Biology subjects

Armaos, A.

Publications and source records attributed to Armaos, A..

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

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

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

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↗

bioRxiv2023

Prediction of protein-RNA interactions from single-cell transcriptomic data

RNA-binding proteins play a crucial role in regulating RNA processing, yet our understanding of their interactions with coding and non-coding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on sequence and structure can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). In the present study, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and we propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be inferred from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. Notably, the incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long non-coding RNAs, and enables the identification of hub RBPs and hub RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. We have made the software freely available at https://github.com/tartaglialabIIT/scRAPID.

bioinformatics↗

bioRxiv2023

Identification and prediction of G-quadruplex RNA-binding proteins with roles in transcription and phase separation

RNA-binding proteins are central for many biological processes and their large-scale identification has demonstrated a broad range of functions. RNA G-quadruplexes are important regulatory elements occurring in both coding and non-coding transcripts, yet our knowledge of their structure-based interactions is limited. Here starting from theoretical prediictions, we show experimentally that a large number of chromatin-binding proteins bind to RNA G-quadruplexes and we classify these based on their RNA G-quadruplex-binding potential. Combining experimental identification of nuclear RNA G-quadruplex-binding proteins with computational analysis, we create a prediction tool that can assign probability score for a protein that it binds RNA G-quadruplexes. We show that predicted G-quadruplex RNA-binding proteins exhibit high degree of protein disorder and hydrophilicity, and suggest involvement in both transcription and phase-separation into membrane-less organelles, particularly the nucleolus. Finally, we present this tool as a web application for estimating RNA G4-binding propensity for proteins of interest at http://service.tartaglialab.com/new_submission/clever_G4_classifier.

molecular biology↗

bioRxiv2023

ORC1 binds to cis-transcribed RNAs for efficient activation of replication origins

Cells must coordinate the activation of thousands of replication origins dispersed throughout their genome. Active transcription is known to favor the formation of mammalian origins, although the role that RNA plays in this process remains unclear. We show that the ORC1 subunit of the human Origin Recognition Complex interacts with RNAs transcribed from genes with origins in their transcription start sites (TSSs), displaying a positive correlation between RNA binding and origin activity. RNA depletion, or the use of ORC1 RNA-binding mutant, result in inefficient activation of proximal origins, linked to impaired ORC1 chromatin release. ORC1 RNA binding activity resides in its intrinsically disordered region, involved in intra- and inter-molecular interactions, regulation by phosphorylation, and phase-separation. We show that RNA binding favors ORC1 chromatin release, by regulating its phosphorylation and subsequent degradation. We propose that fluctuating concentrations of RNA during the cell cycle may play a sequential role in controlling origins through interaction with this flexible region of ORC1. Our results unveil a novel non-coding function of RNA as a dynamic component of the chromatin, orchestrating the activation of replication origins. One sentence summaryThe human origin recognition complex subunit 1 ORC1, binds to RNAs transcribed from genes with origins of replication at the TSS, which is required for optimal origin activation.

molecular biology↗

bioRxiv2023

MAPK/MAK/MRK overlapping kinase (MOK) controls microglial inflammatory/type-I IFN responses via Brd4 and is involved in ALS pathophysiology

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease affecting motor neurons and characterized by microglia-mediated neurotoxic inflammation whose underlying mechanisms remain incompletely understood. In this work we reveal that MAPK/MAK/MRK overlapping kinase (MOK), with unknown physiological substrate, displays an immune function by controlling inflammatory and type-I IFN responses in microglia which are detrimental to primary motor neurons. Moreover, we uncover the epigenetic reader bromodomain-containing protein 4 (Brd4) as the first molecule regulated by MOK, by promoting Ser492-phospho-Brd4 levels. We further demonstrate that MOK regulates Brd4 functions by supporting its binding to cytokine gene promoters, therefore enabling innate immune responses. Remarkably, we show that MOK levels are increased in ALS spinal cord, particularly in microglial cells, and that administration of a chemical MOK-inhibitor to ALS model mice is able to modulate Ser492-phospho-Brd4 levels, suppress microglial activation and modify disease course, indicating a pathophysiological role of MOK kinase in ALS and neuroinflammation.

neuroscience↗

bioRxiv2022

The PRALINE database: Protein and Rna humAn singLe nucleotIde variaNts in condEnsates

SummaryBiological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress-granules, processing bodies, and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates physical properties. AvailabilityPRALINE is freely accessible on the web at http://alvinlee.bio.uniroma1.it/praline. Supplementary informationGeneral information is at http://alvinlee.bio.uniroma1.it/praline/about, where we provide a detailed description of the datasets and the tools employed in the database. Data provided in PRALINE are available at http://alvinlee.bio.uniroma1.it/praline/downloads. The tutorial is at http://alvinlee.bio.uniroma1.it/praline/tutorial.

bioinformatics↗

bioRxiv2022

Reconstructing protein interactions at enhancer-promoter regions in prostate cancer

Here we introduce Promoter-ENhancer-GUided Interaction Networks (PENGUIN), a method to uncover protein-protein interaction (PPI) networks at enhancer-promoter contacts. By integrating H3K27ac-HiChIP data and tissue-specific PPI information, PENGUIN enables cluster enhancers-promoter PPI networks (EPINs) and pinpoint actionable factors. Validating PENGUIN in cancer (LNCaP) and benign (LHSAR) prostate cell lines, we observed distinct CTCF-enriched clusters, which identifies diverse chromatin conformations. In LNCaP, we found an EPIN cluster enriched with oncogenes and prostate cancer-associated SNPs. We uncovered a total of 208 SNPs in LNCaP EPINs and used CRISPR/Cas9 knockout and RNAi screens to confirm their relevance. PENGUINs application in prostate cancer demonstrates its potential for studying human diseases. The approach allows exploration in different cell types and combinations of GWAS data, offering promising avenues for future investigations. In conclusion, PENGUIN provides valuable insights into the interplay between enhancer-promoter interactions and PPI networks, facilitating the identification of relevant genes and potential intervention targets.

cancer biology↗

bioRxiv2022

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in non-coding RNAs

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress-granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also non-coding RNAs. Here, we built the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic non-coding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data. We found that A-to-I editing is linked to alteration of interaction sites with proteins involved in phase-separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive.

biochemistry↗

bioRxiv2022

The RNA-Binding Protein SAM68 regulates cardiomyocyte differentiation by enhancing Gata4 translation

The signal transduction and activation of RNA (STAR) family is composed of RNA-binding proteins (RBPs) that play a central role in mammalian development. Nonetheless, the functions and modes of action that STAR proteins have in lineage specification are still poorly understood. Here, we characterized the role of STAR proteins SAM68 and QUAKING (QKI) in pluripotency and differentiation by performing their depletion through CRISPR-Cas9 in mouse embryonic stem cells (mESCs). Combining RNA-sequencing, ribosome profiling and advanced computational predictions, we found that both SAM68 and QKI regulate the mESCs self-renewal and are indispensable for cardiomyocyte differentiation. At the molecular level, we discovered that SAM68 and QKI antagonistically control the expression of cardiogenic factors. Our calculations indicated that SAM68, unlike QKI, binds the cardiogenic-specific transcription factor Gata4 in a region spanning nucleotides 500 to 1000 of the mRNA corresponding to part of the 5 untranslated region and the first exon. We validated the predictions by electrophoretic mobility shift assay and RNA immunoprecipitation showing that SAM68 controls the translation of Gata4 during mESCs differentiation towards the cardiomyocyte lineage.

molecular biology↗

bioRxiv2021

Aggregation is a context-dependent constraint on protein evolution

Solubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution. In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed in S. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed in E. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function. Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in different cellular contexts.

bioinformatics↗

bioRxiv2020

RNA-Binding and Prion Domains: The Yin and Yang of Phase Separation

Biomolecular condensates are membrane-less organelles mainly composed of proteins and RNAs that organize intracellular spaces and regulate biochemical reactions. The ability of proteins and RNAs to phase separate is encoded in their sequences, yet it is still unknown which domains drive the process and what are their specific roles. Here, we systematically investigated the human and yeast proteomes to find regions promoting biomolecular condensation. Using advanced computational models to predict the phase separation propensity of proteins, we designed a set of experiments to investigate the contributions of Prion-Like Domains (PrLDs) and RNA-Binding Domains (RBDs). We found that while just one PrLD is sufficient to drive protein condensation, multiple RBDs are needed to modulate the dynamicity of the assemblies. In the case of stress granule protein Pub1 we show that the PrLD promotes sequestration of protein partners and the RBD confers liquid-like behaviour to the condensate. Our work sheds light on the fine interplay between RBDs and PrLD to regulate formation of membrane-less organelles, opening up the avenue for their manipulation.

molecular biology↗