Biology subjects

Tartaglia, G. G.

Publications and source records attributed to Tartaglia, G. G..

At least 19 recordsLinked to original sources

bioRxiv2026

PHANES: an integrated resource of protein features for the human proteome

Protein analysis and machine-learning workflows increasingly rely on large-scale numerical representations of proteins. While protein language models provide powerful embeddings, their limited interpretability and high computational cost hinder their use in many biological applications. We present PHANES, a human proteome database and web server providing a comprehensive, biologically interpretable set of features for 20335 proteins. PHANES integrates sequence-derived physicochemical descriptors, structure-based and graph-theoretical features computed from AlphaFold predictions, thermostability indices, and tissue expression specificity. In addition to precomputed features for the human proteome, an integrated web server enables on-demand feature calculation for user-provided protein structures. PHANES supports data export, visualization and comparative analysis at the level of individual proteins and protein groups, bridging the gap between complex numerical representations and biological interpretation while providing an interpretable foundation for exploratory analyses and downstream machine-learning applications. PHANES is freely available at https://tools.tartaglialab.com/phanes.

bioinformatics↗

bioRxiv2026

A circRNA-derived IRES-like element enables programmable translational activation of the endogenous chaperone HSP90

Circular RNAs (circRNAs) are covalently closed transcripts that lack a 5' cap, consequently their translation depends on cap-independent initiation, in most cases through Internal Ribosome Entry Sites (IRESs). Whereas IRES-mediated translation has been studied almost exclusively in cis, the use of IRES-containing circRNAs to activate translation of an independent mRNA in trans has only recently begun to be explored. Here, we characterize SynFarsa, a synthetic circRNA-derived IRES-like element that promotes cap-lindependent translation in mouse and human cells. We engineered SynFarsa into a trans-acting regulator by fusing it to a short antisense sequence complementary to a selected region of a target mRNA. This reproduces the architecture of a circular SINEUP, in which IRES activity promotes translation in trans of a transcript recognized through antisense complementarity. Directing this construct to the hsp90 3'UTR increased HSP90 protein levels approximately 1.6-lfold without altering hsp90 mRNA abundance, whereas the hsp70 3'UTR and the 5'UTRs of both transcripts were not permissive, indicating that activity depends strongly on target site selection. In a FUSP525L neuroblastoma model of Amyotrophic Lateral Sclerosis, this increase in HSP90 was accompanied by a reduction in the number and size of cytoplasmic FUS aggregates. SynFarsa thus provides a circRNA-derived effector domain for targeted translational upregulation of an endogenous protein.

biochemistry↗

bioRxiv2026

Phase Separation Potential of Marsupial RSX RNA Reveals Convergent Evolution of X-Chromosome Inactivation Mechanisms

Background: X-chromosome inactivation (XCI) evolved independently in eutherian and marsupial mammals, where it is orchestrated by the unrelated long non-coding RNAs Xist and RSX, respectively. Xist organizes a repressive nuclear compartment through multivalent RNA-protein interactions, but whether RSX exploits similar biophysical principles remains unknown. A recent paper has identified bona fide RSX interacting proteins. Results: We integrated proteome-scale RNA-protein interaction prediction, experimental validation, phase-separation propensity analysis, functional annotation and comparative RNA-structure modelling to characterize the RSX interaction landscape. Using catRAPID, we ranked 1,168 RNA-binding proteins from the native Monodelphis domestica proteome. Predictions were significantly enriched for experimentally identified RSX interactors, with 4.85-fold enrichment among the top 50 candidates (P approximately 1.6 x 10-5), increasing to approximately eightfold for proteins shared by the experimental RSX and Xist interactomes (P approximately 2 x 10-6). Among 30 high-confidence RSX interactors, 13 were experimentally supported, 17 were previously unrecognized candidates and 17 exhibited high phase-separation propensity. The network was enriched in ribonucleoprotein granules and nuclear bodies and converged on m6A regulators and SR-family splicing factors. Comparative modelling detected no conserved secondary or tertiary architecture between RSX and Xist. Conclusions: RSX and Xist appear to have converged not through RNA sequence or global structure, but through recruitment of related, condensation-prone protein networks. These findings identify interaction-network and biophysical convergence as a potential principle of lncRNA-mediated chromosome regulation and provide testable candidates for determining whether RSX establishes a condensate-like compartment on the marsupial inactive X.

bioinformatics↗

bioRxiv2026

CHD8 orchestrates chromatin landscapes during early female neuronal differentiation

Background: Autism spectrum disorder (ASD) shows a strong male bias (~4:1). CHD8, a frequently mutated ASD gene, regulates neuronal development and Xist regulation in X-chromosome inactivation (XCI). However, its role in female neurodevelopment remains poorly understood due to the predominance of male-derived or sex-agnostic models. Methods: We performed integrated multi-omics analysis of female mouse embryonic stem cells (ES) differentiating to neuronal progenitor cells (NPCs) using wild-type, CHD8 knockdown (KD), knockout (KO), and domain-specific rescue lines (full-length, {Delta}Chromo, {Delta}Helicase). RNA-seq, CHD8 and H3K4me3 ChIP-seq, and ATAC-seq datasets were integrated to assess transcriptional, chromatin-binding and -accessibility changes during differentiation. Results: CHD8 occupancy was substantially remodelled during female neuronal differentiation, with 3,754 genes gaining NPCs-specific CHD8 binding predominantly at distal regulatory elements. CHD8 loss dysregulated 2,752 genes (1,134 upregulated and 1,618 downregulated), in NPCs. Differential accessibility analysis identified 4,486 chromatin regions with significant CHD8-dependent changes. Domain-specific rescue experiments showed that chromodomain and helicase activity makes distinct, non-redundant contributions to transcriptional recovery: {Delta}Chromo-rescued only ~ 1.0% of CHD8-dependent transcriptional changes, {Delta}Helicase rescued ~ 41.1%, and full-length CHD8 rescued ~ 70%. Integration of binding, expression, and accessibility data identified 8 high-confidence direct CHD8 target genes, and cross-referencing with the SFARI Autism Risk Gene database revealed that CHD8-dependent transcriptional changes converge on autism-related pathways. Conclusions: CHD8 acts as a key regulator of female-derived neuronal differentiation, recruited to H3K4me3-marked promoters in a chromodomain-dependent manner, with helicase activity providing non-redundant regulatory capacity at a subset of targets. These findings provide a molecular framework for CHD8-dependent transcriptional regulation in female NPCs and underscore the importance of including female-derived systems in neurodevelopmental disorder research.

neuroscience↗

bioRxiv2026

LBR nucleoplasmic domains regulate X-chromosome solubility and nuclear organization

The nuclear lamina plays a central role in genome organization, yet how specific lamina-associated proteins regulate chromosome architecture during development remains unclear. Here, we show that the nucleoplasmic domains of the Lamin B Receptor (LBR) are essential for X-chromosome localization at the nuclear periphery and chromatin architecture during neural differentiation. Using genetic dissection of LBR function, combined with genome-wide chromatin solubility profiling and transcriptional analyses, we demonstrate that loss of LBR N-terminal domains impairs proper cell differentiation and X chromosome inactivation (XCI), selectively disrupting chromatin structure in neural progenitors but not in pluripotent cells. Strikingly, these effects are disproportionately concentrated - but not limited to - on the inactive X chromosome, which undergoes a pronounced shift toward a more soluble chromatin state. Our findings establish the nucleoplasmic function of LBR as a key determinant of X-chromosome functionality and identify chromatin solubility and accessibility as a previously underappreciated layer of genome regulation by the nuclear lamina in XCI. Finally, our work provides definitive genetic evidence that LBRs nuclear architectural functions are molecularly separable from its metabolic sterol reductase activity, which is preserved in our model, and are critically necessary for XCI in differentiating mouse female XX ESCs models.

genetics↗

bioRxiv2026

HERCULES: an integrative deep-learning framework for predicting RNA-binding propensity and mutation effects at single-residue resolution

RNA-binding proteins (RBPs) regulate essential aspects of RNA metabolism, yet accurately identifying RNA-binding domains (RBDs) and quantifying the impact of sequence variation on RNA-binding ability remain challenging. Here, we present HERCULES (Hybrid framEwoRk for RNA-binding domain loCalization and mUtation anaLysis using physicochemical and languagE modelS), a unified sequence-based framework for simultaneous RBD localization, global RNA-binding propensity prediction and mutation effect assessment. HERCULES integrates a fine-tuned protein language model with an explicit residue-level physicochemical module, combining global contextual representations with local mutation-sensitive descriptors. On an independent test set, the HERCULES global score discriminates RBPs from non-RBPs with an AUROC of 0.86. At residue resolution, HERCULES outperforms state-of-the-art sequence-based predictors in identifying canonical, non-canonical and putative RBDs across Pfam-annotated proteins. Using a curated dataset of experimentally validated RNA-binding-disrupting mutations, HERCULES correctly classifies 87% of deleterious variants, including single-amino acid substitutions. Evaluation on experimentally resolved protein-RNA complexes further demonstrates robust residue-level performance and improved generalization when contact annotations are augmented with AlphaFold3-predicted complexes. By unifying domain localization and mutation sensitivity within a single sequence-only framework, HERCULES provides a mechanistically interpretable approach for studying RNA-protein interactions. HERCULES is freely available at https://tools.tartaglialab.com/hercules and as an open-source Python package at https://github.com/tartaglialabIIT/hercules.git.

biochemistry↗

bioRxiv2025

RNA interactions drive structural and functional diversification of alpha-synuclein fibrils

Nucleic acids, particularly RNA, exert multiple roles on protein aggregation, yet their mechanistic involvement remains poorly understood. Building on prior work demonstrating RNA-driven acceleration of protein aggregation, we now demonstrate that RNA binding markedly reshapes the structural and functional properties of alpha-synuclein (aS) fibrils, a protein implicated in Parkinsons disease (PD). Although monomeric aS exhibits negligible RNA-binding properties, aggregation markedly increases its RNA-binding capacity. Our kinetic analyses reveal that RNA incorporation during initial stages of aggregation alters fibril elongation dynamics and modulates surface-mediated secondary nucleation in a seed concentration-dependent manner. Spectroscopic and microscopic characterization (FTIR, micro-FTIR) demonstrate that RNA promotes the formation of structurally distinct, more compact aS fibrils with heterogeneous morphology. Exploiting the recently discovered catalytic hydrolase activity of aS fibrils, we show that direct RNA binding inhibits enzymatic function, consistent with RNA-induced conformational remodeling that limits the exposure of critical residues. Additionally, RNA binding to fibril surfaces impairs proteolytic degradation by proteinase K, suggesting RNA stabilizes fibrils against clearance mechanisms. Moreover, RNA sequencing of fibril-bound nucleic acids uncovers a preference towards uridine-rich motifs that computational docking locates within hydrophilic cavities of disease-associated fibril structures. Collectively, we identify RNA as an active co-factor that remodels structure, propagation kinetics, stability, and function of aS fibrils. We offer a mechanistic link between RNA interaction and amyloid polymorphism relevant to synucleinopathies and, in broader contexts, to amyloid-associated diseases. Significance statementOur work represents the first systematic evidence that RNA directly interacts with protein fibrils and modulates key functional properties such as aggregation kinetics, proteolytic resistance and catalytic activity. We demonstrate that RNA co-aggregates with alpha-synuclein inducing structural conformations that depend on RNA for efficient assembly. Given the resemblance of these RNA-associated fibrils to those found in multiple system atrophy, our findings suggest that the final fibril structures in the patient brain are influenced by multiple cofactors and may differ substantially from recombinant fibrils formed in vitro, even when seeded with patient-derived material.

biochemistry↗

bioRxiv2025

Integrating RNA Structure and Protein Interactions to Uncover the Mechanisms of Viral and Cellular IRES Function

BackgroundRNAs fold into complex structures that critically influence gene expression. A prominent class of regulatory elements resides in the 5' untranslated region (5' UTR), where internal ribosome entry sites (IRESs) promote cap-independent translation by directly engaging the ribosome. First discovered in viral genomes, IRESs have been classified into four types according to their structural compactness and factor requirements. While viral IRESs are well studied, cellular IRESs remain poorly understood: they display limited sequence conservation, reduced structural compactness, and variable dependence on auxiliary RNA-binding proteins known as IRES trans-acting factors (ITAFs). Whether their activity relies mainly on RNA structure or protein assistance remains unresolved. Here, we present a computational framework that combines in silico mutagenesis and RNA-protein interaction profiling to investigate IRES mechanisms and guide the design of synthetic elements. ResultsUsing the Hepatitis C Virus (HCV) IRES as a benchmark, we performed systematic single-nucleotide mutagenesis coupled with structural predictions. Mutations were classified as synonymous or non-synonymous based on their effect on the secondary structure. The HCV IRES showed overall robustness, but the domain interacting with eIF3 was particularly sensitive, consistent with its essential role in translation initiation. Extending this approach to other viral IRES families revealed distinct profiles of resilience: Aphthoviruses retained structural integrity despite extensive sequence variation, whereas Cripaviruses displayed higher variability. We then applied the same analysis to cellular IRESs, which proved more structurally sensitive, suggesting stronger reliance on cofactor support. To probe this connection, we used the catRAPID approach to predict interactions with translation-related proteins. The method distinguished IRESs with known ITAF binding, such as PTBP1, and highlighted stability-promoting mutations that increased the predicted affinity for translation factors. ConclusionsOur in silico analysis indicates that mutational tolerance mirrors IRES cofactor dependence: compact viral IRESs are structurally robust, whereas non-viral IRESs are more reliant on protein interactions. By linking structure prediction with interaction profiling, we identify variants that both stabilize IRESs and improve binding to ITAFs or translation factors. This framework provides mechanistic insight into sequence-structure-function relationships and supports the rational design of synthetic IRES elements for therapeutic and biotechnological applications.

biochemistry↗

bioRxiv2025

A spatial long-read approach at near-single-cell resolution reveals developmental regulation of splicing and polyadenylation sites in distinct cortical layers and cell types.

Genome-wide single-cell and spatial long-read approaches have gained traction, but mostly lack single-cell resolution - and yield limited read lengths. Here, we introduce spatial ISOform sequencing (Spl-ISO-Seq), which reveals exons and polyadenylation sites from long reads with near-single-cell resolution. Spl-ISO-Seq selects long cDNAs and doubles to triples read lengths compared to standard preparations. Adding a highly specific software tool (Spl-ISOquant) and comparing human post-mortem pre-puberty samples of the visual cortex (8-11 years) to post-puberty samples (16-19 years), we find that cortical layers harbor stronger splicing and poly(A)-site regulation than the adjacent white matter, with enrichment of multiple protein-domain types. For oligodendrocytes however, developmental splicing changes are stronger in white matter. Among cortical layers, layer 4 has the most developmental changes in alternative-exon inclusion in excitatory neurons and in poly(A) sites. We also find many repeat elements, especially ERV1 long terminal repeats downstream of developmentally-regulated layer 4 exons. Overall, alternative splicing changes are linked to synapses - specifically at the post-synapse. Age-linked splicing changes in layers 1-3 and 4 are associated with autism spectrum disorder but not with schizophrenia, amyotrophic lateral sclerosis and Alzheimers disease. These results root developmental splicing changes during puberty and the resulting protein changes in specific layers and cell types. More generally, our new technologies enable new observations for any complex tissue.

genomics↗

bioRxiv2025

Computationally Designed RNA Aptamers Enable Selective Detection of FUS Pathology in ALS

FUS is an RNA/DNA-binding protein whose mislocalization and aggregation are defining pathological features of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Detecting pathological FUS assemblies remains challenging, as antibody-based approaches are frequently limited by epitope masking, conformational heterogeneity, and cross-reactivity with physiological FUS pools. Here we apply rationally designed RNA aptamers to selectively recognize FUS across soluble and aggregated states. The aptamers bind solvent-exposed RNA-binding regions of FUS with low-nanomolar affinity and adopt stable hairpin conformations that support high specificity. In cultured cells expressing the ALS-associated p.P525L FUS mutation, the aptamers detect cytoplasmic and nuclear FUS assemblies that are frequently missed by commercial antibodies and show reduced recognition of FUS-containing protein complexes. Using super-resolution imaging, the aptamers enable visualization of early FUS aggregation intermediates that are inaccessible to conventional amyloid dyes. In post-mortem brain tissue from individuals with FUS-ALS, aptamer staining selectively labels pathological FUS while sparing normal nuclear FUS, revealing prominent nuclear and nucleolar pathology that is poorly resolved by antibody-based methods. Together, these findings establish RNA aptamers as sensitive and selective probes for pathological FUS and uncover previously underappreciated features of FUS aggregation in ALS. This work highlights the value of nucleic-acid-based recognition tools for interrogating protein misfolding and neurodegenerative disease pathology.

biochemistry↗

bioRxiv2025

scRAPID-web: a web server for predicting protein-RNA interactions from single-cell transcriptomics

SummarySingle-cell RNA sequencing (scRNA-seq) enables high-resolution studies of gene regulation, capturing gene expression at the individual cell level. We previously developed scRAPID, a computational pipeline for predicting protein-RNA interactions and identifying hub RNA-binding proteins (RBP) and RNAs through the integration of gene regulatory network (GRNs) inference from scRNA-seq data and catRAPID predictions. To make this tool accessible to a broader audience, we introduce scRAPID-web, a user-friendly web server supporting analysis of scRNA-seq data across eight model organisms. scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as gene selection and cell type filtering. Users can choose from three GRN inference algorithms and decide whether to focus the analysis on specific gene types. Precompiled libraries allow fast filtering and motif-based validation of the inferred interactions. Results include detailed tables of predicted protein-RNA pairs and hubs, along with an interactive network visualization of potential RBP complexes built based on the inferred shared targets. scRAPID-web democratizes access to GRN-based analyses, providing insights into protein-RNA interactions and regulatory complexes in diverse cellular contexts. Availability and implementationscRAPID-web can be accessed at: https://tools.tartaglialab.com/scrapid.

bioinformatics↗

bioRxiv2025

Decoding RNA-RNA Interactions: The Role of Low-Complexity Repeats and a Deep Learning Framework for Sequence-Based Prediction

RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyze several large-scale RRI datasets and identify low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positioning them as key hubs in RNA-RNA interaction networks. These RRIs appear to be important for several aspects of RNA metabolism. Sequencing-based analysis of the lncRNA Lhx1os interactors validates the importance of LCRs in shaping contacts potentially involved in neuronal development. Recognizing the pivotal role of sequence determinants, we develop RIME, a deep learning model that predicts RRIs by leveraging embeddings from a nucleic acid language model. RIME outperforms traditional thermodynamics-based tools, successfully captures the role of LCRs and prioritizes high-confidence interactions, including those established by lncRNAs. RIME is freely available at https://tools.tartaglialab.com/rna_rna.

biochemistry↗

bioRxiv2025

Computationally-designed aptamers targeting RAD51-BRCA2 interaction inhibit RAD51 nuclear recruitment

The interaction between RAD51 and BRCA2 plays a key role in homologous recombination (HR), a critical DNA repair mechanism essential for the survival of cancer cells. Disrupting this interaction increases the sensitivity of cancer cells to chemotherapeutic agents. Here, we employed in silico methods to design a novel class of aptamers--customized single-stranded oligonucleotides--specifically engineered to bind RAD51. These aptamers were developed with the aim of selectively modulating RAD51s nuclear recruitment and its role in DNA repair processes. The leading candidate displays high affinity for RAD51, competing with BRCA2 for the same interaction site in vitro, as confirmed through biolayer interferometry (BLI) and fluorescence lifetime imaging microscopy (FLIM). We tested the efficacy of the leading aptamer in pancreatic cancer cells and observed that it significantly impedes RAD51 nuclear localization, reduces homologous recombination (HR) efficiency, and increases DNA damage. Critically, our aptamer potentiates the cytotoxicity of the PARP inhibitor olaparib, exploiting synthetic lethality (SL) to induce cancer cell death. Our study showcases an aptamer-based approach for selectively targeting protein interactions within DNA repair pathways, introducing a promising avenue for SL-based treatments applicable to a wide range of cancers.

biochemistry↗

bioRxiv2024

Negative regulation of miRNAs sorting in EVs: the RNA-binding protein PCBP2 impairs SYNCRIP-mediated miRNAs EVs loading

While it is accepted that Extracellular Vesicles (EVs)-mediated transfer of microRNAs contributes to intercellular communication, the knowledge about molecular mechanisms controlling the selective and dynamic miRNA-loading in EVs is still limited to few specific RNA-binding proteins interacting with sequence determinants. Moreover, although mutagenesis analysis demonstrated the presence/function of specific intracellular retention motifs, the interacting protein/s remained unknown. Here, PCBP2 was identified as a direct interactor of an intracellular retention motif: CLIP coupled to RNA pull down and proteomic analysis demonstrated that it binds to miRNAs embedding this motif and mutagenesis proved the binding specificity. Notably, PCBP2 binding requires SYNCRIP, a previously characterized miRNA EV-loader as indicated by SYNCRIP knock-down. SYNCRIP and PCBP2 may contemporarily bind to miRNAs as demonstrated by EMSA assays and PCBP2 knock-down causes EV-loading of intracellular microRNAs. This evidence highlights that multiple proteins/miRNA interactions govern miRNA compartmentalization and identifies PCBP2 as a dominant inhibitor of SYNCRIP function.

molecular biology↗

bioRxiv2024

The role of RNA in the nanoscale organization of alpha-synuclein phase separation

The cellular accumulation of alpha-synuclein (aS) aggregates is a hallmark of several neurodegenerative diseases. Recent studies suggest that the aberrant transition of monomeric aS into solid-like aggregates may occur through an intermediate liquid-like state, where the protein partitions between dense and dilute phases. Although aS is not typically recognized as an RNA-binding protein, it can bind RNA under aggregation conditions, but its impact on aS liquid-like phases remains unexplored. Employing a combination of fluorescence spectroscopy techniques, we investigated aS dynamics in both phases in the presence of RNA. Our analysis revealed the formation of nanoclusters involved in initiating phase separation and uncovered heterogeneity within the dense phase, discovering that aS molecules exist in two distinct mobility states. Additionally, we demonstrated that RNA induces morphological changes and promotes the liquid-to-solid transition of aS dense phase. These findings underscore the active role of RNA in modulating aS phase transitions.

biophysics↗

bioRxiv2024

Rationalizing the Effects of RNA Modifications on Protein Interactions

RNA modifications play a crucial role in regulating gene expression by altering RNA structure and modulating interactions with RNA-binding proteins (RBPs). In this study, we explore the impact of specific RNA chemical modifications--N6-methyladenosine (mA), A-to-I editing, and pseudouridine ({Psi})--on RNA secondary structure and protein-RNA interactions. Utilizing genome-wide data, including RNA secondary structure predictions and protein-RNA interaction datasets, we classify proteins into distinct categories based on their binding behaviors: modification-specific and structure-independent, or modification-promiscuous and structure-dependent. For instance, mA readers like YTHDF2 exhibit modification-specific and structure-independent binding, consistently attaching to mA regardless of structural changes. Conversely, proteins such as U2AF2 display modification-promiscuous and structure-dependent behavior, altering their binding preferences in response to structural changes induced by different modifications. A-to-I editing, which causes significant structural changes, typically reduces protein interactions, while {Psi} enhances RNA structural stability, albeit with variable effects on protein binding. To better predict these interactions, we developed the catRAPID 2.0 RNA modifications algorithm, which forecasts the effects of RNA modifications on protein-RNA binding propensities. This algorithm serves as a valuable tool for researchers, enabling the prediction and analysis of RNA modifications impact on protein interactions, thus offering new insights into RNA biology and engineering. The catRAPID 2.0 RNA modifications tool is available at http://service.tartaglialab.com/new_submission/catrapid_omicsv2_rna_mod. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/610603v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@136148corg.highwire.dtl.DTLVardef@17f2d65org.highwire.dtl.DTLVardef@7d4ce9org.highwire.dtl.DTLVardef@447e47_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Accurate Predictions of Phase Separating Proteins at Single Amino Acid Resolution

Liquid-liquid phase separation (LLPS) is a molecular mechanism that leads to the formation of membraneless organelles inside the cell. Despite recent advances in the experimental probing and computational prediction of proteins involved in this process, the identification of the protein regions driving LLPS and the prediction of the effect of mutations on LLPS are lagging behind. Here, we introduce catGRANULE 2.0 ROBOT (R - Ribonucleoprotein, O - Organization, in B - Biocondensates, O - Organelle, T - Types), an advanced algorithm for predicting protein LLPS at single amino acid resolution. Integrating physico-chemical properties of the proteins and structural features derived from AlphaFold models, catGRANULE 2.0 ROBOT significantly surpasses traditional sequence-based and state-of-the-art structure-based methods in performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.76 or higher. We present a comprehensive evaluation of the algorithm across multiple organisms and cellular components, demonstrating its effectiveness in predicting LLPS propensities at the single amino acid level and the impacts of mutations on LLPS. Our results are robustly supported by experimental validations, including immunofluorescence microscopy images from the Human Protein Atlas. catGRANULE 2.0 ROBOTs potential in protein design and mutation control can improve our understanding of proteins propensity to form subcellular compartments and help develop strategies to influence biological processes through LLPS. catGRANULE 2.0 ROBOT is freely available at https://tools.tartaglialab. com/catgranule2.

biochemistry↗

bioRxiv2024

Subgenomic flaviviral RNAs and human proteins: in silico exploration of anti-host defense mechanisms

BackgroundFlaviviruses pose significant global health threats, infecting over 300 million people annually. Among their evasion strategies, the production of subgenomic flaviviral RNAs (sfRNAs) from the 3 UTR of viral genomes is particularly notable. These sfRNAs interact with human proteins, disrupting key cellular processes such as RNA splicing and the interferon response. ResultsUtilizing a comprehensive in silico approach with the catRAPID algorithm, we analyzed over 300,000 interactions between sfRNAs and human proteins derived from more than 8,000 flavivirus genomes, including Dengue, Zika, Yellow Fever, West Nile, and Japanese Encephalitis viruses. Our study not only validated known interactions but also revealed novel human proteins that could be involved in sfRNA-mediated host defense evasion, including helicases, splicing factors, and chemokines. We propose that sfRNAs function as molecular sponges, sequestering specific proteins indicative of sfRNA-forming regions across flaviviruses. These findings represent a valuable resource for diagnostic and therapeutic developments. ConclusionsOur findings significantly expand the known interactome of sfRNAs with human proteins, underscoring their role in modulating host cellular pathways. By providing the first extensive atlas of sfRNA interactions, we offer new insights into how flaviviruses can manipulate host cellular machinery to facilitate viral survival and persistence. Intriguingly, we predict interaction with stress granules, a critical component of the cellular response to viral infection, suggesting a mechanism by which flaviviruses inhibit their formation to evade host defenses. This atlas not only serves as a resource for exploring therapeutic targets but also aids in the identification of sfRNA biomarkers for improved flavivirus diagnostics.

biochemistry↗