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# Artificial intelligence to shed light on the algal dark proteomes

Maxence Plouviez, Eric Dubreucq

► To cite this version:

Maxence Plouviez, Eric Dubreucq. Artificial intelligence to shed light on the algal dark proteomes. Patterns, 2025, 6 (11), pp.101421. 10.1016/j.patter.2025.101421. hal-05406209

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Submitted on 16 Dec 2025

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# Artificial intelligence to shed light on the algal dark proteomes

Maxence Plouviez<fnref n="1" />,* and Eric Dubreucq<fnref n="2" />

Nelson et al. developed LA⁴SR, a large-scale language-model framework to classify translated open reading frames (ORFeomes) across ten algal phyla. LA⁴SR achieves near-complete protein classification coverage even for algal dark proteomes, where alignment tools often return “no hit.” The framework provides a leap forward for high-throughput protein classification in algae and could be a promising tool for better classifying proteins from many organisms.

**Known unknowns:** omics technologies have significantly accelerated scientific discovery in biological research, but most importantly, they have redefined what we know about the genome's encoding potential. For instance, ribosome profiling led to the discovery that ribosomes are making proteins that map to "unexpected" regions of the genome initially not thought to produce proteins (i.e., noncanonical open reading frames [ORFs]).<fnref n="1" />⁻³ From microbes to humans, genetic information is therefore encoded with a greater density than previously suspected, and the proteome (i.e., the entire set of proteins from an organism) exhibits a level of complexity far exceeding earlier expectations.<fnref n="1" />,³,⁴ Unfortunately, many proteins remain "unknown," or "dark," because they lack detectable homologs in existing databases, meaning structural information or functional annotations are lacking.⁴,⁵ The expanse of this "dark proteome" is considerable, as metagenomics projects have revealed that 17% (~1.7 billion) of non-redundant microbial protein sequences from across the biosphere do not harbor homologs in any of the >100,000 available reference genomes.⁵ For lesser-studied organisms like algae, the dark part could represent 65% of the whole proteome.⁹ Scientists are only beginning to understand the role of these unknown proteins in cellular function and in responses to, for example, diseases in mammalian cells.<fnref n="2" />,³ Consequently, what biological information is held in the dark proteomes?

**The challenge:** classical alignment-based methods such as BLASTP and

DIAMOND are invaluable for predicting protein homology and function, but these methods perform poorly with highly divergent, chimeric, or incomplete sequences and cannot classify truly “novel” proteins that do not possess any homologs in databases.⁶⁻⁸ Consequently, how can the information held in the dark proteome be unraveled? Considering the recent advances in artificial intelligence (AI) to solve complex problems and decipher patterns in large datasets, could AI support protein classification and allow the annotation of unknown proteins from sequence datasets? The answer is “yes.”

**AI as a solution:** AI-supported bioinformatics already accelerates large-scale proteomics workflows and data interpretation, from peptide sequence determination to end-to-end analysis of complex datasets. Harnessing the capabilities of large language models (LLMs), Nelson et al.⁶ take this a step further, showing how LLM-based generative AI can greatly outperform conventional methods in speed, accuracy, and recall of protein classification (i.e., identification of all proteins that belong to a class). Alongside systems such as FANTASIA,⁵ which leverages protein language models for high-performance functional annotation, LLM-based AI opens a route to ultimately interrogate the dark proteome, as discussed below.

**LA⁴SR framework in a nutshell:** with promising capabilities in logical inference, knowledge synthesis, and cross-domain generalization, LLMs are showing potential to interpret chemical or biological data.⁷⁻⁹ The framework developed by Nelson

et al.⁶ relies on LLMs, specifically transformers (Mistral, nanoGPT, GPT-NeoX, ByT5, DistilRoBERTa, MiniLM, BLOOM-560m, and Pythia) and state-space models (S6 models from the Mamba framework). For training and performance evaluation, the authors constructed large-scale microbial genomics datasets comprising ~77 million distinct protein sequences, including translated ORFeomes from 166 microalgal genomes across 10 phyla (Chlorophyta, Rhodophyta, Haptophyta, Cercozoa, Ochrophyta, Myzozoa, Euglenophyta, Chlorachniophyta, Streptophyta, and Chromerida). Models were trained on either terminal information (TI)-inclusive datasets (i.e., full-length natural sequences) or TI-free datasets (i.e., synthetic chimeras with terminal regions and gene boundaries missing or mixed up), providing a robust and complementary approach to evaluate the protein classification performance across lineages (i.e., generalizability). Importantly, the datasets generated were also combined with bacterial (TI-free and TI-inclusive), archaeal, and fungal (TI-inclusive only) contaminant sequences, which is essential for the models to accurately recognize the genetic makeup of algae and identify potential contamination in sequencing data. The resultant TI-inclusive and TI-free models showed similar protein classification performance. However, TI-free models achieved up to 10-fold inference/tokenization speedups and exhibited distinctly fewer position-dependent attribution profiles. Therefore, the TI-free models demonstrated strong protein classification performance while exhibiting impressive

<footnote n="1">¹Cawthron Institute, 98 Halifax Street East, Nelson, New Zealand</footnote>

<footnote n="2">²IATE, Institut Agro - INRAE - Montpellier University, Montpellier, France</footnote>

<footnote>*Correspondence: maxence.plouviez@cawthron.org.nz</footnote>

<footnote>https://doi.org/10.1016/j.patter.2025.101421</footnote>
generalization capabilities, even when
handling incomplete or noisy sequence
data. This strong performance was further
evidenced during validation using holdout
sets comprising unseen sequences from
the training taxa (i.e., data that was not
used during the model training), data from
new in-house assemblies from seen spe-
cies (the model microalga *Chlamydomo-
nas reinhardtii*), and clean and contami-
nated assemblies from unseen genera
(including genomes published after the
model was trained). The authors also im-
plemented several tools (SHAP [Shapley
Additive Explanations], HELIX [Hidden
Embedding Layer Information Explorer],
DeepLift LA⁴SR, and DMMP [Deep Motif
Miner Pro]) to interpret and to improve con-
fidence in predictions. These tools also al-
lowed them to determine the mechanisms
of sequence pattern learning (e.g., how
specific amino acid residues, their pat-
ters, and positional relationships influ-
ence model decisions). This critical capa-
bility shed light on the biological relevance
of model outputs and enabled the identifi-
cation of, for example, evolutionarily mean-
ingful motifs across diverse taxa. This evi-
denced that interpretability tools are not
merely diagnostic but can also uncover
biological information. By revealing how
models learn from sequence data (e.g.,
identifying conserved motifs or residue
patterns), these tools bridge the gap be-
tween computational predictions and bio-
logical understanding.

**Performance and scope:** LA⁴SR pro-
cessed queries in 0.0378 ± 0.0029 s on
an A100 GPU, >10,000× faster than
NCBI BLASTP⁺ and 82× faster than DIA-
MOND on identical query sets. Across
complete algal proteomes, LA⁴SR pro-
vided near-complete protein classifica-
tion coverage, with >99% of sequences
receiving a model prediction vs. ~35%
hits with BLASTP/DIAMOND, and
achieved high accuracy on held-out and
unseen data (e.g., macro F₁ score of
0.9173). Overall, by treating protein se-
quences as a form of language, the devel-
oped models within the LA⁴SR framework
learned the syntax governing protein
function. This enabled the inference of
structural motifs, functional domains,
and evolutionary relationships from seen

and unseen sequences. This approach
extends the capability of conventional
alignment-based tools in terms of
throughput, coverage, and classification
of previously unannotated sequences,
while conventional methods remain indis-
pensable for fine-grained homology,
domain curation, and phylogenetic
inference.

However, as acknowledged by the au-
thors, the framework remains con-
strained by the limited availability of bio-
logical sequence data for several key
algal groups that are underrepresented
in public databases (e.g., non-Chloro-
phyta and non-Bacillariophyta). Broad-
ening the pool of experimental data
used for model training is therefore
crucial to further enhance model perfor-
mance and reliability.

The LA⁴SR framework, including all
models and tools, is distributed in a Sin-
gularity container freely available for ac-
demic use, making it efficient, customiz-
able, and accessible. The development
of computationally efficient architectures
and training strategies is a critical step to-
ward democratizing the use of LLMs
across the broader scientific commu-
nity.⁶⁻⁸ Customizable and generalizable,
the framework can be extended to addi-
tional taxa or organisms, deepening our
understanding of biomolecular structure,
function, and evolution. More specifically,
microalgae such as the model
C. reinhardtii offer a robust model system
for elucidating mechanisms of environ-
mental adaptation, including conserved
mechanisms that are shared across both
animals and plants.¹⁰ In combination
with richer datasets, advanced lan-
guage-model frameworks such as
LA⁴SR should enable the systematic
exploration of unknowns such as the
dark proteome and the discovery of new
metabolic pathways or regulatory mecha-
nisms. The deeper understanding gained
through these tools should guide the
development of advanced molecular and
genetic tools for fundamental research
or biotechnological applications (e.g.,
bioengineering). Characterizing proteins
with novel properties could therefore
lead to the development of new biotech-
nological opportunities.

LA⁴SR thus represents an important
step toward scalable and interpretable
protein annotation in algae, with substan-
tial potential for future developments and
applications.

DECLARATION OF INTERESTS

The authors declare no competing interests.

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