe and
colleagues recently implicated ASDURF as the ``missing'' subunit of a
chaperone complex termed the PAQosome. Proximity biotinylation and
pull-down experiments with PAQosome subunits revealed ASDURF as an
interaction partner, and in vitro reconstitution assays suggested that
it is an integral member of a PAQosome subcomplex. The PAQosome is a
recently discovered chaperone that is essential for assembling
complicated macromolecular complexes in the cell, including RNA
polymerases, components of the spliceosome, and protein phosphatases.
The PAQosome consists of two modules, one of which is termed the
prefoldin-like (PFDL) module. The PFDL module shares some subunits and
putative structural homology to prefoldin, another cellular chaperone
required for folding cytoskeletal proteins and other clients. Prefoldin
and the PFDL module are both hexameric, consisting of three alpha- and
three beta-prefoldin subunits, which both contain an alpha-helical
coiled-coil separated by either one (beta) or two (alpha) hairpins;
however, only five of the six PFDL subunits (three alpha and two beta)
had been identified. Tertiary structure modeling with Phyre suggested
that ASDURF is a beta-prefoldin bearing a single beta hairpin and
coiled-coil (Figure 4H), consistent with its potential identification as
the undiscovered beta subunit of the PFDL module of the PAQosome --
suggesting it had been missed because it was not part of the proteome
annotation at the time of the PAQosome's discovery. Many additional
interesting questions are raised by the ASDURF microprotein: Why is it
encoded in an upstream ORF within the \emph{ASNSD1} gene? Does its 5'
UTR location confer stress responsiveness via translational regulation,
as suggested by Cloutier et al.? Is its function or regulation related
to the downstream ASNSD1 protein, per the model of Chen, Weissman and
colleagues that co-encoded microproteins and proteins tend to function
in the same pathways? Regardless, while the structural model requires
experimental validation, it appears that ASDURF is a particularly
compelling example of a microprotein for which structure prediction
informs its interactions and likely function.

\emph{3.5 \textbar{} Intrinsically disordered microproteins}

Microproteins are much shorter than annotated proteins, and they tend to
exhibit limited conservation to protein domains of known function. As a
result, it is challenging to perform bioinformatic analyses, for example
of predicted structure or intrinsic disorder, of microproteins with
confidence, particularly because many of these predictive algorithms
rely, at least in part, on homology to structures of known, larger
proteins on which they are trained. Nonetheless, some studies have
suggested that microproteins may be enriched in intrinsic disorder
relative to canonical proteins (though an alternative analysis suggests
that evolutionarily young microproteins are de-enriched in intrinsic
disorder), which, if true, suggests that some microproteins could carry
out cellular functions associated with intrinsically disordered
proteins, such as regulating signaling and other processes by binding to
protein partners via short linear interaction motifs (SLIMs). In this
section we discuss two human microproteins that have been experimentally
confirmed to be predominantly intrinsically disordered.

MRI (Modulator of retroviral infection) was first identified in a cDNA
library screen for host proteins that could complement resistance to
retroviral infection of human cells, but it remained annotated as a
predicted or uncharacterized protein-coding gene (\emph{C7ORF49} ) in
the early 2010s. While the long isoform of MRI (MRI-1 hereafter) is 157
amino acids long and therefore not a microprotein, a 2013 peptidomics
study identified an unannotated, sORF encoded isoform (MRI-2) of 69
amino acids. Follow-up work demonstrated that the long MRI-1 and short
MRI-2 proteins could interact with a complex of proteins essential for
the non-homologous end joining pathway (NHEJ), which is essential for
repairing DNA double strand breaks in G1 phase of the cell cycle, as
well as for B and T cell receptor gene diversification via V(D)J
recombination. Specifically, MRI-1 interacts with the double-strand
break binding adaptor proteins Ku70/80 (Ku) and DNA-PKcs (DNA-dependent
protein kinase catalytic subunit), while MRI-2 binds to Ku. Both of
these MRI isoforms contain an N-terminal Ku-binding motif, explaining
their association with Ku, while MRI-1 also contains a C-terminal
XLF-like motif (XLM) that associates with additional, distinct NHEJ
factors. The XLM of MRI-1 is absent in the frameshifted, truncated MRI-2
isoform. One study suggests that MRI inhibits aberrant NHEJ at telomeres
during S phase, while two studies to date are consistent with a positive
role for MRI in NHEJ during most phases of the cell cycle, suggesting
that the activity of MRI may be context-dependent. Purified MRI-2 was
shown to promote NHEJ in vitro. However, abrogating all isoforms via
knockout of the MRI gene in vivo and in pre-B cells increases
sensitivity to ionizing radiation and inhibits NHEJ when coupled with
knockout of the NHEJ ``sentinel'' gene XLF. Purified MRI-1 was shown to
be predominantly intrinsically disordered via hydrogen-deuterium
exchange; while MRI-2 was not directly investigated in this study, it is
likely to have a similar degree of intrinsic disorder because these
proteins share substantial sequence identity until the frameshift that
truncates MRI-2. Interestingly, the N-terminal and C-terminal motifs of
MRI-1 alone can nucleate separate complexes of NHEJ factors, and MRI-1
can recruit NHEJ factors to chromatin in the presence of DNA double
strand breaks. It is interesting to speculate MRI-2 may therefore be
able to serve the same nucleating function in NHEJ via its Ku-binding
motif even in the absence of the C-terminal XLM. Sleckman and colleagues
proposed that MRI-1 serves as an adaptor protein for NHEJ, promoting
stable association of active NHEJ complexes at sites of double strand
breaks as a result of its (1) intrinsic disorder, (2) independent linear
interaction motifs, and (3) its potential to multimerize. While better
understanding of the contributions of individual MRI isoforms to their
function in vivo is required, MRI-1 and MRI-2 appear to be paradigmatic
examples of intrinsically disordered (micro)proteins that promote
assembly of a functional protein interaction network.

Another example of an experimentally validated, intrinsically disordered
microprotein is NBDY. NBDY is a 68-amino acid microprotein expressed
from a previously misannotated lncRNA (\emph{LOC550643} ). NBDY
associates with members of the cytoplasmic mRNA decapping complex. The
interaction partners of NBDY, EDC4 and DCP1A, are coactivators required
for allosteric activation of DCP2, which catalyzes the first step in
5'-to-3' mRNA decay (removal of the 7-methylguanosine cap), thus
regulating the stability of thousands of specific mRNA substrates .
Genetic ablation or silencing of NBDY stabilizes a majority of DCP2
substrates, consistent with the requirement of NBDY for their effective
decapping, including transcripts encoding proteins involved in immune
responses -- a pathway previously reported to be regulated by DCP2.
However, at the same time, a number of DCP2 substrates are destabilized
by NBDY ablation, suggesting that the microprotein may act as a
specificity factor for recruitment of mRNA targets to the decapping
complex. In particular, in the presence of NBDY, DCP2 substrate mRNAs
with shorter 5'UTRs decay more rapidly, suggesting that there may be a
requirement for NBDY for efficient recognition of transcripts with short
leader sequences by DCP2. While the molecular mechanism by which NBDY
regulates the mRNA decapping complex is not yet known, mRNA decapping
proteins have previously been reported to associate via SLIMs within
disordered regions, and it is likely that NBDY participates in this
network. NMR experiments indicated that NBDY is largely intrinsically
disordered in solution, consistent with its ability to phase-separate in
the presence of RNA to form liquid droplets in vitro. Within the
intrinsically disordered NBDY sequence, two independent SLIMs interact
with the WD40 domain of EDC4 and the EVH1 domain of DCP1A. The
interaction between EDC4 and NBDY appears to be more important for NBDY
function in mutagenesis experiments, but, given the relatively low
affinity of NBDY for EDC4 (K\textsubscript{D} \textasciitilde{} 1
micromolar), the interaction with DCP1A could speculatively be important
for increasing avidity of NBDY for the mRNA decapping complex, retaining
it at interaction sites. Importantly, NBDY also partially localizes to
and regulates phase-separated RNA granules termed P-bodies in cells,
consistent with a role for intrinsically disordered microproteins in
biological phase separation. NBDY is phosphorylated downstream of EGFR
and cyclin-dependent kinase signaling, and this phosphorylation is
required for dissociation of P-bodies -- likely via electrostatic
repulsion of negatively charged P-body components that promotes
liquid-phase remixing and cell proliferation. Taken together, NBDY's
intrinsic disorder enables its SLIM-mediated protein-protein
interactions, phase separation and regulation of P-bodies, providing a
well-defined example of the functional significance of intrinsic
disorder in a microprotein.

\textbf{4 \textbar{} Conclusion}

As microproteins are increasingly linked with roles in human health and
disease, elucidating their numbers and biological roles will be ever
more essential. Regarding the complete annotation of microproteins,
while there are still inconsistencies in the specific sORF loci
identified across ribosome profiling studies, most recent studies detect
comparable numbers of translated sORFs in a given organism. This
developing consensus suggests that meta-analysis of ribosome profiling
data has the potential to resolve complete sORF translatomes in the near
future. As this effort advances, large-scale CRISPR screens and other
methods can be (re-)employed to identify functional sORFs on scale.
However, it is important to note that most sORF functional screens to
date have focused on cell proliferation/survival, protein-protein
interactions, and/or conservation, thus potentially screening out sORFs
with roles beyond these readouts. For example, microproteins with clear
involvement in yeast mating and cellular responses to stress have been
reported, but can be species-specific, nonessential and may not undergo
long-lived interactions with other proteins, and thus would not appear
as hits in most functional screens to date. Thus, alternative avenues to
identify microproteins with potential functions are needed. Given the
exquisite link between protein three-dimensional structure and function,
investigation of microprotein structure holds tremendous promise to
address this need. The advent of AlphaFold, combined with the rapidly
increasing number of solved microprotein structures and experimentally
characterized intrinsically disordered microproteins, including those
described above, are already contributing to the improved power of
structural prediction to generate functional hypotheses about
uncharacterized microproteins. Experimental structural investigations
are also providing critical mechanistic insights into how microproteins
exert their functions, for example in allosteric regulation of target
proteins. Combined with insights into disease-associated microprotein
mutations and dysregulation, structural and mechanistic information may
also pave the way to determining whether microproteins and/or their
binding partners are druggable in the future.

\textbf{Acknowledgments}

This work was supported by a Mark Foundation for Cancer Research
Emerging Leader Award, a Paul G. Allen Frontiers Group Distinguished
Investigator Award, and a Sloan Research Fellowship (FG-2022-18417) (to
S.A.S.), a Yale University fellowship associated with the NIGMS
Chemistry-Biology Interface Training Program (5T32GM067543), and a
Roberts Fellowship from the Yale University Department of Chemistry (to
J.J.M.).

\textbf{Conflict of Interest}

The authors have no conflicts of interest to declare.

\textbf{Data Availability Statement}

Data sharing is not applicable to this article as no new data were
created or analyzed in this study.

\textbf{References}

\textbf{Figure Legends}

FIGURE 1 Small Open Reading Frames (sORFs) and RNA. Box: Within mRNA
that encodes canonical protein coding sequences (CDS), sORFs can appear
in the 5' UTR (upstream ORF, uORF), initiating in the 5' UTR and
extending into the CDS in an alternative reading frame (upstream
overlapping ORF, u.oORF), in the 3' UTR (downstream ORF, dORF), or
nested within the CDS in an alternative reading frame. sORFs can also be
found in long noncoding RNA (lncRNA, bottom) and circular RNA (circRNA,
right), as well as additional classes of RNA not pictured.

FIGURE 2 Alternative Reading Frames for Same-Strand Overlapping (Nested)
sORFs. The +1 reading frame corresponds to the canonical coding sequence
and is always the frame of reference. Frameshifted translation in the +2
or +3 reading frames generates protein products with completely
different amino acid sequences because the codon identities are changed
in alternative reading frames.

FIGURE 3 Mass Spectrometry Workflow for Detection of Unannotated
Microproteins. To search for novel microproteins in a sample of
interest, low molecular weight proteins are isolated from total protein
after cell lysis. Size-exclusion techniques include, but are not limited
to, solid-phase extraction and polyacrylamide gel electrophoresis
techniques. Low molecular weight protein is digested with a protease,
producing a sample of uniform peptide length appropriate for mass
spectrometric (MS) analysis. Experimental spectra are generated and
matched to theoretical spectra from a custom database using proteomics
software. Detection of annotated microproteins known to be expressed in
the system of interest can serve as a positive control for success of
small protein enrichment and known small proteome coverage, but these
spectra are otherwise computationally excluded. Peptides deriving from
proteolysis of canonical proteins before size-exclusion are
computationally identified and excluded from consideration. High scoring
experimental spectra without any matches to known microproteins can be
subjected to further molecular validation, leading to annotation of
novel microproteins.

FIGURE 4 Experimentally Determined Microprotein Structures. (A) Crystal
structure of AcrB (grayscale) of the \emph{tolC} efflux pump in complex
with microprotein AcrZ (cyan). PDB: 5NC5. (B) Cryo-EM structure of
bacterial microprotein CydX (cyan) in complex with transmembrane
cytochrome bd-I oxidase (grayscale). PDB: 6RKO. (C) Crystal structure of
SERCA1a calcium pump (grayscale) with bound single-pass transmembrane
microprotein phospholamban (cyan), which downregulates SERCA activity.
PDB: 4Y3U. Solid-state NMR structure of helix-loop-helix microprotein
DWORF (cyan) modeled into SERCA1a calcium pump (grayscale) based on
Venkateswara et al. 2022. PDB: 4Y3U, 7MPA. (D) NMR structure of
wild-type humanin in 30\% 2,2,2-trifluoroethanol (organic) solution.
PBD: 1Y32. (E) Crystal structure of Ubiquitin monomer. PDB: 1AAR. (F)
Crystal Structure of ubiquitin-like TINCR microprotein with additional
N-terminal alpha helix. PDB: 7MRJ. (G) Predicted structure of bacterial
microprotein YmcF generated with AlphaFold, obtained from
UniProt{[}166{]} (green). Five cysteines (orange) in the YmcF sequence
are predicted to form a zinc-finger domain common to RNA binding
proteins. (H) Predicted structure of PAQosome binding microprotein
ASDURF generated with AlphaFold, obtained from UniProt{[}166{]}.\selectlanguage{english}
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