Prediction of protein disorder based on IUPred

Genome-scale exon perturbation screens uncover exons critical for cell fitness

CRISPR-Cas technology has transformed functional genomics, yet understanding of how individual exons differentially shape cellular phenotypes remains limited. Here, we optimized and conducted massively parallel exon deletion and splice-site mutation screens in human cell lines to identify exons that regulate cellular fitness. Fitness-promoting exons are prevalent in essential and highly expressed genes and commonly overlap with protein domains and interaction interfaces. Conversely, fitness-suppressing exons are enriched in nonessential genes, exhibiting lower inclusion levels, and overlap with intrinsically disordered regions and disease-associated mutations. In-depth mechanistic investigation of the screen-hit TAF5 alternative exon-8 revealed that its inclusion is required for assembly of the TFIID general transcription initiation complex, thereby regulating global gene expression output. Collectively, our orthogonal exon perturbation screens established a comprehensive repository of phenotypically important exons and uncovered regulatory mechanisms governing cellular fitness and gene expression.

Are Most Human-Specific Proteins Encoded by Long Noncoding RNAs?

By looking for a lack of homologs in a reference database of 27 well-annotated proteomes of primates and 52 well-annotated proteomes of other mammals, 170 putative human-specific proteins were identified. While most of them are deemed uncertain, 2 are known at the protein level and 23 at the transcript level, according to UniProt. Interestingly, 23 of these 25 proteins are found to be encoded or to have close homologs in an open reading frame of a long noncoding human RNA. However, half of them are predicted to be at least 80% globular, with a single structural domain, according to IUPred, and with at least 80% of ordered residues, according to flDPnn. Strikingly, there is a near-complete lack of structural knowledge about these proteins, with no tertiary structure presently available in the Protein Data Bank and a fair prediction for one of them in the AlphaFold Protein Structure Database. Moreover, knowledge about the function of these possibly key proteins remains scarce.

Navigating centriolar satellites: the role of PCM1 in cellular and organismal processes

Centriolar satellites are ubiquitous membrane-less organelles that play critical roles in numerous cellular and organismal processes. They were initially discovered through electron microscopy as cytoplasmic granules surrounding centrosomes in vertebrate cells. These structures remained enigmatic until the identification of pericentriolar material 1 protein (PCM1) as their molecular marker, which has enabled their in-depth characterization. Recently, centriolar satellites have come into the spotlight due to their links to developmental and neurodegenerative disorders. This review presents a comprehensive summary of the major advances in centriolar satellite biology, with a focus on studies that investigated their biology associated with the essential scaffolding protein PCM1. We begin by exploring the molecular, cellular, and biochemical properties of centriolar satellites, laying the groundwork for a deeper understanding of their functions and mechanisms at both cellular and organismal levels. We then examine the implications of their dysregulation in various diseases, particularly highlighting their emerging roles in neurodegenerative and developmental disorders, as revealed by organismal models of PCM1. We conclude by discussing the current state of knowledge and posing questions about the adaptable nature of these organelles, thereby setting the stage for future research.

Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms

Alternative splicing events are a major causal mechanism for complex traits, but they have been understudied due to the limitation of short-read sequencing. Here, we generate a full-length isoform annotation of human immune cells from an individual by long-read sequencing for 29 cell subsets. This contains a number of unannotated transcripts and isoforms such as a read-through transcript of TOMM40-APOE in the Alzheimer's disease locus. We profile characteristics of isoforms and show that repetitive elements significantly explain the diversity of unannotated isoforms, providing insight into the human genome evolution. In addition, some of the isoforms are expressed in a cell-type specific manner, whose alternative 3'-UTRs usage contributes to their specificity. Further, we identify disease-associated isoforms by isoform switch analysis and by integration of several quantitative trait loci analyses with genome-wide association study data. Our findings will promote the elucidation of the mechanism of complex diseases via alternative splicing.

eIF4F is a thermo-sensing regulatory node in the translational heat shock response

Heat-shocked cells prioritize the translation of heat shock (HS) mRNAs, but the underlying mechanism is unclear. We report that HS in budding yeast induces the disassembly of the eIF4F complex, where eIF4G and eIF4E assemble into translationally arrested mRNA ribonucleoprotein particles (mRNPs) and HS granules (HSGs), whereas eIF4A promotes HS translation. Using in vitro reconstitution biochemistry, we show that a conformational rearrangement of the thermo-sensing eIF4A-binding domain of eIF4G dissociates eIF4A and promotes the assembly with mRNA into HS-mRNPs, which recruit additional translation factors, including Pab1p and eIF4E, to form multi-component condensates. Using extracts and cellular experiments, we demonstrate that HS-mRNPs and condensates repress the translation of associated mRNA and deplete translation factors that are required for housekeeping translation, whereas HS mRNAs can be efficiently translated by eIF4A. We conclude that the eIF4F complex is a thermo-sensing node that regulates translation during HS.

Rrm3 and Pif1 division of labor during replication through leading and lagging strand G-quadruplex

Members of the conserved Pif1 family of 5'-3' DNA helicases can unwind G4s and mitigate their negative impact on genome stability. In Saccharomyces cerevisiae, two Pif1 family members, Pif1 and Rrm3, contribute to the suppression of genomic instability at diverse regions including telomeres, centromeres and tRNA genes. While Pif1 can resolve lagging strand G4s in vivo, little is known regarding Rrm3 function at G4s and its cooperation with Pif1 for G4 replication. Here, we monitored replication through G4 sequences in real time to show that Rrm3 is essential for efficient replisome progression through G4s located on the leading strand template, but not on the lagging strand. We found that Rrm3 importance for replication through G4s is dependent on its catalytic activity and its N-terminal unstructured region. Overall, we show that Rrm3 and Pif1 exhibit a division of labor that enables robust replication fork progression through leading and lagging strand G4s, respectively.

Properties of biomolecular condensates defined by Activator of G-protein Signaling 3

Activator of G-protein signaling 3 (AGS3; also known as GPSM1), a receptor-independent activator of G-protein signaling, oscillates among defined subcellular compartments and biomolecular condensates (BMCs) in a regulated manner that is likely related to the functional diversity of the protein. We determined the influence of cell stress on the cellular distribution of AGS3 and core material properties of AGS3 BMCs. Cellular stress (oxidative, pHi and thermal) induced the formation of AGS3 BMCs in HeLa and COS-7 cells, as determined by fluorescent microscopy. Oxidative stress-induced AGS3 BMCs were distinct from G3BP1 stress granules and from RNA processing BMCs defined by the P-body protein Dcp1a. Immunoblots indicated that cellular stress shifted AGS3, but not the stress granule protein G3BP1 to a membrane pellet fraction following cell lysis. The stress-induced generation of AGS3 BMCs was reduced by co-expression of the signaling protein Gαi3, but not the AGS3-binding partner DVL2. Fluorescent recovery following photobleaching of individual AGS3 BMCs indicated that there are distinct diffusion kinetics and restricted fluidity for AGS3 BMCs. These data suggest that AGS3 BMCs represent a distinct class of stress granules that serve as a previously unrecognized signal processing node.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

Hypomorphic variants of SEL1L-HRD1 ER-associated degradation are associated with neurodevelopmental disorders

Recent studies using cell type-specific knockout mouse models have improved our understanding of the pathophysiological relevance of suppressor of lin-12-like-HMG-CoA reductase degradation 1 (SEL1L-HRD1) endoplasmic reticulum-associated (ER-associated) degradation (ERAD); however, its importance in humans remains unclear, as no disease variant has been identified. Here, we report the identification of 3 biallelic missense variants of SEL1L and HRD1 (or SYVN1) in 6 children from 3 independent families presenting with developmental delay, intellectual disability, microcephaly, facial dysmorphisms, hypotonia, and/or ataxia. These SEL1L (p.Gly585Asp, p.Met528Arg) and HRD1 (p.Pro398Leu) variants were hypomorphic and impaired ERAD function at distinct steps of ERAD, including substrate recruitment (SEL1L p.Gly585Asp), SEL1L-HRD1 complex formation (SEL1L p.Met528Arg), and HRD1 activity (HRD1 p.Pro398Leu). Our study not only provides insights into the structure-function relationship of SEL1L-HRD1 ERAD, but also establishes the importance of SEL1L-HRD1 ERAD in humans.

Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .

Prediction of interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins from dihedral angles

Intrinsically disordered proteins (IDPs) are proteins that do not form uniquely defined three-dimensional (3-D) structures. Experimental research on IDPs is difficult since they go against the traditional protein structure-function paradigm. Although there are several predictors of disorder based on amino acid sequences, but very limited based on the 3-D structures of proteins. Dihedral angles have a significant role in predicting protein structure because they establish a protein's backbone, which, coupled with its side chain, establishes its overall shape. Here, we have carried out atomistic Molecular Dynamics (MD) simulations on four different proteins: one ordered protein (Monellin), two partially disordered proteins (p53-TAD and Amyloid beta (Aβ1-42) peptide), and one completely disordered protein (Histatin 5). The MD simulation trajectories for the corresponding four proteins were used to conduct dihedral angle (ϕ and ѱ) analysis. Then, the average dihedral angles for each of the residues were calculated and plotted against the residue index. We noticed steep rises or falls in the average ϕ value at certain locations in the plot. These sudden shifts in the average ϕ value reflect the interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins. Using this method, the probable conformer of a protein with a higher degree of disorder can be found among the ensembles of structures sampled during the MD simulations. The results of our study offer new understandings on precisely identifying regions of various degrees of disorder in intrinsically disordered proteins.Communicated by Ramaswamy H. Sarma.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

Bioinformatic analysis of THAP9 transposase homolog: conserved regions, novel motifs

THAP9 is a transposable element-derived gene that encodes the THAP9 protein, which is homologous to the Drosophila P-element transposase (DmTNP) and can cut and paste DNA. However, the exact functional role of THAP9 is unknown. Here, we perform structure prediction, evolutionary analysis and extensive in silico characterization of THAP9, including predicting domains and putative post-translational modification sites. Comparison of the AlphaFold-predicted structure of THAP9 with the DmTNP CryoEM structure, provided insights about the C2CH motif and other DNA binding residues, RNase H-like catalytic domain and insertion domain of the THAP9 protein. We also predicted previously unreported mammalian-specific post-translational modification sites that may play a role in the subcellular localization of THAP9. Furthermore, we observed that there are distinct organism class-specific conservation patterns of key functional residues in certain THAP9 domains.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

The intrinsically disordered region of eIF5B stimulates IRES usage and nucleates biological granule formation

Cells activate stress response pathways to survive adverse conditions. Such responses involve the inhibition of global cap-dependent translation. This inhibition is a block that essential transcripts must escape via alternative methods of translation initiation, e.g., an internal ribosome entry site (IRES). IRESs have distinct structures and generally require a limited repertoire of translation factors. Cellular IRESs have been identified in many critical cellular stress response transcripts. We previously identified cellular IRESs in the murine insulin receptor (Insr) and insulin-like growth factor 1 receptor (Igf1r) transcripts and demonstrated their resistance to eukaryotic initiation factor 4F (eIF4F) inhibition. Here, we find that eIF5B preferentially promotes Insr, Igf1r, and hepatitis C virus IRES activity through a non-canonical mechanism that requires its highly charged and disordered N terminus. We find that the N-terminal region of eIF5B can drive cytoplasmic granule formation. This eIF5B granule is triggered by cellular stress and is sufficient to specifically promote IRES activity.

Evolution of Intrinsic Disorder in Protein Loops

Intrinsic disorder accounts for the flexibility of protein loops, molecular building blocks that are largely responsible for the processes and molecular functions of the living world. While loops likely represent early structural forms that served as intermediates in the emergence of protein structural domains, their origin and evolution remain poorly understood. Here, we conduct a phylogenomic survey of disorder in loop prototypes sourced from the ArchDB classification. Tracing prototypes associated with protein fold families along an evolutionary chronology revealed that ancient prototypes tended to be more disordered than their derived counterparts, with ordered prototypes developing later in evolution. This highlights the central evolutionary role of disorder and flexibility. While mean disorder increased with time, a minority of ordered prototypes exist that emerged early in evolutionary history, possibly driven by the need to preserve specific molecular functions. We also revealed the percolation of evolutionary constraints from higher to lower levels of organization. Percolation resulted in trade-offs between flexibility and rigidity that impacted prototype structure and geometry. Our findings provide a deep evolutionary view of the link between structure, disorder, flexibility, and function, as well as insights into the evolutionary role of intrinsic disorder in loops and their contribution to protein structure and function.

SAFB associates with nascent RNAs and can promote gene expression in mouse embryonic stem cells

Scaffold attachment factor B (SAFB) is a conserved RNA-binding protein that is essential for early mammalian development. However, the functions of SAFB in mouse embryonic stem cells (ESCs) have not been characterized. Using RNA immunoprecipitation followed by RNA-seq (RIP-seq), we examined the RNAs associated with SAFB in wild-type and SAFB/SAFB2 double-knockout ESCs. SAFB predominantly associated with introns of protein-coding genes through purine-rich motifs. The transcript most enriched in SAFB association was the lncRNA Malat1, which also contains a purine-rich region in its 5' end. Knockout of SAFB/SAFB2 led to differential expression of approximately 1000 genes associated with multiple biological processes, including apoptosis, cell division, and cell migration. Knockout of SAFB/SAFB2 also led to splicing changes in a set of genes that were largely distinct from those that exhibited changes in expression level. The spliced and nascent transcripts of many genes whose expression levels were positively regulated by SAFB also associated with high levels of SAFB, implying that SAFB binding promotes their expression. Reintroduction of SAFB into double-knockout cells restored gene expression toward wild-type levels, an effect again observable at the level of spliced and nascent transcripts. Proteomics analysis revealed a significant enrichment of nuclear speckle-associated and RS domain-containing proteins among SAFB interactors. Neither Xist nor Polycomb functions were dramatically altered in SAFB/2 knockout ESCs. Our findings suggest that among other potential functions in ESCs, SAFB promotes the expression of certain genes through its ability to bind nascent RNA.

A de novo evolved gene contributes to rice grain shape difference between indica and japonica

The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape.

CASC3 Biomolecular Condensates Restrict Turnip Crinkle Virus by Limiting Host Factor Availability

The exon-junction complex (EJC) plays a role in post-transcriptional gene regulation and exerts antiviral activity towards several positive-strand RNA viruses. However, the spectrum of RNA viruses that are targeted by the EJC or the underlying mechanisms are not well understood. EJC components from Arabidopsis thaliana were screened for antiviral activity towards Turnip crinkle virus (TCV, Tombusviridae). Overexpression of the accessory EJC component CASC3 inhibited TCV accumulation > 10-fold in Nicotiana benthamiana while knock-down of endogenous CASC3 resulted in a > 4-fold increase in TCV accumulation. CASC3 forms cytoplasmic condensates and deletion of the conserved SELOR domain reduced condensate size 7-fold and significantly decreased antiviral activity towards TCV. Mass spectrometry of CASC3 complexes did not identify endogenous stress granule or P-body markers and CASC3 failed to co-localize with an aggresome-specific dye suggesting that CASC3 condensates are distinct from well-established membraneless compartments. Mass spectrometry and bimolecular fluorescence complementation assays revealed that CASC3 sequesters Heat shock protein 70 (Hsp70-1) and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), two host factors with roles in tombusvirus replication. Overexpression of Hsp70-1 or GAPDH reduced the antiviral activity of CASC3 2.1-fold and 2.8-fold, respectively, and suggests that CASC3 inhibits TCV by limiting host factor availability. Unrelated Tobacco mosaic virus (TMV) also depends on Hsp70-1 and CASC3 overexpression restricted TMV accumulation 4-fold and demonstrates that CASC3 antiviral activity is not TCV-specific. Like CASC3, Auxin response factor 19 (ARF19) forms poorly dynamic condensates but ARF19 overexpression failed to inhibit TCV accumulation and suggests that CASC3 has antiviral activities that are not ubiquitous among cytoplasmic condensates.

N-terminal intrinsic disorder is an ancestral feature of Gγ subunits that influences the balance between different Gβγ signaling axes in yeast

Activated G protein-coupled receptors promote the dissociation of heterotrimeric G proteins into Gα and Gβγ subunits that bind to effector proteins to drive intracellular signaling responses. In yeast, Gβγ subunits coordinate the simultaneous activation of multiple signaling axes in response to mating pheromones, including MAP kinase (MAPK)-dependent transcription, cell polarization, and cell cycle arrest responses. The Gγ subunit in this complex contains an N-terminal intrinsically disordered region that governs Gβγ-dependent signal transduction in yeast and mammals. Here, we demonstrate that N-terminal intrinsic disorder is likely an ancestral feature that has been conserved across different Gγ subtypes and organisms. To understand the functional contribution of structural disorder in this region, we introduced precise point mutations that produce a stepwise disorder-to-order transition in the N-terminal tail of the canonical yeast Gγ subunit, Ste18. Mutant tail structures were confirmed using circular dichroism and molecular dynamics and then substituted for the wildtype gene in yeast. We find that increasing the number of helix-stabilizing mutations, but not isometric mutation controls, has a negative and proteasome-independent effect on Ste18 protein levels as well as a differential effect on pheromone-induced levels of active MAPK/Fus3, but not MAPK/Kss1. When expressed at wildtype levels, we further show that mutants with an alpha-helical N terminus exhibit a counterintuitive shift in Gβγ signaling that reduces active MAPK/Fus3 levels whilst increasing cell polarization and cell cycle arrest. These data reveal a role for Gγ subunit intrinsically disordered regions in governing the balance between multiple Gβγ signaling axes.

PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

Functional regulation of an intrinsically disordered protein via a conformationally excited state

A longstanding goal in the field of intrinsically disordered proteins (IDPs) is to characterize their structural heterogeneity and pinpoint the role of this heterogeneity in IDP function. Here, we use multinuclear chemical exchange saturation (CEST) nuclear magnetic resonance to determine the structure of a thermally accessible globally folded excited state in equilibrium with the intrinsically disordered native ensemble of a bacterial transcriptional regulator CytR. We further provide evidence from double resonance CEST experiments that the excited state, which structurally resembles the DNA-bound form of cytidine repressor (CytR), recognizes DNA by means of a "folding-before-binding" conformational selection pathway. The disorder-to-order regulatory switch in DNA recognition by natively disordered CytR therefore operates through a dynamical variant of the lock-and-key mechanism where the structurally complementary conformation is transiently accessed via thermal fluctuations.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

The mechanisms to dispose of misfolded proteins in the endoplasmic reticulum of adipocytes

Endoplasmic reticulum (ER)-associated degradation (ERAD) and ER-phagy are two principal degradative mechanisms for ER proteins and aggregates, respectively; however, the crosstalk between these two pathways under physiological settings remains unexplored. Using adipocytes as a model system, here we report that SEL1L-HRD1 protein complex of ERAD degrades misfolded ER proteins and limits ER-phagy and that, only when SEL1L-HRD1 ERAD is impaired, the ER becomes fragmented and cleared by ER-phagy. When both are compromised, ER fragments containing misfolded proteins spatially coalesce into a distinct architecture termed Coalescence of ER Fragments (CERFs), consisted of lipoprotein lipase (LPL, a key lipolytic enzyme and an endogenous SEL1L-HRD1 substrate) and certain ER chaperones. CERFs enlarge and become increasingly insoluble with age. Finally, we reconstitute the CERFs through LPL and BiP phase separation in vitro, a process influenced by both redox environment and C-terminal tryptophan loop of LPL. Hence, our findings demonstrate a sequence of events centered around SEL1L-HRD1 ERAD to dispose of misfolded proteins in the ER of adipocytes, highlighting the profound cellular adaptability to misfolded proteins in the ER in vivo.

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.

Taxonomically Restricted Genes in Bacillus may Form Clusters of Homologs and Can be Traced to a Large Reservoir of Noncoding Sequences

Taxonomically restricted genes (TRGs) are unique for a defined group of organisms and may act as potential genetic determinants of lineage-specific, biological properties. Here, we explore the TRGs of highly diverse and economically important Bacillus bacteria by examining commonly used TRG identification parameters and data sources. We show the significant effects of sequence similarity thresholds, composition, and the size of the reference database in the identification process. Subsequently, we applied stringent TRG search parameters and expanded the identification procedure by incorporating an analysis of noncoding and non-syntenic regions of non-Bacillus genomes. A multiplex annotation procedure minimized the number of false-positive TRG predictions and showed nearly one-third of the alleged TRGs could be mapped to genes missed in genome annotations. We traced the putative origin of TRGs by identifying homologous, noncoding genomic regions in non-Bacillus species and detected sequence changes that could transform these regions into protein-coding genes. In addition, our analysis indicated that Bacillus TRGs represent a specific group of genes mostly showing intermediate sequence properties between genes that are conserved across multiple taxa and nonannotated peptides encoded by open reading frames.

PrePPI: A structure informed proteome-wide database of protein-protein interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.

Graphic Abstract

Aggregation of Disordered Proteins Associated with Neurodegeneration

Cellular deposition of protein aggregates, one of the hallmarks of neurodegeneration, disrupts cellular functions and leads to neuronal death. Mutations, posttranslational modifications, and truncations are common molecular underpinnings in the formation of aberrant protein conformations that seed aggregation. The major proteins involved in neurodegeneration include amyloid beta (Aβ) and tau in Alzheimer's disease, α-synuclein in Parkinson's disease, and TAR DNA-binding protein (TDP-43) in amyotrophic lateral sclerosis (ALS). These proteins are described as intrinsically disordered and possess enhanced ability to partition into biomolecular condensates. In this review, we discuss the role of protein misfolding and aggregation in neurodegenerative diseases, specifically highlighting implications of changes to the primary/secondary (mutations, posttranslational modifications, and truncations) and the quaternary/supramolecular (oligomerization and condensation) structural landscapes for the four aforementioned proteins. Understanding these aggregation mechanisms provides insights into neurodegenerative diseases and their common underlying molecular pathology.

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.

Evolution of the orthopoxvirus core genome

Orthopoxviruses comprise several relevant pathogens, including the causative agent of smallpox and monkeypox virus. Analysis of orthopoxvirus genome evolution mainly focused on gene gains/losses. We instead analyzed core genes, which are conserved in all orthopoxviruses. We show that, despite their strong constraint, some genes involved in viral morphogenesis and transcription/replication were targets of pervasive positive selection, which was relatively uncommon in immunomodulatory genes. However at least three of the positively selected genes, E3L, A24R, and H3L, might have evolved in response to immune selection. Episodic positive selection was particularly common on the internal branches of the orthopox phylogeny and on the monkeypox virus lineage. The latter showed evidence of episodic positive selection at the D14L gene, which encodes a modulator of complement activation (MOPICE). Notably, two genes (B1R and A33R) targeted by episodic selection on more than one branch are involved in forms of intra-genomic conflict. Finally, we found that, in orthopoxvirus proteomes, intrinsically disordered regions (IDRs) tend to be less constrained and are common targets of positive selection. Extension of our analysis to all poxviruses showed no evidence that the IDR fraction differs with host range. Conversely, we found a strong effect of base composition, which was however not sufficient to explain IDR fraction. We thus suggest that, in poxviruses, the IDR fraction is maintained by modulating GC content to accommodate disorder-promoting codons. Overall, our data provide novel insight in orthopoxvirus evolution and provide a list of genes and sites that are expected to modulate viral phenotypes.

Nutrient status regulates MED19a phase separation for ORESARA1-dependent senescence

The mediator complex is highly conserved in eukgaryotes and is integral for transcriptional responses. Mediator subunits associate with signal-responsive transcription factors (TF) to activate expression of specific signal-responsive genes. As the key TF of Arabidopsis thaliana senescence, ORESARA1 (ORE1) is required for nitrogen deficiency (-N) induced senescence; however, the mediator subunit that associates with ORE1 remains unknown. Here, we show that Arabidopsis MED19a associates with ORE1 to activate -N senescence-responsive genes. Disordered MED19a forms inducible nuclear condensates under -N that is regulated by decreasing MED19a lysine acetylation. MED19a carboxyl terminus (cMED19a) harbors a mixed-charged intrinsically disordered region (MC-IDR) required for ORE1 interaction and liquid-liquid phase separation (LLPS). Plant and human cMED19 are sufficient to form heterotypic condensates with ORE1. Human cMED19 MC-IDR, but not yeast cMED19 IDR, partially complements med19a suggesting functional conservation in evolutionarily distant eukaryotes. Phylogenetic analysis of eukaryotic cMED19 revealed that the MC-IDR could arise through convergent evolution. Our result of MED19 MC-IDR suggests that plant MED19 is regulated by phase separation during stress responses.

MobiDB: 10 years of intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

Evolution of sequence traits of prion-like proteins linked to amyotrophic lateral sclerosis (ALS)

Prions are proteinaceous particles that can propagate an alternative conformation to further copies of the same protein. They have been described in mammals, fungi, bacteria and archaea. Furthermore, across diverse organisms from bacteria to eukaryotes, prion-like proteins that have similar sequence characters are evident. Such prion-like proteins have been linked to pathomechanisms of amyotrophic lateral sclerosis (ALS) in humans, in particular TDP43, FUS, TAF15, EWSR1 and hnRNPA2. Because of the desire to study human disease-linked proteins in model organisms, and to gain insights into the functionally important parts of these proteins and how they have changed across hundreds of millions of years of evolution, we analyzed how the sequence traits of these five proteins have evolved across eukaryotes, including plants and metazoa. We discover that the RNA-binding domain architecture of these proteins is deeply conserved since their emergence. Prion-like regions are also deeply and widely conserved since the origination of the protein families for FUS, TAF15 and EWSR1, and since the last common ancestor of metazoa for TDP43 and hnRNPA2. Prion-like composition is uncommon or weak in any plant orthologs observed, however in TDP43 many plant proteins have equivalent regions rich in other amino acids (namely glycine and tyrosine and/or serine) that may be linked to stress granule recruitment. Deeply conserved low-complexity domains are identified that likely have functional significance.

Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions in proteins are parts of the sequence that do not form ordered, folded structures under typical physiological conditions. In the original implementation of RRD, Rosetta ab initio models were generated, and disordered regions were predicted based on residue scores (disordered residues typically exist in regions of unfavorable scores). In this work, we show that by (i) replacing the ab initio modeling with AF (using the same scoring and disorder assignment approach) and (ii) updating the score function, the predictiveness improved significantly. Residues were better ranked by the order/disorder, evidenced by an improvement in receiver operating characteristic area-under-the-curve from 0.69 to 0.78 on a large (229 protein) and balanced data set (relatively even ordered versus disordered residues). Finally, the binary prediction accuracy also improved from 62% to 74% on the same data set. Our results show that the combined AF-RRD approach was as good as or better than all existing methods by these metrics (AF-RRD had the highest prediction accuracy).

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Phosprof: pathway analysis database of drug response based on phosphorylation activity measurements

Protein phosphorylation plays a fundamental role in many cellular processes. Proteins are phosphorylated by kinases, which have been studied as drug targets for the treatment of various diseases, particularly cancer. Because kinases have multiple roles in interconnected molecular pathways, their specific regulation is required to enhance beneficial and reduce adversarial effects of drugs. Using our previously developed platform, we measured phosphorylation profiles of MCF7 and K562 cells treated with 94 clinical drugs. These phosphorylation profiles can provide insights into pathway activities and biological functions. Here, we introduce Phosprof, a novel database of drug response based on phosphorylation activity. Phosprof is able to present up- or downregulated phosphorylated signature proteins on pathway maps, significant pathways on the hierarchal tree in signal transduction and commonly perturbed pathways affected by the selected drugs. It also serves as a useful web interface for new or known drug profile search based on their molecular similarity with the 94 drugs. Phosprof can be helpful for further investigation of drug responses in terms of phosphorylation by utilizing the various approved drugs whose target phenotypes are known.

DATABASE URL

https://phosprof.medals.jp/.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Wide-Line NMR Melting Diagrams, Their Thermodynamic Interpretation, and Secondary Structure Predictions for A30P and E46K α-Synuclein

Parkinson's disease is thought to be caused by aggregation of the intrinsically disordered protein, α-synuclein. Two amyloidogenic variants, A30P, and E46K familial mutants were investigated by wide-line ¹H NMR spectrometry as a completion of our earlier work on wild-type and A53T α-synuclein (Bokor M. et al. WT and A53T α-synuclein systems: melting diagram and its new interpretation. Int. J. Mol. Sci.2020, 21, 3997.). A monolayer of mobile water molecules hydrates A30P α-synuclein at the lowest potential barriers (temperatures), while E46K α-synuclein has here third as much mobile hydration, insufficient for functionality. According to wide-line ¹H NMR results and secondary structure predictions, E46K α-synuclein is more compact than the A30P variant and they are more compact than the wild type (WT) and A53T variants. Linear hydration vs potential barrier sections of A30P α-synuclein shows one and E46K shows two slopes. The different slopes of the latter between potential barriers E _a,1 and E _a,2 reflect a change in water-protein interactions. The 31-32% homogeneous potential barrier distribution of the protein-water bonds refers to a non-negligible amount of secondary structures in all four α-synuclein variants. The secondary structures detected by wide-line ¹H NMR are solvent-exposed α-helices, which are predicted by secondary structure models. β-sheets are only minor components of the protein structures as three- and eight-state predicted secondary structures are dominated by α-helices and coils.

Notch ankyrin domain: evolutionary rise of a thermodynamic sensor

Notch signalling pathway plays a key role in metazoan biology by contributing to resolution of binary decisions in the life cycle of cells during development. Outcomes such as proliferation/differentiation dichotomy are resolved by transcriptional remodelling that follows a switch from Notch^on to Notch^off state, characterised by dissociation of Notch intracellular domain (NICD) from DNA-bound RBPJ. Here we provide evidence that transitioning to the Notch^off state is regulated by heat flux, a phenomenon that aligns resolution of fate dichotomies to mitochondrial activity. A combination of phylogenetic analysis and computational biochemistry was utilised to disclose structural adaptations of Notch1 ankyrin domain that enabled function as a sensor of heat flux. We then employed DNA-based micro-thermography to measure heat flux during brain development, followed by analysis in vitro of the temperature-dependent behaviour of Notch1 in mouse neural progenitor cells. The structural capacity of NICD to operate as a thermodynamic sensor in metazoans stems from characteristic enrichment of charged acidic amino acids in β-hairpins of the ankyrin domain that amplify destabilising inter-residue electrostatic interactions and render the domain thermolabile. The instability emerges upon mitochondrial activity which raises the perinuclear and nuclear temperatures to 50 °C and 39 °C, respectively, leading to destabilization of Notch1 transcriptional complex and transitioning to the Notch^off state. Notch1 functions a metazoan thermodynamic sensor that is switched on by intercellular contacts, inputs heat flux as a proxy for mitochondrial activity in the Notch^on state via the ankyrin domain and is eventually switched off in a temperature-dependent manner.

Phase transition and remodeling complex assembly are important for SS18-SSX oncogenic activity in synovial sarcomas

Oncoprotein SS18-SSX is a hallmark of synovial sarcomas. However, as a part of the SS18-SSX fusion protein, SS18’s function remains unclear. Here, we depict the structures of both human SS18/BRG1 and yeast SNF11/SNF2 subcomplexes. Both subcomplexes assemble into heterodimers that share a similar conformation, suggesting that SNF11 might be a homologue of SS18 in chromatin remodeling complexes. Importantly, our study shows that the self-association of the intrinsically disordered region, QPGY domain, leads to liquid-liquid phase separation (LLPS) of SS18 or SS18-SSX and the subsequent recruitment of BRG1 into phase-separated condensates. Moreover, our results show that the tyrosine residues in the QPGY domain play a decisive role in the LLPS of SS18 or SS18-SSX. Perturbations of either SS18-SSX LLPS or SS18-SSX’s binding to BRG1 impair NIH3T3 cell transformation by SS18-SSX. Our data demonstrate that both LLPS and assembling into chromatin remodelers contribute to the oncogenic activity of SS18-SSX in synovial sarcomas.

Oncoprotein SS18-SSX is a hallmark of synovial sarcoma. Here the authors report phase separation of SS18-SSX and the binding of SS18-SSX to chromatin remodeling complex are important for the transformation activity of the oncoprotein SS18-SSX.

idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are proteins or protein-domains that do not have a single native structure, rather, they are a class of flexible peptides that can rapidly adopt multiple conformations. IDPs are quite abundant, and their dynamic characteristics provide unique advantages for various biological processes. The field of “unstructured biology” has emerged, in part, because of numerous computational studies that had identified the unique characteristics of IDPs and IDRs. The package ‘idpr’, short for Intrinsically Disordered Proteins in R, implements several R functions that match the established characteristics of IDPs to protein sequences of interest. This includes calculations of residue composition, charge-hydropathy relationships, and predictions of intrinsic disorder. Additionally, idpr integrates several amino acid substitution matrices and calculators to supplement IDP-based workflows. Overall, idpr aims to integrate tools for the computational analysis of IDPs within R, facilitating the analysis of these important, yet under-characterized, proteins. The idpr package can be downloaded from Bioconductor (https://bioconductor.org/packages/idpr/).

Intrinsically disordered signaling proteins: Essential hub players in the control of stress responses in Saccharomyces cerevisiae

Cells have developed diverse mechanisms to monitor changes in their surroundings. This allows them to establish effective responses to cope with adverse environments. Some of these mechanisms have been well characterized in the budding yeast Saccharomyces cerevisiae, an excellent experimental model to explore and elucidate some of the strategies selected in eukaryotic organisms to adjust their growth and development in stressful conditions. The relevance of structural disorder in proteins and the impact on their functions has been uncovered for proteins participating in different processes. This is the case of some transcription factors (TFs) and other signaling hub proteins, where intrinsically disordered regions (IDRs) play a critical role in their function. In this work, we present a comprehensive bioinformatic analysis to evaluate the significance of structural disorder in those TFs (170) recognized in S. cerevisiae. Our findings show that 85.2% of these TFs contain at least one IDR, whereas ~30% exhibit a higher disorder level and thus were considered as intrinsically disordered proteins (IDPs). We also found that TFs contain a higher number of IDRs compared to the rest of the yeast proteins, and that intrinsically disordered TFs (IDTFs) have a higher number of protein-protein interactions than those with low structural disorder. The analysis of different stress response pathways showed a high content of structural disorder not only in TFs but also in other signaling proteins. The propensity of yeast proteome to undergo a liquid-liquid phase separation (LLPS) was also analyzed, showing that a significant proportion of IDTFs may undergo this phenomenon. Our analysis is a starting point for future research on the importance of structural disorder in yeast stress responses.

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins

Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale three-dimensional modeling of proteins on minimal hardware, and we demonstrate this by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper.

Exploring the Peptide Potential of Genomes

Recent studies attribute a central role to the noncoding genome in the emergence of novel genes. The widespread transcription of noncoding regions and the pervasive translation of the resulting RNAs offer to the organisms a vast reservoir of novel peptides. Although the majority of these peptides are anticipated as deleterious or neutral, and thereby expected to be degraded right away or short-lived in evolutionary history, some of them can confer an advantage to the organism. The latter can be further subjected to natural selection and be established as novel genes. In any case, characterizing the structural properties of these pervasively translated peptides is crucial to understand (1) their impact on the cell and (2) how some of these peptides, derived from presumed noncoding regions, can give rise to structured and functional de novo proteins. Therefore, we present a protocol that aims to explore the potential of a genome to produce novel peptides. It consists in annotating all the open reading frames (ORFs) of a genome (i.e., coding and noncoding ones) and characterizing the fold potential and other structural properties of their corresponding potential peptides. Here, we apply our protocol to a small genome and show how to apply it to very large genomes. Finally, we present a case study which aims to probe the fold potential of a set of 721 translated ORFs in mouse lncRNAs, identified with ribosome profiling experiments. Interestingly, we show that the distribution of their fold potential is different from that of the nontranslated lncRNAs and more generally from the other noncoding ORFs of the mouse.

Meiotic sex chromosome inactivation and the XY body: a phase separation hypothesis

In mammalian male meiosis, the heterologous X and Y chromosomes remain unsynapsed and, as a result, are subject to meiotic sex chromosome inactivation (MSCI). MSCI is required for the successful completion of spermatogenesis. Following the initiation of MSCI, the X and Y chromosomes undergo various epigenetic modifications and are transformed into a nuclear body termed the XY body. Here, we review the mechanisms underlying the initiation of two essential, sequential processes in meiotic prophase I: MSCI and XY-body formation. The initiation of MSCI is directed by the action of DNA damage response (DDR) pathways; downstream of the DDR, unique epigenetic states are established, leading to the formation of the XY body. Accumulating evidence suggests that MSCI and subsequent XY-body formation may be driven by phase separation, a physical process that governs the formation of membraneless organelles and other biomolecular condensates. Thus, here we gather literature-based evidence to explore a phase separation hypothesis for the initiation of MSCI and the formation of the XY body.

Evolutionary analysis of angiosperm dehydrin gene family reveals three orthologues groups associated to specific protein domains

Dehydrins (DHNs) are a family of plant proteins that play important roles on abiotic stress tolerance and seed development. They are classified into five structural subgroups: K-, SK-, YK-, YSK-, and KS-DHNs, according to the presence of conserved motifs named K-, Y- and S- segments. We carried out a comparative structural and phylogenetic analysis of these proteins, focusing on the less-studied KS-type DHNs. A search for conserved motifs in DHNs from 56 plant genomes revealed that KS-DHNs possess a unique and highly conserved N-terminal, 15-residue amino acid motif, not previously described. This novel motif, that we named H-segment, is present in DHNs of angiosperms, gymnosperms and lycophytes, suggesting that HKS-DHNs were present in the first vascular plants. Phylogenetic and microsynteny analyses indicate that the five structural subgroups of angiosperm DHNs can be assigned to three groups of orthologue genes, characterized by the presence of the H-, F- or Y- segments. Importantly, the hydrophilin character of DHNs correlate with the phylogenetic origin of the DHNs rather than to the traditional structural subgroups. We propose that angiosperm DHNs can be ultimately subdivided into three orthologous groups, a phylogenetic framework that should help future studies on the evolution and function of this protein family.

Two stripe rust effectors impair wheat resistance by suppressing import of host Fe-S protein into chloroplasts

Several effectors from phytopathogens usually target various cell organelles to interfere with plant defenses, and they generally contain sequences that direct their translocation into organelles, such as chloroplasts. In this study, we characterized a different mechanism for effectors to attack chloroplasts in wheat (Triticum aestivum). Two effectors from Puccinia striiformis f. sp. tritici (Pst), Pst_4, and Pst_5, inhibit Bax-mediated cell death and plant immune responses, such as callose deposition and reactive oxygen species (ROS) accumulation. Gene silencing of the two effectors induced significant resistance to Pst, demonstrating that both effectors function as virulence factors of Pst. Although these two effectors have low sequence similarities and lack chloroplast transit peptides, they both interact with TaISP (wheat cytochrome b6-f complex iron-sulfur subunit, a chloroplast protein encoded by nuclear gene) in the cytoplasm. Silencing of TaISP impaired wheat resistance to avirulent Pst and resulted in less accumulation of ROS. Heterogeneous expression of TaISP enhanced chloroplast-derived ROS accumulation in Nicotiana benthamiana. Co-localization in N. benthamiana and western blot assay of TaISP content in wheat chloroplasts show that both effectors suppressed TaISP from entering chloroplasts. We conclude that these biotrophic fungal effectors suppress plant defenses by disrupting the sorting of chloroplast protein, thereby limiting host ROS accumulation and promoting fungal pathogenicity.

Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution

The noncoding genome plays an important role in de novo gene birth and in the emergence of genetic novelty. Nevertheless, how noncoding sequences' properties could promote the birth of novel genes and shape the evolution and the structural diversity of proteins remains unclear. Therefore, by combining different bioinformatic approaches, we characterized the fold potential diversity of the amino acid sequences encoded by all intergenic open reading frames (ORFs) of S. cerevisiae with the aim of (1) exploring whether the structural states' diversity of proteomes is already present in noncoding sequences, and (2) estimating the potential of the noncoding genome to produce novel protein bricks that could either give rise to novel genes or be integrated into pre-existing proteins, thus participating in protein structure diversity and evolution. We showed that amino acid sequences encoded by most yeast intergenic ORFs contain the elementary building blocks of protein structures. Moreover, they encompass the large structural state diversity of canonical proteins, with the majority predicted as foldable. Then, we investigated the early stages of de novo gene birth by reconstructing the ancestral sequences of 70 yeast de novo genes and characterized the sequence and structural properties of intergenic ORFs with a strong translation signal. This enabled us to highlight sequence and structural factors determining de novo gene emergence. Finally, we showed a strong correlation between the fold potential of de novo proteins and one of their ancestral amino acid sequences, reflecting the relationship between the noncoding genome and the protein structure universe.

New Findings on LMO7 Transcripts, Proteins and Regulatory Regions in Human and Vertebrate Model Organisms and the Intracellular Distribution in Skeletal Muscle Cells

LMO7 is a multifunctional PDZ–LIM protein that can interact with different molecular partners and is found in several intracellular locations. The aim of this work was to shed light on LMO7 evolution, alternative transcripts, protein structure and gene regulation through multiple in silico analyses. We also explored the intracellular distribution of the LMO7 protein in chicken and zebrafish embryonic skeletal muscle cells by means of confocal fluorescence microscopy. Our results revealed a single LMO7 gene in mammals, sauropsids, Xenopus and in the holostean fish spotted gar while two lmo7 genes (lmo7a and lmo7b) were identified in teleost fishes. In addition, several different transcripts were predicted for LMO7 in human and in major vertebrate model organisms (mouse, chicken, Xenopus and zebrafish). Bioinformatics tools revealed several structural features of the LMO7 protein including intrinsically disordered regions. We found the LMO7 protein in multiple intracellular compartments in chicken and zebrafish skeletal muscle cells, such as membrane adhesion sites and the perinuclear region. Curiously, the LMO7 protein was detected within the nuclei of muscle cells in chicken but not in zebrafish. Our data showed that a conserved regulatory element may be related to muscle-specific LMO7 expression. Our findings uncover new and important information about LMO7 and open new challenges to understanding how the diverse regulation, structure and distribution of this protein are integrated into highly complex vertebrate cellular milieux, such as skeletal muscle cells.

Study of transcription factor druggabilty for prostate cancer using structure information, gene regulatory networks and protein moonlighting

Prostate cancer is the second leading cause of cancer-related death in men. Metastasis shows poor survival even though the recovery rate is high. In spite of numerous studies regarding prostate carcinoma, multiple questions are still unanswered. In this regards, gene regulatory network can uncover the mechanisms behind cancer progression, and metastasis. Under a feed forward loop, transcription factors (TFs) can be a good druggable candidate. We have proposed a computational model to study the uncertainty of TFs and suggest the appropriate cellular conditions for drug targeting. We have selected feed-forward loops depending on the shared list of the functional annotations among TFs, genes and miRNAs. From the potential feed forward loop cores, six TFs were identified as druggable targets, which include AR, CEBPB, CREB1, ETS1, NFKB1 and RELA. However, TFs are known for their Protein Moonlighting properties, which provide unrelated multi-functionalities within the same or different subcellular localizations. Following that, we have identified such functions that are suitable for drug targeting. On the other hand, we have tried to identify membraneless organelles for providing more specificity to the proposed time and space theory. The study has provided certain possibilities on TF-based therapeutics. The controlled dynamic nature of the TF may have enhanced the chances where TFs can be considered as one of the prime drug targets. Finally, the combination of membranless phase separation and protein moonlighting has provided possible druggable period within the biological clock.