Stochastic Gain and Loss of Novel Transcribed Open Reading Frames in the Human Lineage

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

Motivation

De 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.

Results

This 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 Implementation

DeNoFo 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.

The lawc gene emerged de novo from conserved genomic elements and acquired a broad expression pattern in Drosophila

It has recently become evident that the de novo emergence of genes is widespread and documented for a variety of organisms. De novo genes frequently emerge in proximity to existing genes, forming gene overlaps. Here, we present an analysis of the evolutionary history of a putative de novo gene, lawc, which overlaps with the conserved Trf2 gene, which encodes a general transcription factor in Drosophila melanogaster. We demonstrate that lawc emerged approximately 68 million years ago in the 5'-untranslated region (UTR) of Trf2 and displays an extensive spatiotemporal expression pattern. One of the most remarkable features of the lawc evolutionary history is that its emergence was facilitated by the engagement of Drosophilidae-specific short, highly conserved regions located in Trf2 introns. This represents a unique example of putative de novo gene birth involving conserved DNA regions localized in introns of conserved genes. The observed lawc expression pattern may be due to the overlap of lawc with the 5'-UTR of Trf2. This study not only enriches our understanding of gene evolution but also highlights the complex interplay between genetic conservation and innovation.

Modeling Length Changes in De Novo Open Reading Frames during Neutral Evolution

For protein coding genes to emerge de novo from a non-genic DNA, the DNA sequence must gain an open reading frame (ORF) and the ability to be transcribed. The newborn de novo gene can further evolve to accumulate changes in its sequence. Consequently, it can also elongate or shrink with time. Existing literature shows that older de novo genes have longer ORF, but it is not clear if they elongated with time or remained of the same length since their inception. To address this question we developed a mathematical model of ORF elongation as a Markov-jump process, and show that ORFs tend to keep their length in short evolutionary timescales. We also show that if change occurs it is likely to be a truncation. Our genomics and transcriptomics data analyses of seven Drosophila melanogaster populations are also in agreement with the model's prediction. We conclude that selection could facilitate ORF length extension that may explain why longer ORFs were observed in old de novo genes in studies analysing longer evolutionary time scales. Alternatively, shorter ORFs may be purged because they may be less likely to yield functional proteins.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

High-throughput Selection of Human de novo-emerged sORFs with High Folding Potential

De novo genes emerge from previously noncoding stretches of the genome. Their encoded de novo proteins are generally expected to be similar to random sequences and, accordingly, with no stable tertiary fold and high predicted disorder. However, structural properties of de novo proteins and whether they differ during the stages of emergence and fixation have not been studied in depth and rely heavily on predictions. Here we generated a library of short human putative de novo proteins of varying lengths and ages and sorted the candidates according to their structural compactness and disorder propensity. Using Förster resonance energy transfer combined with Fluorescence-activated cell sorting, we were able to screen the library for most compact protein structures, as well as most elongated and flexible structures. We find that compact de novo proteins are on average slightly shorter and contain lower predicted disorder than less compact ones. The predicted structures for most and least compact de novo proteins correspond to expectations in that they contain more secondary structure content or higher disorder content, respectively. Our experiments indicate that older de novo proteins have higher compactness and structural propensity compared with young ones. We discuss possible evolutionary scenarios and their implications underlying the age-dependencies of compactness and structural content of putative de novo proteins.

Origin of functional de novo genes in humans from "hopeful monsters"

For a long time, it was believed that new genes arise only from modifications of preexisting genes, but the discovery of de novo protein-coding genes that originated from noncoding DNA regions demonstrates the existence of a "motherless" origination process for new genes. However, the features, distributions, expression profiles, and origin modes of these genes in humans seem to support the notion that their origin is not a purely "motherless" process; rather, these genes arise preferentially from genomic regions encoding preexisting precursors with gene-like features. In such a case, the gene loci are typically not brand new. In this short review, we will summarize the definition and features of human de novo genes and clarify their process of origination from ancestral non-coding genomic regions. In addition, we define the favored precursors, or "hopeful monsters," for the origin of de novo genes and present a discussion of the functional significance of these young genes in brain development and tumorigenesis in humans. This article is categorized under: RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

Towards understanding paleoclimate impacts on primate de novo genes

De novo genes are genes that emerge as new genes in some species, such as primate de novo genes that emerge in certain primate species. Over the past decade, a great deal of research has been conducted regarding their emergence, origins, functions, and various attributes in different species, some of which have involved estimating the ages of de novo genes. However, limited by the number of species available for whole-genome sequencing, relatively few studies have focused specifically on the emergence time of primate de novo genes. Among those, even fewer investigate the association between primate gene emergence with environmental factors, such as paleoclimate (ancient climate) conditions. This study investigates the relationship between paleoclimate and human gene emergence at primate species divergence. Based on 32 available primate genome sequences, this study has revealed possible associations between temperature changes and the emergence of de novo primate genes. Overall, findings in this study are that de novo genes tended to emerge in the recent 13 MY when the temperature continues cooling, which is consistent with past findings. Furthermore, in the context of an overall trend of cooling temperature, new primate genes were more likely to emerge during local warming periods, where the warm temperature more closely resembled the environmental condition that preceded the cooling trend. Results also indicate that both primate de novo genes and human cancer-associated genes have later origins in comparison to random human genes. Future studies can be in-depth on understanding human de novo gene emergence from an environmental perspective as well as understanding species divergence from a gene emergence perspective.

Thousands of Pristionchus pacificus orphan genes were integrated into developmental networks that respond to diverse environmental microbiota

Adaptation of organisms to environmental change may be facilitated by the creation of new genes. New genes without homologs in other lineages are known as taxonomically-restricted orphan genes and may result from divergence or de novo formation. Previously, we have extensively characterized the evolution and origin of such orphan genes in the nematode model organism Pristionchus pacificus. Here, we employ large-scale transcriptomics to establish potential functional associations and to measure the degree of transcriptional plasticity among orphan genes. Specifically, we analyzed 24 RNA-seq samples from adult P. pacificus worms raised on 24 different monoxenic bacterial cultures. Based on coexpression analysis, we identified 28 large modules that harbor 3,727 diplogastrid-specific orphan genes and that respond dynamically to different bacteria. These coexpression modules have distinct regulatory architecture and also exhibit differential expression patterns across development suggesting a link between bacterial response networks and development. Phylostratigraphy revealed a considerably high number of family- and even species-specific orphan genes in certain coexpression modules. This suggests that new genes are not attached randomly to existing cellular networks and that integration can happen very fast. Integrative analysis of protein domains, gene expression and ortholog data facilitated the assignments of biological labels for 22 coexpression modules with one of the largest, fast-evolving module being associated with spermatogenesis. In summary, this work presents the first functional annotation for thousands of P. pacificus orphan genes and reveals insights into their integration into environmentally responsive gene networks.

Population genomics reveals mechanisms and dynamics of de novo expressed open reading frame emergence in Drosophila melanogaster

Novel genes are essential for evolutionary innovations and differ substantially even between closely related species. Recently, multiple studies across many taxa showed that some novel genes arise de novo, that is, from previously noncoding DNA. To characterize the underlying mutations that allowed de novo gene emergence and their order of occurrence, homologous regions must be detected within noncoding sequences in closely related sister genomes. So far, most studies do not detect noncoding homologs of de novo genes because of incomplete assemblies and annotations, and long evolutionary distances separating genomes. Here, we overcome these issues by searching for de novo expressed open reading frames (neORFs), the not-yet fixed precursors of de novo genes that emerged within a single species. We sequenced and assembled genomes with long-read technology and the corresponding transcriptomes from inbred lines of Drosophila melanogaster, derived from seven geographically diverse populations. We found line-specific neORFs in abundance but few neORFs shared by lines, suggesting a rapid turnover. Gain and loss of transcription is more frequent than the creation of ORFs, for example, by forming new start and stop codons. Consequently, the gain of ORFs becomes rate limiting and is frequently the initial step in neORFs emergence. Furthermore, transposable elements (TEs) are major drivers for intragenomic duplications of neORFs, yet TE insertions are less important for the emergence of neORFs. However, highly mutable genomic regions around TEs provide new features that enable gene birth. In conclusion, neORFs have a high birth-death rate, are rapidly purged, but surviving neORFs spread neutrally through populations and within genomes.

Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms

"Newly Born" proteins, devoid of detectable homology to any other proteins, known as orphan proteins, occur in a single species or within a taxonomically restricted gene family. They are generated by the expression of novel open reading frames, and appear throughout evolution. We were curious if three recently developed programs for predicting protein structures, namely, AlphaFold2, RoseTTAFold, and ESMFold, might be of value for comparison of such "Newly Born" proteins to random polypeptides with amino acid content similar to that of native proteins, which have been called "Never Born" proteins. The programs were used to compare the structures of two sets of "Never Born" proteins that had been expressed-Group 1, which had been shown experimentally to possess substantial secondary structure, and Group 3, which had been shown to be intrinsically disordered. Overall, although the models generated were scored as being of low quality, they nevertheless revealed some general principles. Specifically, all four members of Group 1 were predicted to be compact by all three algorithms, in agreement with the experimental data, whereas the members of Group 3 were predicted to be very extended, as would be expected for intrinsically disordered proteins, again consistent with the experimental data. These predicted differences were shown to be statistically significant by comparing their accessible surface areas. The three programs were then used to predict the structures of three orphan proteins whose crystal structures had been solved, two of which display novel folds. Surprisingly, only for the protein which did not have a novel fold, and was taxonomically restricted, rather than being a true orphan, did all three algorithms predict very similar, high-quality structures, closely resembling the crystal structure. Finally, they were used to predict the structures of seven orphan proteins with well-identified biological functions, whose 3D structures are not known. Two proteins, which were predicted to be disordered based on their sequences, are predicted by all three structure algorithms to be extended structures. The other five were predicted to be compact structures with only two exceptions in the case of AlphaFold2. All three prediction algorithms make remarkably similar and high-quality predictions for one large protein, HCO_11565, from a nematode. It is conjectured that this is due to many homologs in the taxonomically restricted family of which it is a member, and to the fact that the Dali server revealed several nonrelated proteins with similar folds. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:Proteins:3.

A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

Experimental characterization of de novo proteins and their unevolved random-sequence counterparts

De novo gene emergence provides a route for new proteins to be formed from previously non-coding DNA. Proteins born in this way are considered random sequences and typically assumed to lack defined structure. While it remains unclear how likely a de novo protein is to assume a soluble and stable tertiary structure, intersecting evidence from random sequence and de novo-designed proteins suggests that native-like biophysical properties are abundant in sequence space. Taking putative de novo proteins identified in human and fly, we experimentally characterize a library of these sequences to assess their solubility and structure propensity. We compare this library to a set of synthetic random proteins with no evolutionary history. Bioinformatic prediction suggests that de novo proteins may have remarkably similar distributions of biophysical properties to unevolved random sequences of a given length and amino acid composition. However, upon expression in vitro, de novo proteins exhibit moderately higher solubility which is further induced by the DnaK chaperone system. We suggest that while synthetic random sequences are a useful proxy for de novo proteins in terms of structure propensity, de novo proteins may be better integrated in the cellular system than random expectation, given their higher solubility.

Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning

Background: De novo protein coding genes emerge from scratch in the non-coding regions of the genome and have, per definition, no homology to other genes. Therefore, their encoded de novo proteins belong to the so-called "dark protein space". So far, only four de novo protein structures have been experimentally approximated. Low homology, presumed high disorder and limited structures result in low confidence structural predictions for de novo proteins in most cases. Here, we look at the most widely used structure and disorder predictors and assess their applicability for de novo emerged proteins. Since AlphaFold2 is based on the generation of multiple sequence alignments and was trained on solved structures of largely conserved and globular proteins, its performance on de novo proteins remains unknown. More recently, natural language models of proteins have been used for alignment-free structure predictions, potentially making them more suitable for de novo proteins than AlphaFold2. Methods: We applied different disorder predictors (IUPred3 short/long, flDPnn) and structure predictors, AlphaFold2 on the one hand and language-based models (Omegafold, ESMfold, RGN2) on the other hand, to four de novo proteins with experimental evidence on structure. We compared the resulting predictions between the different predictors as well as to the existing experimental evidence. Results: Results from IUPred, the most widely used disorder predictor, depend heavily on the choice of parameters and differ significantly from flDPnn which has been found to outperform most other predictors in a comparative assessment study recently. Similarly, different structure predictors yielded varying results and confidence scores for de novo proteins. Conclusions: We suggest that, while in some cases protein language model based approaches might be more accurate than AlphaFold2, the structure prediction of de novo emerged proteins remains a difficult task for any predictor, be it disorder or structure.

Evolution and implications of de novo genes in humans

Genes and translated open reading frames (ORFs) that emerged de novo from previously non-coding sequences provide species with opportunities for adaptation. When aberrantly activated, some human-specific de novo genes and ORFs have disease-promoting properties-for instance, driving tumour growth. Thousands of putative de novo coding sequences have been described in humans, but we still do not know what fraction of those ORFs has readily acquired a function. Here, we discuss the challenges and controversies surrounding the detection, mechanisms of origin, annotation, validation and characterization of de novo genes and ORFs. Through manual curation of literature and databases, we provide a thorough table with most de novo genes reported for humans to date. We re-evaluate each locus by tracing the enabling mutations and list proposed disease associations, protein characteristics and supporting evidence for translation and protein detection. This work will support future explorations of de novo genes and ORFs in humans.

De novo birth of functional microproteins in the human lineage

Small open reading frames (sORFs) can encode functional "microproteins" that perform crucial biological tasks. However, their size makes them less amenable to genomic analysis, and their origins and conservation are poorly understood. Given their short length, it is plausible that some of these functional microproteins have recently originated entirely de novo from noncoding sequences. Here we sought to identify such cases in the human lineage by reconstructing the evolutionary origins of human microproteins previously found to have measurable, statistically significant fitness effects. By tracing the formation of each ORF and its transcriptional activation, we show that novel microproteins with significant phenotypic effects have emerged de novo throughout animal evolution, including two after the human-chimpanzee split. Notably, traditional methods for assessing coding potential would miss most of these cases. This evidence demonstrates that the functional potential intrinsic to sORFs can be relatively rapidly and frequently realized through de novo gene emergence.

Heterologous expression of naturally evolved putative de novo proteins with chaperones

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 are scarce. This might be due to difficulties in handling de novo proteins in vitro, as most are short and predicted to be 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. STATEMENT: Today, we know that proteins do not only evolve by duplication and divergence of existing proteins but also arise from previously non-coding DNA. These proteins are called de novo proteins. Their properties are still poorly understood and their experimental analysis faces major obstacles. Here, we aim to present a starting point for soluble expression of de novo proteins with the help of chaperones and thereby enable further characterization.

Open reading frame dominance indicates protein‐coding potential of RNAs

Recent studies have identified numerous RNAs with both coding and noncoding functions. However, the sequence characteristics that determine this bifunctionality remain largely unknown. In the present study, we develop and test the open reading frame (ORF) dominance score, which we define as the fraction of the longest ORF in the sum of all putative ORF lengths. This score correlates with translation efficiency in coding transcripts and with translation of noncoding RNAs. In bacteria and archaea, coding and noncoding transcripts have narrow distributions of high and low ORF dominance, respectively, whereas those of eukaryotes show relatively broader ORF dominance distributions, with considerable overlap between coding and noncoding transcripts. The extent of overlap positively and negatively correlates with the mutation rate of genomes and the effective population size of species, respectively. Tissue‐specific transcripts show higher ORF dominance than ubiquitously expressed transcripts, and the majority of tissue‐specific transcripts are expressed in mature testes. These data suggest that the decrease in population size and the emergence of testes in eukaryotic organisms allowed for the evolution of potentially bifunctional RNAs.

Towards unveiling the nature of short SERPINA1 transcripts: Avoiding the main ORF control to translate alpha1-antitrypsin C-terminal peptides

Alternative ORFs in-frame with the known genes are challenging to reveal. Yet they may contribute significantly to proteome diversity. Here we focused on the individual expression of the SERPINA1 gene exon 5 leading to direct translation of alpha1-antitrypsin (AAT) C-terminal peptides. The discovery of alternative ways for their production may expand the current understanding of the serpin gene's functioning. We detected short transcripts expressed primarily in hepatocytes. We identified four variants of hepatocyte-specific SERPINA1 short transcripts and individually probed their potential to be translated in living cells. The long mRNA gave the full-length AAT-eGFP fusion, while in case of short transcripts we deduced four active SERPINA1 in-frame alternative ORFs encoding 10, 21, 153 and 169 amino acids AAT C-terminal oligo- and polypeptides. Unlike secretory AAT-eGFP fusion exhibiting classical AAT behavior, truncated AAT-fusions differ by intracellular retention and nuclear enrichment. Immunofluorescence on the endogenous AAT C-terminal epitope showed its accumulation in the cell nucleoli, indicating that short transcripts may be translated in vivo. FANTOM5 CAGE data on SERPINA1 suggest that short transcripts originate from the post-transcriptional cleavage of the spliced mRNA, initiated mainly from the hepatocyte-specific promoter. CONCLUSION: Short SERPINA1 transcripts may represent a source for the direct synthesis of AAT C-terminal peptides with properties uncommon to AAT.

New Genomic Signals Underlying the Emergence of Human Proto-Genes

De novo genes are novel genes which emerge from non-coding DNA. Until now, little is known about de novo genes’ properties, correlated to their age and mechanisms of emergence. In this study, we investigate four related properties: introns, upstream regulatory motifs, 5′ Untranslated regions (UTRs) and protein domains, in 23,135 human proto-genes. We found that proto-genes contain introns, whose number and position correlates with the genomic position of proto-gene emergence. The origin of these introns is debated, as our results suggest that 41% of proto-genes might have captured existing introns, and 13.7% of them do not splice the ORF. We show that proto-genes which emerged via overprinting tend to be more enriched in core promotor motifs, while intergenic and intronic genes are more enriched in enhancers, even if the TATA motif is most commonly found upstream in these genes. Intergenic and intronic 5′ UTRs of proto-genes have a lower potential to stabilise mRNA structures than exonic proto-genes and established human genes. Finally, we confirm that proteins expressed by proto-genes gain new putative domains with age. Overall, we find that regulatory motifs inducing transcription and translation of previously non-coding sequences may facilitate proto-gene emergence. Our study demonstrates that introns, 5′ UTRs, and domains have specific properties in proto-genes. We also emphasize that the genomic positions of de novo genes strongly impacts these properties.

Lineage-Specific Genes and Family Expansions in Dictyostelid Genomes Display Expression Bias and Evolutionary Diversification during Development

Gene duplications generate new genes that can contribute to expression changes and the evolution of new functions. Genomes often consist of gene families that undergo expansions, some of which occur in specific lineages that reflect recent adaptive diversification. In this study, lineage-specific genes and gene family expansions were studied across five dictyostelid species to determine when and how they are expressed during multicellular development. Lineage-specific genes were found to be enriched among genes with biased expression (predominant expression in one developmental stage) in each species and at most developmental time points, suggesting independent functional innovations of new genes throughout the phylogeny. Biased duplicate genes had greater expression divergence than their orthologs and paralogs, consistent with subfunctionalization or neofunctionalization. Lineage-specific expansions in particular had biased genes with both molecular signals of positive selection and high expression, suggesting adaptive genetic and transcriptional diversification following duplication. Our results present insights into the potential contributions of lineage-specific genes and families in generating species-specific phenotypes during multicellular development in dictyostelids.

Structure and function of naturally evolved de novo proteins

Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.