Biology subjects

Matic, M.

Publications and source records attributed to Matic, M..

6 recordsLinked to original sources

bioRxiv2026

De novo macrocyclic peptide discovery produces potent class A GPCR ligands with diverse pharmacology

G protein-coupled receptors (GPCRs) represent one of the largest protein families of therapeutic importance, as over one third of all currently approved drugs target GPCRs. A growing proportion of GPCR drugs are peptides. However, class A GPCRs (over 80% of clinically studied targets) are underexploited as peptide targets, presumably since their only structured domain is the core, seven-helix, integral membrane fold presenting a limited solvent-exposed surface area. Screening libraries of de novo macrocyclic peptides have allowed for the targeting of otherwise intractable proteins, but their potential to generate selective ligands for class A GPCRs has not been demonstrated. Here, we used random nonstandard peptides integrated discovery (RaPID) to identify de novo macrocyclic peptide ligands targeting the chemokine receptor CXCR4. CXCR4 regulates cell migration, immune surveillance, and inflammation, making it an important therapeutic target for multiple diseases. We discovered novel, potent CXCR4-selective antagonists binding within the orthosteric pocket with nanomolar affinity. The most active peptides effectively inhibited receptor signaling, suppressed chemokine-driven cell migration, and blocked CXCR4-driven HIV entry. Our results show that even the structurally minimal class A GPCRs are viable targets for de novo cyclic peptide discovery, highlighting the potential for uncovering ligands towards other members of the GPCR superfamily.

pharmacology and toxicology↗

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

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

Pharmacologic NLRP3 Inhibition Modulates Parkinson's Disease-Associated Microglial Transcriptomic Signatures and Mitigates α-Synuclein-Triggered Neurodegeneration

BackgroundParkinsons disease (PD), the second most common neurodegenerative disorder after Alzheimers disease, and the rare disorder multiple system atrophy (MSA), are both characterized by intracellular accumulation of -synuclein fibrils and early, sustained microglial reactivity in parallel to the neurodegeneration. Activation of the NLRP3 inflammasome in disease-associated reactive microglia is increasingly recognized as a key pathogenic driver and a promising therapeutic target in synucleinopathies. Dapansutrile (OLT1177(R)) is a selective, orally bioavailable NLRP3 inhibitor with a favorable safety profile in clinical trials for non-neurological indications. Here, we evaluated the therapeutic potential of dapansutrile in preclinical models of PD and MSA and explored the predictive and translational value of its effects. MethodsTwo established mouse models of synucleinopathy with nigral neurodegeneration were employed: the -synuclein preformed fibril (PFF) propagation model and the transgenic PLP--syn model expressing human wild-type -synuclein in oligodendrocytes. Pharmacokinetic analyses assessed plasma and brain exposure after oral administration. The efficacy of six-month dapansutrile treatment was examined in both preventive (post-PFF injection) and therapeutic (PLP--syn mice) paradigms, using behavioral, histopathological, and molecular readouts. Transcriptomic profiling of striatal and midbrain microglia identified differentially expressed genes (DEGs) associated with treatment and compared them with post-mortem transcriptomic signatures of disease-associated microglia in PD patients. Plasma IL-18 and neurofilament light chain (NfL) levels were evaluated as translational biomarkers. ResultsChronic oral dapansutrile treatment at clinically relevant doses improved motor performance, reduced -synuclein inclusions, attenuated gliosis, and mitigated nigral neurodegeneration in both models. Microglial transcriptomic analyses revealed that dapansutrile reversed key transcriptional signatures characteristic of PD-associated reactive microglia. Moreover, plasma IL-18 and NfL levels correlated with neuropathological and functional outcomes, supporting their potential as biomarkers of target engagement and treatment efficacy. ConclusionsThese data identify chronic NLRP3 activation as a shared and targetable mechanism in PD and MSA and highlight dapansutrile as a CNS-penetrant, clinically advanced candidate for disease modification in -synucleinopathies. The observed transcriptomic reprogramming of microglia and the parallel changes in blood biomarkers provide a strong translational bridge to clinical development.

neuroscience↗

bioRxiv2023

The landscape of cancer rewired GPCR signaling axes

We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes, which revealed that multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulated pathway activity signatures from metabolomics datasets, providing valuable surrogate information for GPCRs responding to organic ligands. We found that several GPCRs signaling components were significantly associated with patient survival in a cancer type-specific fashion. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many pairs involving receptor- biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including e.g., muscarinic, adenosine, 5-hydroxytryptamine and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it). SignificanceComprehensive analysis of GPCR extracellular network in cancer transcriptomics datasets reveals signaling axes associated to patient survival, whose targeting is associated with growth inhibition in cancer cell lines drug sensitivity assays.

bioinformatics↗

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↗