Biology subjects

Miglionico, P.

Publications and source records attributed to Miglionico, P..

10 recordsLinked to original sources

bioRxiv2026

Atlantis: An integrative database for human proteome structural and functional sites

Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/.

bioinformatics↗

bioRxiv2026

GABAergic Circuit Activation Induces a Therapeutically Responsive State for anti-PD-L1 Immunotherapy in Glioblastoma

Glioblastoma (GBM) disrupts cortical excitatory-inhibitory balance and establishes an immunosuppressive microenvironment that limits therapeutic efficacy. Whether restoring inhibitory signaling can restrain glioma progression and improve responsiveness to immune checkpoint blockade remains unknown. Peritumoral parvalbumin-positive (PV+) interneurons were bidirectionally manipulated by chemogenetics in orthotopic GL261 gliomas to assess tumor growth and neurological function. GABAB signaling was pharmacologically activated with baclofen in GL261 and CT-2A models and combined with anti-PD-L1 blockade in GL261. Therapeutic response, survival, tumor rechallenge, and early myeloid remodeling were evaluated. Human GBM single-cell transcriptomic data were analyzed to examine the relationship between GABAergic and PD-L1 intercellular signaling. PV activation transiently restrained glioma growth, reduced tumor proliferation and preserved cortical function, whereas PV+ silencing increased seizure susceptibility and neurological impairment without accelerating tumor growth. Baclofen monotherapy did not affect survival, whereas its combination with anti-PD-L1 immunotherapy induced complete tumor eradication in 66% of GL261-bearing mice, prolonged survival, and conferred durable protection against tumor rechallenge. Combination therapy also altered the proportions of Arg1+ and CD11c+ cells within the intratumoral F4/80+ compartment. Human single-cell analysis revealed a shared myeloid-centered communication axis linking GABAB and PD-L1 signaling. These findings identify GABAergic signaling as a modulator of GBM progression and demonstrate that combining baclofen with anti-PD-L1 induces durable tumor regression, and prolongs survival in the GL261 model, supporting a neuro-immune framework for combining GABAergic modulation with immunotherapy.

cancer biology↗

bioRxiv2026

Evolutionary divergence of LRRK2 interaction domains contributes to human- and mouse-specific protein interaction networks

Leucine-rich repeat kinase 2 (LRRK2) is a complex multidomain protein whose catalytic and protein-protein interaction domains regulate a wide range of cellular processes. To investigate whether evolutionary divergence of these domains contributes to species-specific differences in LRRK2 biology, we combined phylogenetic, sequence, interactome and structural analyses of human and mouse LRRK2. Phylogenetic analysis revealed that the catalytic core predates the acquisition of the N-terminal and C-terminal protein-protein interaction domains during LRRK2 evolution. Accordingly, despite the high overall sequence similarity between human and mouse LRRK2, sequence divergence was not uniformly distributed across the protein but was concentrated within protein-protein interaction domains, whereas the catalytic ROC-COR- kinase core displayed markedly higher conservation. Consistent with this pattern, comparison of curated human and mouse interactomes revealed substantial differences in protein interaction networks and associated biological pathways. Structural modelling of a subset of interactors further showed that predicted interaction interfaces are enriched for residues that differ between the two species, providing a structural rationale for altered interaction specificity. Together, these findings support the view that evolutionary divergence of LRRK2 protein-protein interaction domains contributes to species-specific interactome organization. These results provide an evolutionary framework for interpreting differences between human and mouse LRRK2 and highlight the importance of considering species-specific interaction networks when translating findings from experimental models.

bioinformatics↗

bioRxiv2026

Computed atlas of the human GPCR-G protein signaling complexes

Experimental mapping of G protein-coupled receptors (GPCR)-G protein signaling coupling has illuminated hundreds of receptors, yet the coupling specificity of a large fraction of this large receptor family remains unknown, thereby preventing the development of new GPCR-targeting therapies. Here, we used AlphaFold3 (AF3) to predict the 3D structures of the human GPCRome in complex with heterotrimeric G proteins. We used experimental GPCR-G protein binding data to show that AF3 predictions significantly discriminate between positive and negative binders, and used 3D structural features to train a machine learning (ML) algorithm to predict coupling potency. Interpretation of the ML model helped discriminate universal features governing the strength of G protein coupling from those determining binding specificity. We computationally illuminated the coupling preferences of 180 non-olfactory GPCRs (non-OR) with previously unreported transduction mechanisms and experimentally validate the predicted couplings for multiple previously uncharacterized GPCRs, including QRFPR, GPR50, GPR37, GPR37L1 and GPRC5A. Our predictions established that Gi/o is the most prevalent coupling among non-OR GPCRs, which is often co-occurring with Gq/11 and, to a lesser extent, G12/13 signaling. Gs coupling is less common and restricted to specific clusters within the non-OR GPCRome phylogeny, likely due to stricter structural requirements for its binding. We also computed G protein complexes for over 400 ORs, establishing Gs as the most prevalent coupling. ORs are predicted to bind to Gs with a simpler interface compared to non-ORs, ultimately leading to energetically less stable complexes. Additionally, we predict recurrent bindings to Gq/11 and Gi/o proteins for ORs, suggesting potentially novel ORs signaling mechanisms. We exploited the GPCRome coupling atlas to interpret healthy and cancer expression data, revealing the coupling of most GPCR-G protein co-expressed pairs. This analysis highlights a richer coupling repertoire in healthy tissues compared to cancer, likely reflecting the high signaling requirements of specialized normal cell functions, which are lost in most cancer cells due to their de-differentiated state or under cancer selection processes. In summary, this study provides the first computational 3D atlas of the human GPCR-G protein transductome, thereby illuminating the signaling mechanisms of neglected GPCR classes and providing the basis for interpreting omics datasets from a myriad of pathological conditions, thus enabling the development of novel precision therapeutics.

bioinformatics↗

bioRxiv2025

Communication breakdown and evolution of the cancer cell

1We studied cell-cell interactions (CCIs) in large-scale transcriptomic datasets, which showed higher co-expression in cancer compared to healthy tissues. CCIs are more co-expressed than any other type of intracellular interaction and, likewise, they are the protein-protein interaction (PPI) class that is most co-evolved in sequenced genomes. Similar trends of stricter regulation and evolutionary pressure are observed when comparing extracellular versus intracellular interactions mediated by G protein Coupled Receptors (GPCRs), whose ligand interactions are also characterized by a higher mutational burden in later tumor stages when considering somatic mutations associated with tumor clonal evolution. CCIs undergo the most extensive rewiring of their tumor co-expression networks relative to healthy tissues, more so than any other PPI type, with a set of CCI hubs highly conserved across multiple tumor tissues, and a higher diversity on healthy ones. Cancer rewiring is also associated with the formation of recurrent circuits of co-expressed CCI pairs, represented by enriched network motifs such as triad or tetrad cliques. These act as integrative hotspots to facilitate the crosstalk of distinct processes and the interaction of the cancer cell with its tumor microenvironment (TME). Remarkably, many CCI circuits are significantly associated with patient survival and are predictive of patient response to immunotherapy. CCI circuits mapping to allograft rejection and inflammatory response inform immunotherapy response prediction, while those related to epithelial-mesenchymal transition are associated with poorer prognosis. Overall, we show that CCIs expression signatures could be effectively exploited to stratify patients and, at the same time, they highlight new combination therapeutic opportunities in personalized medicine settings.

bioinformatics↗

bioRxiv2024

Prediction and discovery of protein-protein direct interactions and stable complexes based on gene co-expression and co-evolution

In this study we employed a data-driven approach to explore the evolutionary and genetic determinants of protein direct interactions and stable complex formation in the human proteome. We found that simple co-evolutionary and co-expression metrics are highly informative of direct interactions and stable complexes. We used this information to train supervised binary classifiers to predict interactions either directly involved in the formation of a complex (as annotated in IntAct) or forming stable complexes (from Complex Portal). In the former task, our model was able to discriminate direct interactions with an AUROC=0.813, while in the latter it discriminated interaction forming stable complexes with an AUROC=0.964. In both cases, our approach outperformed String, that we employed as a baseline. Feature importance analysis revealed different contributions to the prediction of these distinct interaction types. Co-evolutionary features, in particular those referred to protein domains involved in interaction interfaces, are more important to discriminate direct interactions. On the other hand, co-expression features contributed more to the prediction of stable complexes. From these pairwise predictions we generated a proteome-wide network that we clustered to assess the recovery of known complexes from Complex Portal within network communities. We were able to recover known complexes at a higher accuracy compared to other approaches. In conclusion, we propose a new method able to discriminate direct interactions as well as forming stable complexes. This method can be used to stratify molecular interaction networks, as well as to perform discovery of new functional complexes at a proteome-wide scale.

bioinformatics↗

bioRxiv2024

Proximity proteomics reveals a co-evolved LRRK2-regulatory network linked to centrosomes

Leucine-rich repeat kinase 2 (LRRK2) not only plays a vital role in familial forms of Parkinsons disease (PD) but also represents a risk factor for idiopathic PD. Its multi-domain architecture enables fine-tuned regulation of its biological function by orchestrating intra- and intermolecular interactions. Here, we present BioID proximity proteomes of LRRK2 that reveal new interactors, which we further characterize using a novel evolutionary and structural bioinformatics pipeline. Co-evolutionary analysis of the protein-protein interaction network identifies a structural and functional module enriched in cytoskeletal components associated with the centrosome and microtubules. In addition, structural modelling of binary interactions using AlphaFold-Multimer reveals distinct groups of interactors that engage LRRK2 in a manner dependent on specific conformations and epitopes. Furthermore, we identify distinct changes in the LRRK2 proximity proteome that are induced by the type I kinase inhibitor MLi-2 or by co-expression of the LRRK2 upstream effector RAB29. Depending on its activity state and conformation, these protein-protein interactions link LRRK2 to defined cellular sub-compartments, including centriolar satellites and vesicular sub-compartments. SummaryThis study defines a comprehensive, LRRK2 interaction network by combining proximity proteomics with evolutionary and structural analyses, revealing how LRRK2 conformation and activity state dictate distinct protein-protein interactions. These findings link LRRK2 to conserved centrosomal, cytoskeletal, and vesicular pathways, providing mechanistic insight into its role in Parkinsons disease. Key resultsO_LICentrosome-linked LRRK2 network: BioID combined with co-evolutionary analysis identifies a highly conserved module enriched in centrosomal and microtubule-associated proteins, including CYLD as a top co-evolved interactor. C_LIO_LIConformation-specific interactions: AlphaFold-Multimer modelling reveals distinct classes of LRRK2 interactors that bind different domains and conformations ("locked" vs. "unlocked"), corresponding to specific cellular functions. C_LIO_LIState-dependent rewiring of the interactome: The type I inhibitor MLi-2 selectively redirects LRRK2 to centriolar satellite proteins, whereas RAB29 co-expression shifts LRRK2 interactions toward vesicular and lysosomal pathways. C_LI

neuroscience↗

bioRxiv2024

Learning and actioning general principles of cancer cell drug sensitivity

High-throughput screening platforms for the profiling of drug sensitivity of hundreds of cancer cell lines (CCLs) have generated large datasets that hold the potential to unlock targeted, anti-tumor therapies. In this study, we leveraged these datasets to create predictive models of cancer cells drug sensitivity. To this aim we trained explainable machine learning algorithms by employing cell line transcriptomics to predict the growth inhibitory potential of drugs. We used large language models (LLMs) to expand descriptions of the mechanisms of action (MOA) for each drug starting from available annotations, which were matched to the semantically closest pathways from reference knowledge bases. By leveraging this AI-curated resource, and the interpretability of our model, we demonstrated that pathways enriched for genes crucial for prediction often matched known drug-MOAs and essential genes, suggesting that our models learned the molecular determinants of drug response. Furthermore, we demonstrated that by incorporating only LLM-curated genes associated with MOAs, we enhanced the predictive accuracy of our drug models. To enhance translatability to a clinical setting, we employed a pipeline to align bulk RNAseq from CCLs, used for training the models, to those from patient samples, used for inference. We proved the effectiveness of our approach on TCGA samples, where patients best scoring drugs matched those prescribed for their cancer type. We further showed its usefulness by predicting and experimentally validating effective drugs for the patients of two highly lethal solid tumors, i.e. pancreatic cancer and glioblastoma. In summary, our method facilitates the inference and interpretation of cancer cell line drug sensitivity and holds potential to effectively translate them into new cancer therapeutics. Highlights-Interpretable drug-response prediction models were trained on large scale pharmacogenomics data sets (i.e. GDSC and PRISM). -Large language models were used to enhance the curation of biological pathways associated to drugs MOA -Unbiased interpretation of the models demonstrated learning of drug MOAs and gene essentiality -Inference of TCGA cohort samples recovered mono- and combination cancer drug prescriptions and indicate potential repurposing candidates. -Drug candidates predicted from bulk RNAseq samples of pancreatic cancer and glioblastoma were experimentally validated.

bioinformatics↗

bioRxiv2023

Biophysical analysis reveals autophosphorylation as an important negative regulator of LRRK2 dimerization

Leucine-rich repeat kinase 2 (LRRK2) is a large, multi-domain protein which is associated with Parkinsons disease. Although high-resolution structures of LRRK2 are available, little is known about the complex dynamics behind the inter-domain regulation of LRRK2 and its perturbation by pathogenic variants. Previous studies have demonstrated that LRRK2 goes through an oligomerization cycle at the membrane, however it remains unclear in which form it exerts its kinase activity. Moreover, the LRRK2 monomer-dimer equilibrium and associated functional implications at a molecular level also need further investigation. In the present work, we used a multi-faceted approach to better understand LRRK2 oligomerization and suggest a functional model of how LRRK2 interacts with its substrates. To this end, we combined nano differential scanning calorimetry and mass photometry with molecular modelling. The thermal analysis resulted in a multistep denaturation profile, elucidating novel insights into the composite structural organization of the multi-domain protein LRRK2. Furthermore, LRRK2 shows a remarkable thermal stability, confirming its oligomeric nature. By using mass photometry, we could observe a monomer-dimer equilibrium which is altered by R1441G, a pathogenic variant within the Roc-COR interface. Most importantly, we could demonstrate that autophosphorylation induces LRRK2 monomerization, indicating a novel intramolecular feedback mechanism. Finally, we investigated the interaction of LRRK2 with its substrate, RAB10 by integrative computational modelling. The resulting models suggest that the monomeric form of LRRK2 is the favored protein conformation for the interaction with its substrate, leading to an increasing interest in the monomer-dimer equilibrium as a possible intervention point for the pathology.

biochemistry↗

bioRxiv2022

GPCRome-wide structural analysis of G-protein-coupling selectivity

We present a comprehensive computational analysis of available 3D GPCR-G-protein complexes to inspect the structural determinants of G-protein-coupling selectivity. Analysis of the residue contacts at interaction interfaces has revealed a network of secondary structure elements recapitulating known structural hallmarks determining G-protein-coupling specificity, including TM5, TM6 and ICLs. We coded interface contacts into generic-number fingerprints to reveal specific coupling-determinant positions. Clustering of Gs vs Gi complexes is best achieved when considering both GPCR and G-protein contacting residues rather than separated representations of the interaction partners, suggesting that coupling specificity emerges as contextual residue interactions at the interface. Interestingly, Gs-GPCR complexes contain a higher number of contacts than Gi/o-GPCR complexes, likely caused by overall higher conservation and structural constraint on the Gs interface. In contrast, Gi/o proteins adopt a wider number of alternative docking poses on cognate receptors, as assessed via structural alignments of representative 3D complexes. Furthermore, binding energy calculations demonstrate that distinct structural properties of the complexes contribute to higher stability of Gs than Gi/o complexes. AlphaFold2 predictions of experimental binary complexes confirmed several of these structural features and allowed us to augment the structural coverage of poorly characterized complexes (e.g. G12/13). We propose that the structural properties of different G-protein complexes, such as structural restraining of Gs compared to Gi/o ones, could be instrumental in fine-tuning their activation and downstream signaling mechanisms. Highlights-Comprehensive structural bioinformatics analysis of available GPCR-G-protein complexes captures common as well as group-specific structural features responsible of receptor-G-protein recognition -Distinct contact patterns explain different docking modes of Gi/o vs Gs complexes, the latter being characterized by higher enrichment of characteristic contacts and lower structural variability suggestive of higher interface conservation. -Structural hallmarks are associated with different estimated binding energies, which mainly discriminates Gs versus Gi/o couplings, but which also point to class-dependent differences (e.g. Class A vs Class B) in binding the same transducer (Gs)

bioinformatics↗