The Ancient Gamete Fusogen HAP2 Is a Eukaryotic Class II Fusion Protein.

Sexual reproduction is almost universal in eukaryotic life and involves the fusion of male and female haploid gametes into a diploid cell. The sperm-restricted single-pass transmembrane protein HAP2-GCS1 has been postulated to function in membrane merger. Its presence in the major eukaryotic taxa-animals, plants, and protists (including important human pathogens like Plasmodium)-suggests that many eukaryotic organisms share a common gamete fusion mechanism. Here, we report combined bioinformatic, biochemical, mutational, and X-ray crystallographic studies on the unicellular alga Chlamydomonas reinhardtii HAP2 that reveal homology to class II viral membrane fusion proteins. We further show that targeting the segment corresponding to the fusion loop by mutagenesis or by antibodies blocks gamete fusion. These results demonstrate that HAP2 is the gamete fusogen and suggest a mechanism of action akin to viral fusion, indicating a way to block Plasmodium transmission and highlighting the impact of virus-cell genetic exchanges on the evolution of eukaryotic life.

Recent advances in predicting and modeling protein-protein interactions.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Cell-free formation of RNA granules: bound RNAs identify features and components of cellular assemblies.

Cellular granules lacking boundary membranes harbor RNAs and their associated proteins and play diverse roles controlling the timing and location of protein synthesis. Formation of such granules was emulated by treatment of mouse brain extracts and human cell lysates with a biotinylated isoxazole (b-isox) chemical. Deep sequencing of the associated RNAs revealed an enrichment for mRNAs known to be recruited to neuronal granules used for dendritic transport and localized translation at synapses. Precipitated mRNAs contain extended 3' UTR sequences and an enrichment in binding sites for known granule-associated proteins. Hydrogels composed of the low complexity (LC) sequence domain of FUS recruited and retained the same mRNAs as were selectively precipitated by the b-isox chemical. Phosphorylation of the LC domain of FUS prevented hydrogel retention, offering a conceptual means of dynamic, signal-dependent control of RNA granule assembly.

Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels.

Eukaryotic cells contain assemblies of RNAs and proteins termed RNA granules. Many proteins within these bodies contain KH or RRM RNA-binding domains as well as low complexity (LC) sequences of unknown function. We discovered that exposure of cell or tissue lysates to a biotinylated isoxazole (b-isox) chemical precipitated hundreds of RNA-binding proteins with significant overlap to the constituents of RNA granules. The LC sequences within these proteins are both necessary and sufficient for b-isox-mediated aggregation, and these domains can undergo a concentration-dependent phase transition to a hydrogel-like state in the absence of the chemical. X-ray diffraction and EM studies revealed the hydrogels to be composed of uniformly polymerized amyloid-like fibers. Unlike pathogenic fibers, the LC sequence-based polymers described here are dynamic and accommodate heterotypic polymerization. These observations offer a framework for understanding the function of LC sequences as well as an organizing principle for cellular structures that are not membrane bound.

Classification of domains in predicted structures of the human proteome.

Recent advances in protein structure prediction have generated accurate structures of previously uncharacterized human proteins. Identifying domains in these predicted structures and classifying them into an evolutionary hierarchy can reveal biological insights. Here, we describe the detection and classification of domains from the human proteome. Our classification indicates that only 62% of residues are located in globular domains. We further classify these globular domains and observe that the majority (65%) can be classified among known folds by sequence, with a smaller fraction (33%) requiring structural data to refine the domain boundaries and/or to support their homology. A relatively small number (966 domains) cannot be confidently assigned using our automatic pipelines, thus demanding manual inspection. We classify 47,576 domains, of which only 23% have been included in experimental structures. A portion (6.3%) of these classified globular domains lack sequence-based annotation in InterPro. A quarter (23%) have not been structurally modeled by homology, and they contain 2,540 known disease-causing single amino acid variations whose pathogenesis can now be inferred using AF models. A comparison of classified domains from a series of model organisms revealed expansions of several immune response-related domains in humans and a depletion of olfactory receptors. Finally, we use this classification to expand well-known protein families of biological significance. These classifications are presented on the ECOD website (http://prodata.swmed.edu/ecod/index_human.php).

Computational analysis of propeptide-containing proteins and prediction of their post-cleavage conformation changes.

A propeptide is removed from a precursor protein to generate its active or mature form. Propeptides play essential roles in protein folding, transportation, and activation and are present in about 2.3% of reviewed proteins in the UniProt database. They are often found in secreted or membrane-bound proteins including proteolytic enzymes, hormones, and toxins. We identified a variety of globular and nonglobular Pfam domains in protein sequences designated as propeptides, some of which form intramolecular interactions with other domains in the mature proteins. Propeptide-containing enzymes mostly function as proteases, as they are depleted in other enzyme classes such as hydrolases acting on DNA and RNA, isomerases, and lyases. We applied AlphaFold to generate structural models for over 7000 proteins with propeptides having no less than 20 residues. Analysis of residue contacts in these models revealed conformational changes for over 300 proteins before and after the cleavage of the propeptide. Examples of conformation change occur in several classes of proteolytic enzymes in the families of subtilisins, trypsins, aspartyl proteases, and thermolysin-like metalloproteases. In most of the observed cases, cleavage of the propeptide releases the constraints imposed by the covalent bond between the propeptide and the mature protein, and cleavage enables stronger interactions between the propeptide and the mature protein. These findings suggest that post-cleavage propeptides could play critical roles in regulating the activity of mature proteins.

Impact of Asp/Glu-ADP-ribosylation on protein-protein interaction and protein function.

PARylation plays critical role in regulating multiple cellular processes such as DNA damage response and repair, transcription, RNA processing, and stress response. More than 300 human proteins have been found to be modified by PARylation on acidic residues, that is, Asp (D) and Glu (E). We used the deep-learning tool AlphaFold to predict protein-protein interactions (PPIs) and their interfaces for these proteins based on coevolution signals from joint multiple sequence alignments (MSAs). AlphaFold predicted 260 confident PPIs involving PARylated proteins, and about one quarter of these PPIs have D/E-PARylation sites in their predicted PPI interfaces. AlphaFold predictions offer novel insights into the mechanisms of PARylation regulations by providing structural details of the PPI interfaces. D/E-PARylation sites have a preference to occur in coil regions and disordered regions, and PPI interfaces containing D/E-PARylation sites tend to occur between short linear sequence motifs in disordered regions and globular domains. The hub protein PCNA is predicted to interact with more than 20 proteins via the common PIP box motif and the structurally variable flanking regions. D/E-PARylation sites were found in the interfaces of key components of the RNA transcription and export complex, the SF3a spliceosome complex, and H/ACA and C/D small nucleolar ribonucleoprotein complexes, suggesting that systematic PARylation have a profound effect in regulating multiple RNA-related processes such as RNA nuclear export, splicing, and modification. Finally, PARylation of SUMO2 could modulate its interaction with CHAF1A, thereby representing a potential mechanism for the cross-talk between PARylation and SUMOylation in regulation of chromatin remodeling.

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

MOTIVATION: Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. RESULTS: We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. AVAILABILITY AND IMPLEMENTATION: Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DisEnrich: database of enriched regions in human dark proteome.

MOTIVATION: Intrinsically disordered proteins (IDPs) are involved in numerous processes crucial for living organisms. Bias in amino acid composition of these proteins determines their unique biophysical and functional features. Distinct intrinsically disordered regions (IDRs) with compositional bias play different important roles in various biological processes. IDRs enriched in particular amino acids in human proteome have not been described consistently. RESULTS: We developed DisEnrich-the database of human proteome IDRs that are significantly enriched in particular amino acids. Each human protein is described using Gene Ontology (GO) function terms, disorder prediction for the full-length sequence using three methods, enriched IDR composition and ranks of human proteins with similar enriched IDRs. Distribution analysis of enriched IDRs among broad functional categories revealed significant overrepresentation of R- and Y-enriched IDRs in metabolic and enzymatic activities and F-enriched IDRs in transport. About 75% of functional categories contain IDPs with IDRs significantly enriched in hydrophobic residues that are important for protein-protein interactions. AVAILABILITY AND IMPLEMENTATION: The database is available at http://prodata.swmed.edu/DisEnrichDB/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.

Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14).

This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.

pCRM1exportome: database of predicted CRM1-dependent Nuclear Export Signal (NES) motifs in cancer-related genes.

MOTIVATION: The consensus pattern of Nuclear Export Signal (NES) is a short sequence motif that is commonly identified in protein sequences, whether the motif acts as an NES (true positive) or not (false positive). Finding more plausible NES functioning regions among the vast array of consensus-matching segments would provide an interesting resource for further experimental validation. Better defined NES should also allow meaningful mapping of cancer-related mutation positions, leading to plausible explanations for the relationship between nuclear export and disease. RESULTS: Possible NES candidate regions are extracted from the cancer-related human reference proteome. Extracted NES are scored for reliability by combining sequence-based and structure-based approaches. The confidently identified NES candidate motifs were checked for overlap with cancer-related mutation positions annotated in the COSMIC database. Among the ∼700 cancer-related sequences in the COSMIC Cancer Gene Census, 178 sequences are predicted to have possible NES motifs containing cancer-related mutations at their key positions. These lists are organized into our database (pCRM1exportome), and other protein sequences in the human reference proteome can also be retrieved by their UniProt IDs. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://prodata.swmed.edu/pCRM1exportome. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pathogenic UFSP2 variant in an autosomal recessive form of pediatric neurodevelopmental anomalies and epilepsy.

PURPOSE: Neurodevelopmental disabilities are common and genetically heterogeneous. We identified a homozygous variant in the gene encoding UFM1-specific peptidase 2 (UFSP2), which participates in the UFMylation pathway of protein modification. UFSP2 variants are implicated in autosomal dominant skeletal dysplasias, but not neurodevelopmental disorders. Homozygosity for the variant occurred in eight children from four South Asian families with neurodevelopmental delay and epilepsy. We describe the clinical consequences of this variant and its effect on UFMylation. METHODS: Exome sequencing was used to detect potentially pathogenic variants and identify shared regions of homozygosity. Immunoblotting assessed protein expression and post-translational modifications in patient-derived fibroblasts. RESULTS: The variant (c.344T>A; p.V115E) is rare and alters a conserved residue in UFSP2. Immunoblotting in patient-derived fibroblasts revealed reduced UFSP2 abundance and increased abundance of UFMylated targets, indicating the variant may impair de-UFMylation rather than UFMylation. Reconstituting patient-derived fibroblasts with wild-type UFSP2 reduced UFMylation marks. Analysis of UFSP2's structure indicated that variants observed in skeletal disorders localize to the catalytic domain, whereas V115 resides in an N-terminal domain possibly involved in substrate binding. CONCLUSION: Different UFSP2 variants cause markedly different diseases, with homozygosity for V115E causing a severe syndrome of neurodevelopmental disability and epilepsy.

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type.

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

Metabolic impact of pathogenic variants in the mitochondrial glutamyl-tRNA synthetase EARS2.

Glutamyl-tRNA synthetase 2 (encoded by EARS2) is a mitochondrial aminoacyl-tRNA synthetase required to translate the 13 subunits of the electron transport chain encoded by the mitochondrial DNA. Pathogenic EARS2 variants cause combined oxidative phosphorylation deficiency, subtype 12 (COXPD12), an autosomal recessive disorder involving lactic acidosis, intellectual disability, and other features of mitochondrial compromise. Patients with EARS2 deficiency present with variable phenotypes ranging from neonatal lethality to a mitigated disease with clinical improvement in early childhood. Here, we report a neonate homozygous for a rare pathogenic variant in EARS2 (c.949G>T; p.G317C). Metabolomics in primary fibroblasts from this patient revealed expected abnormalities in TCA cycle metabolites, as well as numerous changes in purine, pyrimidine, and fatty acid metabolism. To examine genotype-phenotype correlations in COXPD12, we compared the metabolic impact of reconstituting these fibroblasts with wild-type EARS2 versus four additional EARS2 variants from COXPD12 patients with varying clinical severity. Metabolomics identified a group of signature metabolites, mostly from the TCA cycle and amino acid metabolism, that discriminate between EARS2 variants causing relatively mild and severe COXPD12. Taken together, these findings indicate that metabolomics in patient-derived fibroblasts may help establish genotype-phenotype correlations in EARS2 deficiency and likely other mitochondrial disorders.

Comparative genomics in Chlamydomonas and Plasmodium identifies an ancient nuclear envelope protein family essential for sexual reproduction in protists, fungi, plants, and vertebrates.

Fertilization is a crucial yet poorly characterized event in eukaryotes. Our previous discovery that the broadly conserved protein HAP2 (GCS1) functioned in gamete membrane fusion in the unicellular green alga Chlamydomonas and the malaria pathogen Plasmodium led us to exploit the rare biological phenomenon of isogamy in Chlamydomonas in a comparative transcriptomics strategy to uncover additional conserved sexual reproduction genes. All previously identified Chlamydomonas fertilization-essential genes fell into related clusters based on their expression patterns. Out of several conserved genes in a minus gamete cluster, we focused on Cre06.g280600, an ortholog of the fertilization-related Arabidopsis GEX1. Gene disruption, cell biological, and immunolocalization studies show that CrGEX1 functions in nuclear fusion in Chlamydomonas. Moreover, CrGEX1 and its Plasmodium ortholog, PBANKA_113980, are essential for production of viable meiotic progeny in both organisms and thus for mosquito transmission of malaria. Remarkably, we discovered that the genes are members of a large, previously unrecognized family whose first-characterized member, KAR5, is essential for nuclear fusion during yeast sexual reproduction. Our comparative transcriptomics approach provides a new resource for studying sexual development and demonstrates that exploiting the data can lead to the discovery of novel biology that is conserved across distant taxa.

Requirement of the fusogenic micropeptide myomixer for muscle formation in zebrafish.

Skeletal muscle formation requires fusion of mononucleated myoblasts to form multinucleated myofibers. The muscle-specific membrane proteins myomaker and myomixer cooperate to drive mammalian myoblast fusion. Whereas myomaker is highly conserved across diverse vertebrate species, myomixer is a micropeptide that shows relatively weak cross-species conservation. To explore the functional conservation of myomixer, we investigated the expression and function of the zebrafish myomixer ortholog. Here we show that myomixer expression during zebrafish embryogenesis coincides with myoblast fusion, and genetic deletion of myomixer using CRISPR/Cas9 mutagenesis abolishes myoblast fusion in vivo. We also identify myomixer orthologs in other species of fish and reptiles, which can cooperate with myomaker and substitute for the fusogenic activity of mammalian myomixer. Sequence comparison of these diverse myomixer orthologs reveals key amino acid residues and a minimal fusogenic peptide motif that is necessary for promoting cell-cell fusion with myomaker. Our findings highlight the evolutionary conservation of the myomaker-myomixer partnership and provide insights into the molecular basis of myoblast fusion.

A sequence family database built on ECOD structural domains.

Motivation: The ECOD database classifies protein domains based on their evolutionary relationships, considering both remote and close homology. The family group in ECOD provides classification of domains that are closely related to each other based on sequence similarity. Due to different perspectives on domain definition, direct application of existing sequence domain databases, such as Pfam, to ECOD struggles with several shortcomings. Results: We created multiple sequence alignments and profiles from ECOD domains with the help of structural information in alignment building and boundary delineation. We validated the alignment quality by scoring structure superposition to demonstrate that they are comparable to curated seed alignments in Pfam. Comparison to Pfam and CDD reveals that 27 and 16% of ECOD families are new, but they are also dominated by small families, likely because of the sampling bias from the PDB database. There are 35 and 48% of families whose boundaries are modified comparing to counterparts in Pfam and CDD, respectively. Availability and implementation: The new families are now integrated in the ECOD website. The aggregate HMMER profile library and alignment are available for download on ECOD website (http://prodata.swmed.edu/ecod). Supplementary information: Supplementary data are available at Bioinformatics online.

The cytoplasmic domain of the gamete membrane fusion protein HAP2 targets the protein to the fusion site in Chlamydomonas and regulates the fusion reaction.

Cell-cell fusion between gametes is a defining step during development of eukaryotes, yet we know little about the cellular and molecular mechanisms of the gamete membrane fusion reaction. HAP2 is the sole gamete-specific protein in any system that is broadly conserved and shown by gene disruption to be essential for gamete fusion. The wide evolutionary distribution of HAP2 (also known as GCS1) indicates it was present in the last eukaryotic common ancestor and, therefore, dissecting its molecular properties should provide new insights into fundamental features of fertilization. HAP2 acts at a step after membrane adhesion, presumably directly in the merger of the lipid bilayers. Here, we use the unicellular alga Chlamydomonas to characterize contributions of key regions of HAP2 to protein location and function. We report that mutation of three strongly conserved residues in the ectodomain has no effect on targeting or fusion, although short deletions that include those residues block surface expression and fusion. Furthermore, HAP2 lacking a 237-residue segment of the cytoplasmic region is expressed at the cell surface, but fails to localize at the apical membrane patch specialized for fusion and fails to rescue fusion. Finally, we provide evidence that the ancient HAP2 contained a juxta-membrane, multi-cysteine motif in its cytoplasmic region, and that mutation of a cysteine dyad in this motif preserves protein localization, but substantially impairs HAP2 fusion activity. Thus, the ectodomain of HAP2 is essential for its surface expression, and the cytoplasmic region targets HAP2 to the site of fusion and regulates the fusion reaction.

Using homology relations within a database markedly boosts protein sequence similarity search.

Inference of homology from protein sequences provides an essential tool for analyzing protein structure, function, and evolution. Current sequence-based homology search methods are still unable to detect many similarities evident from protein spatial structures. In computer science a search engine can be improved by considering networks of known relationships within the search database. Here, we apply this idea to protein-sequence-based homology search and show that it dramatically enhances the search accuracy. Our new method, COMPADRE (COmparison of Multiple Protein sequence Alignments using Database RElationships) assesses the relationship between the query sequence and a hit in the database by considering the similarity between the query and hit's known homologs. This approach increases detection quality, boosting the precision rate from 18% to 83% at half-coverage of all database homologs. The increased precision rate allows detection of a large fraction of protein structural relationships, thus providing structure and function predictions for previously uncharacterized proteins. Our results suggest that this general approach is applicable to a wide variety of methods for detection of biological similarities. The web server is available at prodata.swmed.edu/compadre.