Toxoplasma membrane inositol phospholipid binding protein TgREMIND is essential for secretory organelle function and host infection.

Apicomplexan parasites possess specialized secretory organelles called rhoptries, micronemes, and dense granules that play a vital role in host infection. In this study, we demonstrate that TgREMIND, a protein found in Toxoplasma gondii, is necessary for the biogenesis of rhoptries and dense granules. TgREMIND contains a Fes-CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain, which binds to membrane phospholipids, as well as a novel uncharacterized domain that we have named REMIND (regulator of membrane-interacting domain). Both the F-BAR domain and the REMIND are crucial for TgREMIND functions. When TgREMIND is depleted, there is a significant decrease in the abundance of dense granules and abnormal transparency of rhoptries, leading to a reduction in protein secretion from these organelles. The absence of TgREMIND inhibits host invasion and parasite dissemination, demonstrating that TgREMIND is essential for the proper function of critical secretory organelles required for successful infection by Toxoplasma.

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.

A New Gene Family Diagnostic for Intracellular Biomineralization of Amorphous Ca Carbonates by Cyanobacteria.

Cyanobacteria have massively contributed to carbonate deposition over the geological history. They are traditionally thought to biomineralize CaCO3 extracellularly as an indirect byproduct of photosynthesis. However, the recent discovery of freshwater cyanobacteria-forming intracellular amorphous calcium carbonates (iACC) challenges this view. Despite the geochemical interest of such a biomineralization process, its molecular mechanisms and evolutionary history remain elusive. Here, using comparative genomics, we identify a new gene (ccyA) and protein family (calcyanin) possibly associated with cyanobacterial iACC biomineralization. Proteins of the calcyanin family are composed of a conserved C-terminal domain, which likely adopts an original fold, and a variable N-terminal domain whose structure allows differentiating four major types among the 35 known calcyanin homologs. Calcyanin lacks detectable full-length homologs with known function. The overexpression of ccyA in iACC-lacking cyanobacteria resulted in an increased intracellular Ca content. Moreover, ccyA presence was correlated and/or colocalized with genes involved in Ca or HCO3- transport and homeostasis, supporting the hypothesis of a functional role of calcyanin in iACC biomineralization. Whatever its function, ccyA appears as diagnostic of intracellular calcification in cyanobacteria. By searching for ccyA in publicly available genomes, we identified 13 additional cyanobacterial strains forming iACC, as confirmed by microscopy. This extends our knowledge about the phylogenetic and environmental distribution of cyanobacterial iACC biomineralization, especially with the detection of multicellular genera as well as a marine species. Moreover, ccyA was probably present in ancient cyanobacteria, with independent losses in various lineages that resulted in a broad but patchy distribution across modern cyanobacteria.

Navigating the amino acid sequence space between functional proteins using a deep learning framework.

MOTIVATION: Shedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these dependencies between sequences and functions, they can ultimately be used to generate new sequences and navigate through uncharted area of molecular evolution. RESULTS: This study presents an Adversarial Auto-Encoder (AAE) approached, an unsupervised generative model, to generate new protein sequences. AAEs are tested on three protein families known for their multiple functions the sulfatase, the HUP and the TPP families. Clustering results on the encoded sequences from the latent space computed by AAEs display high level of homogeneity regarding the protein sequence functions. The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential properties of original sequences linked to known functional properties issued from different families and functions. Generated sequences by interpolation between latent space data points demonstrate the ability of the AAE to generalize and produce meaningful biological sequences from an evolutionary uncharted area of the biological sequence space. Finally, 3D structure models computed by comparative modelling using generated sequences and templates of different sub-families point out to the ability of the latent space arithmetic to successfully transfer protein sequence properties linked to function between different sub-families. All in all this study confirms the ability of deep learning frameworks to model biological complexity and bring new tools to explore amino acid sequence and functional spaces.

A topology-based investigation of protein interaction sites using Hydrophobic Cluster Analysis.

Hydrophobic clusters, as defined by Hydrophobic Cluster Analysis (HCA), are conditioned binary patterns, made of hydrophobic and non-hydrophobic positions, whose limits fit well those of regular secondary structures. They were proved to be useful for predicting secondary structures in proteins from the only information of a single amino acid sequence and have permitted to assess, in a comprehensive way, the leading role of binary patterns in secondary structure preference towards a particular state. Here, we considered the available experimental 3D structures of protein globular domains to enlarge our previously reported hydrophobic cluster database (HCDB), almost doubling the number of hydrophobic cluster species (each species being defined by a unique binary pattern) that represent the most frequent structural bricks encountered within protein globular domains. We then used this updated HCDB to show that the hydrophobic amino acids of discordant clusters, i.e. those less abundant clusters for which the observed secondary structure is in disagreement with the binary pattern preference of the species to which they belong, are more exposed to solvent and are more involved in protein interfaces than the hydrophobic amino acids of concordant clusters. As amino acid composition differs between concordant/discordant clusters, considering binary patterns may be used to gain novel insights into key features of protein globular domain cores and surfaces. It can also provide useful information on possible conformational plasticity, including disorder to order transitions.

Discovery and Evolution of New Domains in Yeast Heterochromatin Factor Sir4 and Its Partner Esc1.

Sir4 is a core component of heterochromatin found in yeasts of the Saccharomycetaceae family, whose general hallmark is to harbor a three-loci mating-type system with two silent loci. However, a large part of the Sir4 amino acid sequences has remained unexplored, belonging to the dark proteome. Here, we analyzed the phylogenetic profile of yet undescribed foldable regions present in Sir4 as well as in Esc1, an Sir4-interacting perinuclear anchoring protein. Within Sir4, we identified a new conserved motif (TOC) adjacent to the N-terminal KU-binding motif. We also found that the Esc1-interacting region of Sir4 is a Dbf4-related H-BRCT domain, only present in species possessing the HO endonuclease and in Kluveryomyces lactis. In addition, we found new motifs within Esc1 including a motif (Esc1-F) that is unique to species where Sir4 possesses an H-BRCT domain. Mutagenesis of conserved amino acids of the Sir4 H-BRCT domain, known to play a critical role in the Dbf4 function, shows that the function of this domain is separable from the essential role of Sir4 in transcriptional silencing and the protection from HO-induced cutting in Saccharomyces cerevisiae. In the more distant methylotrophic clade of yeasts, which often harbor a two-loci mating-type system with one silent locus, we also found a yet undescribed H-BRCT domain in a distinct protein, the ISWI2 chromatin-remodeling factor subunit Itc1. This study provides new insights on yeast heterochromatin evolution and emphasizes the interest of using sensitive methods of sequence analysis for identifying hitherto ignored functional regions within the dark proteome.

"Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations.

Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95's third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify "communication pathways" and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as "infostery", from "info" - information - and "steric" - arrangement of residues in space. We provide a fully automated tool, COMMA2 ( www.lcqb.upmc.fr/COMMA2 ), that can be used to guide medicinal research by selecting important positions/mutations.

Order in Disorder as Observed by the "Hydrophobic Cluster Analysis" of Protein Sequences.

Hydrophobic cluster analysis (HCA) is an original approach for protein sequence analysis, which provides access to the foldable repertoire of the protein universe, including yet unannotated protein segments ("dark proteome"). Foldable segments correspond to ordered regions, as well as to intrinsically disordered regions (IDRs) undergoing disorder to order transitions. In this review, how HCA can be used to give insight into this last category of foldable segments is illustrated, with examples matching known 3D structures. After reviewing the HCA principles, examples of short foldable segments are given, which often contain short linear motifs, typically matching hydrophobic clusters. These segments become ordered upon contact with partners, with secondary structure preferences generally corresponding to those observed in the 3D structures within the complexes. Such small foldable segments are sometimes larger than the segments of known 3D structures, including flanking hydrophobic clusters that may be critical for interaction specificity or regulation, as well as intervening sequences allowing fuzziness. Cases of larger conditionally disordered domains are also presented, with lower density in hydrophobic clusters than well-folded globular domains or with exposed hydrophobic patches, which are stabilized by interaction with partners.

Origins and structural properties of novel and de novo protein domains during insect evolution.

Over long time scales, protein evolution is characterized by modular rearrangements of protein domains. Such rearrangements are mainly caused by gene duplication, fusion and terminal losses. To better understand domain emergence mechanisms we investigated 32 insect genomes covering a speciation gradient ranging from ~ 2 to ~ 390 mya. We use established domain models and foldable domains delineated by hydrophobic cluster analysis (HCA), which does not require homologous sequences, to also identify domains which have likely arisen de novo, that is, from previously noncoding DNA. Our results indicate that most novel domains emerge terminally as they originate from ORF extensions while fewer arise in middle arrangements, resulting from exonization of intronic or intergenic regions. Many novel domains rapidly migrate between terminal or middle positions and single- and multidomain arrangements. Young domains, such as most HCA-defined domains, are under strong selection pressure as they show signals of purifying selection. De novo domains, linked to ancient domains or defined by HCA, have higher degrees of intrinsic disorder and disorder-to-order transition upon binding than ancient domains. However, the corresponding DNA sequences of the novel domains of de novo origins could only rarely be found in sister genomes. We conclude that novel domains are often recruited by other proteins and undergo important structural modifications shortly after their emergence, but evolve too fast to be characterized by cross-species comparisons alone.

Hemimetabolous genomes reveal molecular basis of termite eusociality.

Around 150 million years ago, eusocial termites evolved from within the cockroaches, 50 million years before eusocial Hymenoptera, such as bees and ants, appeared. Here, we report the 2-Gb genome of the German cockroach, Blattella germanica, and the 1.3-Gb genome of the drywood termite Cryptotermes secundus. We show evolutionary signatures of termite eusociality by comparing the genomes and transcriptomes of three termites and the cockroach against the background of 16 other eusocial and non-eusocial insects. Dramatic adaptive changes in genes underlying the production and perception of pheromones confirm the importance of chemical communication in the termites. These are accompanied by major changes in gene regulation and the molecular evolution of caste determination. Many of these results parallel molecular mechanisms of eusocial evolution in Hymenoptera. However, the specific solutions are remarkably different, thus revealing a striking case of convergence in one of the major evolutionary transitions in biological complexity.

PhylOligo: a package to identify contaminant or untargeted organism sequences in genome assemblies.

MOTIVATION: Genome sequencing projects sometimes uncover more organisms than expected, especially for complex and/or non-model organisms. It is therefore useful to develop software to identify mix of organisms from genome sequence assemblies. RESULTS: Here we present PhylOligo, a new package including tools to explore, identify and extract organism-specific sequences in a genome assembly using the analysis of their DNA compositional characteristics. AVAILABILITY AND IMPLEMENTATION: The tools are written in Python3 and R under the GPLv3 Licence and can be found at https://github.com/itsmeludo/Phyloligo/. CONTACT: ludovic.mallet@inra.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring the dark foldable proteome by considering hydrophobic amino acids topology.

The protein universe corresponds to the set of all proteins found in all organisms. A way to explore it is by taking into account the domain content of the proteins. However, some part of sequences and many entire sequences remain un-annotated despite a converging number of domain families. The un-annotated part of the protein universe is referred to as the dark proteome and remains poorly characterized. In this study, we quantify the amount of foldable domains within the dark proteome by using the hydrophobic cluster analysis methodology. These un-annotated foldable domains were grouped using a combination of remote homology searches and domain annotations, leading to define different levels of darkness. The dark foldable domains were analyzed to understand what make them different from domains stored in databases and thus difficult to annotate. The un-annotated domains of the dark proteome universe display specific features relative to database domains: shorter length, non-canonical content and particular topology in hydrophobic residues, higher propensity for disorder, and a higher energy. These features make them hard to relate to known families. Based on these observations, we emphasize that domain annotation methodologies can still be improved to fully apprehend and decipher the molecular evolution of the protein universe.

Computational Identification of Novel Genes: Current and Future Perspectives.

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Domain similarity based orthology detection.

BACKGROUND: Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. RESULTS: We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. CONCLUSION: We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

Detection of orphan domains in Drosophila using "hydrophobic cluster analysis".

INTRODUCTION: Comparative genomics has become an important strategy in life science research. While many genes, and the proteins they code for, can be well characterized by assigning orthologs, a significant amount of proteins or domains remain obscure "orphans". Some orphans are overlooked by current computational methods because they rapidly diverged, others emerged relatively recently (de novo). Recent research has demonstrated the importance of orphans, and of de novo proteins and domains for development of new phenotypic traits and adaptation. New approaches for detecting novel domains are thus of paramount importance. RESULTS: The hydrophobic cluster analysis (HCA) method delineates globular-like domains from the information of a protein sequence and thereby allows bypassing some of the established methods limitations based on conserved sequence similarity. In this study, HCA is tested for orphan domain detection on 12 Drosophila genomes. After their detection, the oprhan domains are classified into two categories, depending on their presence/absence in distantly related species. The two categories show significantly different physico-chemical properties when compared to previously characterized domains from the Pfam database. The newly detected domains have a higher degree of intrinsic disorder and a particular hydrophobic cluster composition. The older the domains are, the more similar their hydrophobic cluster content is to the cluster content of Pfam domains. The results suggest that, over time, newly created domains acquire a canonical set of hydrophobic clusters but conserve some features of intrinsically disordered regions. CONCLUSION: Our results agree with previous findings on orphan domains and suggest that the physico-chemical properties of domains change over evolutionary long time scale. The presented HCA-based method is able to detect domains with unusual properties without relying on prior knowledge, such as the availability of homologs. Therefore, the method has large potential for complementing existing strategies to annotate genomes, and for better understanding how molecular features emerge.

MDAT- Aligning multiple domain arrangements.

BACKGROUND: Proteins are composed of domains, protein segments that fold independently from the rest of the protein and have a specific function. During evolution the arrangement of domains can change: domains are gained, lost or their order is rearranged. To facilitate the analysis of these changes we propose the use of multiple domain alignments. RESULTS: We developed an alignment program, called MDAT, which aligns multiple domain arrangements. MDAT extends earlier programs which perform pairwise alignments of domain arrangements. MDAT uses a domain similarity matrix to score domain pairs and aligns the domain arrangements using a consistency supported progressive alignment method. CONCLUSION: MDAT will be useful for analysing changes in domain arrangements within and between protein families and will thus provide valuable insights into the evolution of proteins and their domains. MDAT is coded in C++, and the source code is freely available for download at http://www.bornberglab.org/pages/mdat .