Homorepeat variability within the human population.

Genetic variation within populations plays a crucial role in driving evolution. Unlike the average protein sequence, the evolution of homorepeats can be influenced by DNA replication slippage, when DNA polymerases either add or skip repeats of nucleotides. While there are some diseases known to be caused by abnormal changes in the length of amino acid homorepeats, naturally occurring variations in homorepeat length remain relatively unexplored. In our study, we examined the variation in amino acid homorepeat length of human individuals by analyzing 125 748 exomes, as well as 15 708 whole genomes. Our analyses revealed significant variability in homorepeat length across the human population, indicating that these motifs are prone to mutations at higher rates than non repeat sequences. We focused our study on glutamine homorepeats, also known as polyQ sequences, and found that shorter polyQ sequences tend to exhibit greater length variation, while longer ones primarily undergo deletions. Notably, polyQ sequencesthat are more conserved across primates tend to show less variation within the human population, indicating stronger selective pressure to maintain their length. Overall, our results demonstrate that there is large natural variation in the length of homorepeats within the human population, with no apparent impact on observable traits.

Structured Tandem Repeats in Protein Interactions.

Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Structure-function relationships in protein homorepeats.

Homorepeats (or polyX), protein segments containing repetitions of the same amino acid, are abundant in proteomes from all kingdoms of life and are involved in crucial biological functions as well as several neurodegenerative and developmental diseases. Mainly inserted in disordered segments of proteins, the structure/function relationships of homorepeats remain largely unexplored. In this review, we summarize present knowledge for the most abundant homorepeats, highlighting the role of the inherent structure and the conformational influence exerted by their flanking regions. Recent experimental and computational methods enable residue-specific investigations of these regions and promise novel structural and dynamic information for this elusive group of proteins. This information should increase our knowledge about the structural bases of phenomena such as liquid-liquid phase separation and trinucleotide repeat disorders.

The nucleotide landscape of polyXY regions.

PolyXY regions are compositionally biased regions composed of two different amino acids. They are classified according to the arrangement of the two amino acid types 'X' and 'Y' into direpeats (composed of alternating amino acids, e.g. 'XYXYXY'), joined (composed of two consecutive stretches of each amino acid, e.g. 'XXXYYY') and shuffled (other arrangements, e.g., 'XYXXYY'). They have been characterized at the amino acid level in all domains of life, and are described as often found within intrinsically disordered regions. Since DNA replication slippage has been proposed as a driver of repeat variation, and given that some polyXY have a repetitive nature, we hypothesized that characterizing the nucleotide coding of various types of polyXY could give hints about their origin and evolution. To test this, we obtained all polyXY regions in the human transcriptome, categorized them, and studied their coding nucleotide sequences. We observed that polyXY exacerbates the codon biases, and that the similarity between the X and Y codons is higher than in the background proteome. Our results support a general mechanism of emergence and evolution of polyXY from single-codon polyX. PolyXY are revealed as hotspots for replication slippage, particularly those composed of repeats: joined and direpeat polyXY. Inter-conversion to shuffled polyXY disrupts nucleotide repeats and restricts further evolution by replication slippage, a mechanism that we previously observed in polyX. Our results shed light on polyXY composition and should simplify the determination of their functions.

A STRP-ed definition of Structured Tandem Repeats in Proteins.

Tandem Repeat Proteins (TRPs) are a class of proteins with repetitive amino acid sequences that have been studied extensively for over two decades. Different features at the level of sequence, structure, function and evolution have been attributed to them by various authors. And yet many of its salient features appear only when looking at specific subclasses of protein tandem repeats. Here, we attempt to rationalize the existing knowledge on Tandem Repeat Proteins (TRPs) by pointing out several dichotomies. The emerging picture is more nuanced than generally assumed and allows us to draw some boundaries of what is not a "proper" TRP. We conclude with an operational definition of a specific subset, which we have denominated STRPs (Structural Tandem Repeat Proteins), which separates a subclass of tandem repeats with distinctive features from several other less well-defined types of repeats. We believe that this definition will help researchers in the field to better characterize the biological meaning of this large yet largely understudied group of proteins.

Evolutionary Study of Protein Short Tandem Repeats in Protein Families.

Tandem repeats in proteins are patterns of residues repeated directly adjacent to each other. The evolution of these repeats can be assessed by using groups of homologous sequences, which can help pointing to events of unit duplication or deletion. High pressure in a protein family for variation of a given type of repeat might point to their function. Here, we propose the analysis of protein families to calculate protein short tandem repeats (pSTRs) in each protein sequence and assess their variability within the family in terms of number of units. To facilitate this analysis, we developed the pSTR tool, a method to analyze the evolution of protein short tandem repeats in a given protein family by pairwise comparisons between evolutionarily related protein sequences. We evaluated pSTR unit number variation in protein families of 12 complete metazoan proteomes. We hypothesize that families with more dynamic ensembles of repeats could reflect particular roles of these repeats in processes that require more adaptability.

Protein repeats evolve and emerge in giant viruses.

Nucleocytoplasmatic large DNA viruses (NCLDVs or giant viruses) stand out because of their relatively large genomes encoding hundreds of proteins. These species give us an unprecedented opportunity to study the emergence and evolution of repeats in protein sequences. On the one hand, as viruses, these species have a restricted set of functions, which can help us better define the functional landscape of repeats. On the other hand, given the particular use of the genetic machinery of the host, it is worth asking whether this allows the variations of genetic material that lead to repeats in non-viral species. To support research in the characterization of repeat protein evolution and function, we present here an analysis focused on the repeat proteins of giant viruses, namely tandem repeats (TRs), short repeats (SRs), and homorepeats (polyX). Proteins with large and short repeats are not very frequent in non-eukaryotic organisms because of the difficulties that their folding may entail; however, their presence in giant viruses remarks their advantage for performance in the protein environment of the eukaryotic host. The heterogeneous content of these TRs, SRs and polyX in some viruses hints at diverse needs. Comparisons to homologs suggest that the mechanisms that generate these repeats are extensively used by some of these viruses, but also their capacity to adopt genes with repeats. Giant viruses could be very good models for the study of the emergence and evolution of protein repeats.

Regions with two amino acids in protein sequences: A step forward from homorepeats into the low complexity landscape.

Low complexity regions (LCRs) differ in amino acid composition from the background provided by the corresponding proteomes. The simplest LCRs are homorepeats (or polyX), regions composed of mostly-one amino acid type. Extensive research has been done to characterize homorepeats, and their taxonomic, functional and structural features depend on the amino acid type and sequence context. From them, the next step towards the study of LCRs are the regions composed of two types of amino acids, which we call polyXY. We classify polyXY in three categories based on the arrangement of the two amino acid types 'X' and 'Y': direpeats (e.g. 'XYXYXY'), joined (e.g. 'XXXYYY') and shuffled (e.g. 'XYYXXY'). We developed a script to search for polyXY, and located them in a comprehensive set of 20,340 reference proteomes. These results are available in a dedicated web server called XYs, in which the user can also submit their own protein datasets to detect polyXY. We studied the distribution of polyXY types by amino acid pair XY and category, and show that polyXY in Eukaryota are mainly located within intrinsically disordered regions. Our study provides a first step towards the characterization of polyXY as protein motifs.

Functional Tuning of Intrinsically Disordered Regions in Human Proteins by Composition Bias.

Intrinsically disordered regions (IDRs) in protein sequences are flexible, have low structural constraints and as a result have faster rates of evolution. This lack of evolutionary conservation greatly limits the use of sequence homology for the classification and functional assessment of IDRs, as opposed to globular domains. The study of IDRs requires other properties for their classification and functional prediction. While composition bias is not a necessary property of IDRs, compositionally biased regions (CBRs) have been noted as frequent part of IDRs. We hypothesized that to characterize IDRs, it could be helpful to study their overlap with particular types of CBRs. Here, we evaluate this overlap in the human proteome. A total of 2/3 of residues in IDRs overlap CBRs. Considering CBRs enriched in one type of amino acid, we can distinguish CBRs that tend to be fully included within long IDRs (R, H, N, D, P, G), from those that partially overlap shorter IDRs (S, E, K, T), and others that tend to overlap IDR terminals (Q, A). CBRs overlap more often IDRs in nuclear proteins and in proteins involved in liquid-liquid phase separation (LLPS). Study of protein interaction networks reveals the enrichment of CBRs in IDRs by tandem repetition of short linear motifs (rich in S or P), and the existence of E-rich polar regions that could support specific protein interactions with non-specific interactions. Our results open ways to pin down the function of IDRs from their partial compositional biases.

The sequence context in poly-alanine regions: structure, function and conservation.

MOTIVATION: Poly-alanine (polyA) regions are protein stretches mostly composed of alanines. Despite their abundance in eukaryotic proteomes and their association to nine inherited human diseases, the structural and functional roles exerted by polyA stretches remain poorly understood. In this work we study how the amino acid context in which polyA regions are settled in proteins influences their structure and function. RESULTS: We identified glycine and proline as the most abundant amino acids within polyA and in the flanking regions of polyA tracts, in human proteins as well as in 17 additional eukaryotic species. Our analyses indicate that the non-structuring nature of these two amino acids influences the α-helical conformations predicted for polyA, suggesting a relevant role in reducing the inherent aggregation propensity of long polyA. Then, we show how polyA position in protein N-termini relates with their function as transit peptides. PolyA placed just after the initial methionine is often predicted as part of mitochondrial transit peptides, whereas when placed in downstream positions, polyA are part of signal peptides. A few examples from known structures suggest that short polyA can emerge by alanine substitutions in α-helices; but evolution by insertion is observed for longer polyA. Our results showcase the importance of studying the sequence context of homorepeats as a mechanism to shape their structure-function relationships. AVAILABILITY AND IMPLEMENTATION: The datasets used and/or analyzed during the current study are available from the corresponding author onreasonable request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Low Complexity Induces Structure in Protein Regions Predicted as Intrinsically Disordered.

There is increasing evidence that many intrinsically disordered regions (IDRs) in proteins play key functional roles through interactions with other proteins or nucleic acids. These interactions often exhibit a context-dependent structural behavior. We hypothesize that low complexity regions (LCRs), often found within IDRs, could have a role in inducing local structure in IDRs. To test this, we predicted IDRs in the human proteome and analyzed their structures or those of homologous sequences in the Protein Data Bank (PDB). We then identified two types of simple LCRs within IDRs: regions with only one (polyX or homorepeats) or with only two types of amino acids (polyXY). We were able to assign structural information from the PDB more often to these LCRs than to the surrounding IDRs (polyX 61.8% > polyXY 50.5% > IDRs 39.7%). The most frequently observed polyX and polyXY within IDRs contained E (Glu) or G (Gly). Structural analyses of these sequences and of homologs indicate that polyEK regions induce helical conformations, while the other most frequent LCRs induce coil structures. Our work proposes bioinformatics methods to help in the study of the structural behavior of IDRs and provides a solid basis suggesting a structuring role of LCRs within them.

Analysis of Huntington's Disease Modifiers Using the Hyperbolic Mapping of the Protein Interaction Network.

Huntington's disease (HD) is caused by the production of a mutant huntingtin (HTT) with an abnormally long poly-glutamine (polyQ) tract, forming aggregates and inclusions in neurons. Previous work by us and others has shown that an increase or decrease in polyQ-triggered aggregates can be passive simply due to the interaction of proteins with the aggregates. To search for proteins with active (functional) effects, which might be more effective in finding therapies and mechanisms of HD, we selected among the proteins that interact with HTT a total of 49 pairs of proteins that, while being paralogous to each other (and thus expected to have similar passive interaction with HTT), are located in different regions of the protein interaction network (suggesting participation in different pathways or complexes). Three of these 49 pairs contained members with opposite effects on HD, according to the literature. The negative members of the three pairs, MID1, IKBKG, and IKBKB, interact with PPP2CA and TUBB, which are known negative factors in HD, as well as with HSP90AA1 and RPS3. The positive members of the three pairs interact with HSPA9. Our results provide potential HD modifiers of functional relevance and reveal the dynamic aspect of paralog evolution within the interaction network.

PolyX2: Fast Detection of Homorepeats in Large Protein Datasets.

Homorepeat sequences, consecutive runs of identical amino acids, are prevalent in eukaryotic proteins. It has become necessary to annotate and evaluate this feature in entire proteomes. The definition of what constitutes a homorepeat is not fixed, and different research approaches may require different definitions; therefore, flexible approaches to analyze homorepeats in complete proteomes are needed. Here, we present polyX2, a fast, simple but tunable script to scan protein datasets for all possible homorepeats. The user can modify the length of the window to scan, the minimum number of identical residues that must be found in the window, and the types of homorepeats to be found.

Annotation and Analysis of 3902 Odorant Receptor Protein Sequences from 21 Insect Species Provide Insights into the Evolution of Odorant Receptor Gene Families in Solitary and Social Insects.

The gene family of insect olfactory receptors (ORs) has expanded greatly over the course of evolution. ORs enable insects to detect volatile chemicals and therefore play an important role in social interactions, enemy and prey recognition, and foraging. The sequences of several thousand ORs are known, but their specific function or their ligands have only been identified for very few of them. To advance the functional characterization of ORs, we have assembled, curated, and aligned the sequences of 3902 ORs from 21 insect species, which we provide as an annotated online resource. Using functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator, we identified amino acid positions that best predict response to ligands. We examined the conservation of these predicted relevant residues in all OR subfamilies; the results showed that the subfamilies that expanded strongly in social insects had a high degree of conservation in their binding sites. This suggests that the ORs of social insect families are typically finely tuned and exhibit sensitivity to very similar odorants. Our novel approach provides a powerful tool to exploit functional information from a limited number of genes to study the functional evolution of large gene families.

Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors.

In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).