Sandro Cosconati

GreenSound-PNAs combines ultrasound-assisted PNA synthesis with greener solvents and reagents, enabling a more sustainable, scalable, and efficient platform for PNA production.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a dense desmoplastic tumor microenvironment (TME) that limits drug penetration and promotes immune evasion. Effective treatment, therefore, requires simultaneous modulation of multiple signaling pathways. Here, we describe a directed polypharmacological strategy to identify dual modulators of c-MET and Smoothened (SMO), aiming to disrupt the protective stroma through SMO inhibition while directly suppressing tumor cell survival via c-MET targeting. An AI-guided virtual screening workflow combining the machine-learning platform PyRMD, trained on known c-MET and SMO ligands, with structure-based molecular docking was applied to a library of over 9 million compounds. This approach led to the identification of compound 21 , an aminopyrimidine-benzamide-phenoxyquinoline derivative, as a dual c-MET/SMO inhibitor. Biochemical and cellular studies demonstrated that compound 21 selectively binds the SMO orthosteric site (pKi = 5.60), inhibits agonist-induced GLI (Glioma-associated oncogene) signaling (pIC ₅₀ = 5.50), and potently suppresses c-MET kinase activity (pIC ₅₀ = 6.94). Western blot analyses further revealed that compound 21 promotes ubiquitin–proteasome-mediated degradation of c-MET, eliminating receptor availability and limiting compensatory resistance signaling. In 3D heterotypic models comprising MIAPaCa2 pancreatic cancer cells and CAF154-hTERT fibroblasts, dual inhibition of SMO-mediated stromal support and c-MET-driven tumor progression resulted in greater cytotoxicity than the combination of the selective inhibitors Sonidegib and PHA-665752. Overall, compound 21 overcomes stromal-mediated resistance, enhances tumor cell death, and validates dual SMO/c-MET targeting as a promising single-agent therapeutic strategy for PDAC. One Sentence Summary: An AI-identified dual SMO/c-MET inhibitor overcomes stromal resistance and degrades c-MET to suppress pancreatic cancer.

Targeting protein–protein interactions (PPIs) with small molecules is historically challenging due to shallow, solvent-exposed interfaces that lack classical binding pockets. Furthermore, employing traditional structure-based virtual screening (SBVS) across ultralarge chemical spaces to find novel chemotypes imposes prohibitive computational bottlenecks. Here, we report the first prospective, real-world application of the PyRMD2Dock platform, an AI-enforced SBVS workflow that integrates machine learning and standard docking available within the PyRMD Studio suite. To target the structurally demanding immune receptor CD28, a chemically diverse subset of 2.4 million molecules from the Enamine REAL Diversity Space was docked into a cleft adjacent to the canonical ligand interface. These data were used to train 672 classification models, and the optimized model rapidly screened the remaining ∼46 million compounds. Following interaction filtering and clustering, 232 highly prioritized ligands were identified. Experimental validation of 150 purchased candidates yielded a strong hit rate, identifying multiple direct CD28 binders. Lead compounds 100 and 104 exhibited submicromolar affinity (Kd = 343.8 nM and 407.1 nM, respectively), potent CD28-CD80 disruption, and functional blockade in cellular reporter assays. Furthermore, these compounds successfully reduced cytokine secretion in primary human tumor-PBMC and epithelial tissue coculture models. This study validates PyRMD2Dock as a highly scalable, effective protocol for mining massive chemical libraries to discover small-molecule modulators of challenging immune receptor interfaces.

GPR183 is a chemotactic GPCR involved in immune cell migration. Using AI-driven virtual screening and biophysical assays, we identify inverse agonists. From 70 compounds and a subsequent hit expansion, compound 78 emerges as a potent inhibitor of constitutive and agonist-induced Gi signaling as well as β-arrestin2 recruitment. Binding within the receptor core is confirmed by a conformational biosensor, molecular dynamics simulations, and mutagenesis. The compound also blocks agonist-driven migration of peripheral blood mononuclear cells ex vivo with very high potency. Additionally, our analyses reveal key features of GPR183 activation, highlighting tyrosine 260 (Y260 ^6.51 ) in transmembrane helix 6 as critical. Mutation of this residue alters compound 78 efficacy as well as induces receptor signaling bias, indicating a switch mechanism. Overall, this study provides tools to probe GPR183 function, identifies a chemical scaffold, and advances understanding of receptor activation, supporting therapeutic targeting in inflammatory, autoimmune, and cancer-related diseases.

Artificial Intelligence (AI) has become a cornerstone of modern drug discovery. Yet, its widespread adoption is often hindered by the steep learning curve of command-line tools and the prevalence of overfitting due to improper data splitting. Here, we introduce PyRMD Studio, a major update to our open-source virtual screening (VS) software, designed to address these challenges through enhanced accessibility, scientific rigor, and computational efficiency. This new version features a comprehensive graphical user interface (GUI) compatible with both Linux and Windows, democratizing access to sophisticated AI workflows and allowing nonexpert users to perform both ligand-based and structure-based VS without coding expertise. To ensure reliable performance estimation, PyRMD Studio implements a rigorous validation strategy based on Butina clustering, which separates structural clusters between training and test sets to mitigate data leakage and prevent overoptimistic benchmarking. Furthermore, extensive code optimizations and vectorization have resulted in an approximate 3.3-fold increase in screening speed compared to the previous release. By combining a user-friendly environment with robust validation protocols, PyRMD Studio provides a streamlined and reliable toolkit for high-throughput virtual screening campaigns. PyRMD Studio is freely available at https://github.com/sandrocosconati/PyRMD-Studio.

Rhodesain is a cysteine protease that plays a key role in the life cycle of Trypanosoma brucei rhodesiense, an endemic parasite in sub-Saharan Africa and responsible for Human African Trypanosomiasis (HAT), a disease that can be fatal if not treated promptly. Due to the limitations associated with current HAT pharmacological therapy, the search for new targets for the development of antitrypanosomal agents is urgently needed; in this context, rhodesain represents a promising therapeutic target. In this study, new chalcones were synthesized and tested against rhodesain. Given their affinity for the trypanosomal cysteine protease (K_i values in the micromolar range), chalcone 1a was selected to evaluate its effect in combination with the nutraceutical curcumin. The Combination Index (CI) was calculated using Chou and Talalay’s method. The analysis of the CI calculated at different f_a values of enzyme inhibition for the combination curcumin + 1a showed promising results. For all f_a values, the CI is less than one, indicating a synergistic effect when chalcone 1a is combined with curcumin. In particular, at the most significant f_a value (0.90), corresponding to 90% of enzyme inhibition, the CI value is 0.1781, indicating a strong synergism between the synthetic drug and the nutraceutical. The combined use of curcumin and chalcone 1a led to an enhancement of rhodesain inhibitory activity, resulting in a strong synergistic effect and supporting further investigation of this combination.

Engineering protein stability is a powerful strategy across biotechnology and medicine, supporting a broad range of applications such as atomic structure determination, discovery of therapeutic molecules, biomanufacturing, diagnostic reagents, industrial biocatalysis, etc. However, achieving rapid and significant improvements has been historically challenging due to the vast mutational space and the complex interplay of sequence, structure, and function. Indeed, traditional experimental and computational methods often struggle to predict the impact of multiple mutations and effectively integrate diverse data types. To address these limitations, we developed StabLyzeGraph, a novel computational framework powered by Graph Neural Networks (GNNs) for protein mutational analysis and classification of stabilizing mutations. StabLyzeGraph represents proteins as graphs, integrating amino acid physicochemical properties, evolutionary conservation scores, and mapped three‐dimensional structural information. The framework consists of a Benchmarking module to evaluate performance, and a Screening module to identify and rank impactful mutations. Benchmarking across 23 diverse datasets demonstrated strong predictive performance, highlighting the GNN's ability to leverage integrated features. Mutational analysis enables the generation and probability scoring of single‐ and multi‐site mutants, demonstrating the model's capacity to classify beneficial combinations of mutants based on learned structural impact rather than mere mutation frequency. StabLyzeGraph also features a user‐friendly Graphical User Interface and demonstrates reasonable computational efficiency and scalability for exploring mutational landscapes. This tool provides a robust and versatile approach to accelerate the efficient discovery of stabilizing mutations with tailored properties and represents a step forward in rational protein design, poised to accelerate the creation of novel biologics with enhanced performance. StabLyzeGraph is freely available on GitHub ( https://github.com/cosconatilab/StabLyzeGraph ) as an open‐source tool.

GPR183 is a chemotactic G protein-coupled receptor implicated in immune cell migration and various diseases. Here, we report the discovery of novel GPR183 inverse agonists using an artificial intelligence-driven virtual screening approach combined with biophysical sensors including a first-in-class conformational biosensor. From an initial screen of 70 compounds and a hit expansion, a selected candidate - compound 78 - demonstrated potent inhibition of GPR183’s constitutive and agonist-stimulated Gi activation as well β-arrestin2 recruitment. The binding of the compound to the receptor core was confirmed by studies using a new conformational sensor based on intramolecular BRET, molecular dynamics, and site-directed mutagenesis experiments. Finally, the compound 78 was able to block agonist-induced migration of peripheral blood mononuclear cells ex vivo . This work provides a novel chemical scaffold for GPR183 modulation. It introduces a new tool for dissecting receptor function, paving the way for therapeutic exploration in inflammatory, autoimmune, and neoplastic disorders.

Background/Objectives: Rhodesain is a cysteine protease crucial for the life cycle of Trypanosoma brucei rhodesiense, a parasite that causes the lethal form of human African trypanosomiasis. For these reasons, rhodesain is considered an important target for the drug discovery process of novel antitrypanosomal agents. Methods: In the present work, we carried out a combination study of two novel synthetic nitriles, Nitrile 1 and Nitrile 2, with curcumin, the golden multitarget nutraceutical obtained from Curcuma longa L., which we demonstrated to inhibit rhodesain in a non-competitive manner. We calculated the combination index (CI) in both the combination studies by using the Chou and Talalay method. Results: Comparing the CI values of the combinations Nitrile 1 + curcumin and Nitrile 2 + curcumin, we assessed that the inhibitory effect of the combination Nitrile 2 + curcumin against rhodesain was much more potent than that of the other combination. At the IC₅₀ value, in the case of the combination Nitrile 1 + curcumin an additive effect occurred, while in the case of Nitrile 2 + curcumin, we observed a moderate synergism: at 99% of the effect, the synergism induced by the combination Nitrile 2 + curcumin was much stronger than the synergism promoted by the combination Nitrile 1 + curcumin (CI = 0.3843 vs 0.6622, respectively). Conclusions: The co-administration of dipeptide nitriles with curcumin enhances rhodesain inhibition through synergistic effects. Notably, Nitrile 2 + curcumin exhibits a stronger synergy at higher inhibition levels, indicating a greater therapeutic potential.

Trimethylamine N-oxide (TMAO) is an endogenous osmolyte produced by enzymatic reactions starting in the human gut, where microbiota release trimethylamine (TMA) from foods, and ending in the liver, where TMA is oxidized to TMAO by flavin-containing monooxygenase 3 (FMO3). While physiological concentrations of TMAO help proteins preserve their folding, high levels of this metabolite are harmful and promote oxidative stress, inflammation, and atherosclerosis. In humans, elevated levels of circulating TMAO predispose individuals to cardiovascular diseases and chronic kidney disease and increase mortality risk, especially in the elderly. How TMAO exerts its negative effects has been only partially elucidated. In hypertensive rats, the eNOS substrate L-arginine and Taurisolo^®, a nutraceutical endowed with TMAO-reducing activity, act synergistically to reduce arterial blood pressure. Here, we investigate the molecular mechanisms underpinning this synergism and prove that TMAO, the target of Taurisolo^®, acts as direct inhibitor of endothelial nitric oxide synthase (eNOS) and competes with L-arginine at its catalytic site, ultimately inhibiting NO production and acetylcholine (Ach)-induced relaxation in murine aortas.