Biology subjects

Eicholt, L. A.

Publications and source records attributed to Eicholt, L. A..

8 recordsLinked to original sources

bioRxiv2026

A discrete protein subset drives structure prediction discordance in orphan proteins

Structure and disorder predictors are increasingly used as decision-grade tools in protein engineering and in the analysis of newly emerged proteins, yet how the current state-of-the-art behaves on sequences outside the well-charted evolutionary space remains poorly characterised. We previously reported that AlphaFold2 confidence and the disorder predictor flDPnn produced discordant predictions for naturally evolved de novo Drosophila proteins and for shuffled sequences. Here, we revisit the comparison with AlphaFold3 and the best-performing disorder predictor PUNCH2 on the same sequence sets together with conserved Drosophila proteins and intrinsically disordered proteins. The discordance persists: pLDDT correlates positively with PUNCH2 disorder in random and de novo proteins and negatively with {beta}-strand fraction, opposite to the conserved and disordered baselines. A class-specific, score-defined driver subset jointly captures the unusual high-pLDDT, high-disorder, low-strand combination and contains 24.5% of de novo, 29.4% of random, 5.1% of conserved, and 1.3% of disordered proteins. Removing this subset normalises the correlations. A held-out classifier trained on architectural and compositional features that were not used in the driver definition recovers the subset, with helix and coil fraction, sequence length, entropy and hydropathy as the strongest predictors. The discordance is therefore not a sequence-class artefact but a localised, compositionally identifiable phenotype that current predictors handle in a non-canonical way - a concrete failure mode that protein designers and others working on sequences remote in sequence space should be aware of when relying on predictor outputs.

bioinformatics↗

bioRxiv2026

How are evolutionarily young and old proteins distributed in sequence space?

Protein sequence space is vast due to the combinatorial diversity of 20 amino acids. However, evolution has generated a limited set of "old" canonical protein families sharing evolutionary ancestry, structures and functions. It remains unclear how canonical sequences are placed in sequence space, how recently evolved "young" proteins compare to them, and whether random, young, and canonical sequences can interconvert along evolutionarily plausible paths, and which biophysical properties distinguish or link these sequences. Here, we analyse naturally occurring de novo proteins from yeast and flies, which originate from non-coding DNA and thus have experienced limited evolutionary selection. They serve as a model for examining the relationships between young de novo and intergenic proteins, older canonical proteins, and their randomized counterparts. Because de novo and randomized sequences lack detectable homology, we use an alignment-free k-mer-based distance approach. Randomization shifts distance distributions toward expected random behaviour in all classes, but natural, non-randomized sequence classes remain distinct, indicating non-random residue organization. Each class exhibits characteristic k-mer patterns, with de novo proteins clearly separated from both canonical and all randomized sequences. Sequences bridging these classes are frequently predicted to contain transmembrane helices. De novo proteins are thus not random samples of sequence space. Instead, they occupy constrained yet evolutionarily accessible regions defined by residue order and biophysical constraints, suggesting a plausible pathway for the emergence and diversification of new proteins. Significance StatementDespite the vast combinatorial potential of amino acids, evolution has produced only a limited repertoire of canonical proteins with conserved structure and function. How evolutionarily young proteins relate to older canonical proteins, and whether the sequence space between them is traversable, remain unclear. Here, we decompose canonical proteins, intergenic sequences, and recently emerged yeast and fly de novo proteins, together with randomized controls, into short, interpretable fragments (k-mers) and compare them using alignment-free distances. De novo proteins are markedly distinct from both randomized and canonical sequences. Notwithstanding their evolutionary distance, sequences are connected by stepwise paths comprising bridge sequences, often enriched for low-complexity motifs and transmembrane helices, connecting disordered and structured regions of sequence space.

evolutionary biology↗

bioRxiv2025

Pervasive translation of short open reading frames and de novo gene emergence in Arabidopsis

Ancestrally non-genic sequences are now widely recognized as potential reservoirs for the de novo emergence of new genes. Across clades, some de novo genes were proven to have substantial phenotypic effects, and to contribute to the emergence of novel biological functions. Yet, little is still known about the starting material from which de novo genes emerge, especially in plants. To fill this gap, we generated Ribosome Profiling data from Arabidopsis lyrata and characterized the evolution of translated regions genome-wide across the Arabidopsis genus. Synteny analysis revealed 163 actively translated regions in A. lyrata whose coding potential (Open Reading Frames, ORFs or Coding DNA sequences, CDSs) has emerged de novo within the Arabidopsis genus. Most of these de novo translated regions were species- and even accession-specific, indicating their transient nature, with patterns of polymorphism consistent with neutral evolution in natural populations. They were also significantly shorter and less expressed than conserved protein-coding genes, and their GC content increased with phylogenetic conservation. Twenty-one of them belonged to previously annotated CDSs, and are therefore promising putative de novo genes, while most were located in intergenic regions and are thus newly discovered. Our results demonstrate the abundance of translation events outside of conserved CDSs, and their role as starting material for the emergence of novel genes in plants. Significance statementA central open question in genome evolution is how novel protein-coding genes arise from noncoding nucleotide DNA sequences and eventually contribute to the stable repertoires of "canonical" genes. Here, we focused specifically on the early stages of this important evolutionary process, whereby previously non-coding intergenic nucleotide sequences eventually acquire open reading frames carrying signatures of active translation. This phenomenon has been crucially under-studied so far, especially in plants. By combining ribosome profiling data and a careful genome comparison strategy among closely related Arabidopsis species, our results demonstrate the pervasive translation and de novo origin of a large number of small intergenic ORFs and illustrate their role as starting material for the emergence of novel genes in plants. Key-words: de novo genes, Arabidopsis, pervasive translation, intergenic ORFs, ribosome profiling

genomics↗

bioRxiv2025

Orthologs of an essential orphan gene vary in their capacities for function and subcellular localization in Drosophila melanogaster

Orphan genes evolve rapidly, raising questions about whether their functions remain conserved or diverge across species. To address this, we investigated goddard (gdrd), an orphan gene essential for spermatogenesis in Drosophila melanogaster. Within the Drosophila genus, Gdrd proteins retain a conserved core structure but display substantial variation in length and primary sequence. Here we perform cross-species gene-swap assays in D. melanogaster testes to examine how these lineage-specific changes affect Gdrd function. Strikingly, the highly divergent D. mojavensis ortholog fully rescues fertility in gdrd null flies, suggesting that ancestral Gdrd acted within a conserved spermatogenesis pathway. By contrast, several orthologs, including one from a more closely related species, cannot substitute for the melanogaster gene. Cytological analysis shows that all divergent Gdrd orthologs retain some ability to interact with axonemes and ring centrioles, consistent with the proteins structural conservation, but many non-complementing orthologs display weaker axonemal binding. Furthermore, all tested orthologs exhibit divergent localizations to organellar structures. Using computational analyses and molecular dynamics simulations, we identified intrinsic protein qualities that may account for several observations made in the gene swap assays. Rescuing orthologs bear motifs with shared physicochemical properties in their intrinsically disordered regions, while non-rescuing variants exhibit structural instabilities. Taken together, these findings show that while Gdrds ancestral structure and interactions are conserved, several orthologs have undergone lineage-specific evolutionary changes.

genetics↗

bioRxiv2025

DeNoFo: a file format and toolkit for standardised, comparable de novo gene annotation

MotivationDe novo genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterised by their rapid evolution and their novel structural properties or functional roles, de novo genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results. ResultsThis work presents a standardised annotation format to document the methodology of de novo gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardised format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field. Availability and ImplementationDeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo. All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

bioinformatics↗

bioRxiv2024

Sequence, Structure and Functional space of Drosophila de novo proteins

During de novo emergence, new protein coding genes emerge from previously non-genic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, many functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterisation are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning based tools and refined the results for de novo proteins. We found that most de novo proteins are indeed different from conserved proteins both in their structure and sequence. However, some de novo proteins are predicted to adopt known protein folds, participate in cellular reactions, and to form biomolecular condensates. Apart from broadening our understanding of de novo protein evolution, our study also provides a large set of testable hypotheses for focused experimental studies on structure and function of de novo proteins in Drosophila.

bioinformatics↗

bioRxiv2023

Random, de novo and conserved proteins: How structure and disorder predictors perform differently

Understanding the emergence and structural characteristics of de novo and random proteins is crucial for unraveling protein evolution and designing novel enzymes. However, experimental determination of their structures remains challenging. Recent advancements in protein structure prediction, particularly with AlphaFold2 (AF2), have expanded our knowledge of protein structures, but their applicability to de novo and random proteins is unclear. In this study, we investigate the structural predictions and confidence scores of AF2 and protein language model (pLM)-based predictor ESMFold for de novo, random, and conserved proteins. We find that the structural predictions for de novo and random proteins differ significantly from conserved proteins. Interestingly, a positive correlation between disorder and confidence scores (pLDDT) is observed for de novo and random proteins, in contrast to the negative correlation observed for conserved proteins. Furthermore, the performance of structure predictors for de novo and random proteins is hampered by the lack of sequence identity. We also observe varying predicted disorder among different sequence length quartiles for random proteins, suggesting an influence of sequence length on disorder predictions. In conclusion, while structure predictors provide initial insights into the structural composition of de novo and random proteins, their accuracy and applicability to such proteins remain limited. Experimental determination of their structures is necessary for a comprehensive understanding. The positive correlation between disorder and pLDDT could imply a potential for conditional folding and transient binding interactions of de novo and random proteins.

bioinformatics↗

bioRxiv2022

Chaperones facilitate heterologous expression of naturally evolved putative de novo proteins

Over the past decade, evidence has accumulated that new protein coding genes can emerge de novo from previously non-coding DNA. Most studies have focused on large scale computational predictions of de novo protein coding genes across a wide range of organisms. In contrast, experimental data concerning the folding and function of de novo proteins is scarce. This might be due to difficulties in handling de novo proteins in vitro, as most are predicted to be short and disordered. Here we propose a guideline for the effective expression of eukaryotic de novo proteins in Escherichia coli. We used 11 sequences from Drosophila melanogaster and 10 from Homo sapiens, that are predicted de novo proteins from former studies, for heterologous expression. The candidate de novo proteins have varying secondary structure and disorder content. Using multiple combinations of purification tags, E. coli expression strains and chaperone systems, we were able to increase the number of solubly expressed putative de novo proteins from 30 % to 62 %. Our findings indicate that the best combination for expressing putative de novo proteins in E.coli is a GST-tag with T7 Express cells and co-expressed chaperones. We found that, overall, proteins with higher predicted disorder were easier to express.

molecular biology↗