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Biomedical research benefits from the rapid growth and diversity of experimentally detected protein–protein interactions (PPIs) by gaining important biological insights. However, increasingly dense PPI networks can be challenging to interpret and apply. The 2025 update of the Integrated Interactions Database (IID) enhances accessibility and utility through several new features. We identify and incorporate network structural components from co-purified protein sets, as well as curated and predicted complexes, enabling users to explore network organization Show less

Computational metabolomics will be established in drug discovery and research on complex biological networks. This field of research enhances the detection of metabolic biomarkers and the prediction of molecular interactions by combining multiscale analysis with in silico and molecular docking methods. These include nuclear magnetic resonance, mass spectrometry, and innovative bioinformatics, which enable the accurate generation and characterization of metabolomes. Molecular docking is a crucial tool for simulating the interaction between ligands and receptors, thereby facilitating the identification of potential therapeutics. It also discusses the potential of metabolomics to inform drug modes of action, from pharmacokinetics to forecasting toxicity, thereby streamlining drug development pipelines. We highlight applications in anticancer, antimicrobial, and antiviral drug discovery and explain how these computational models can accelerate target validation and enhance the accuracy of therapeutic strategies. In addition, this review addresses the current challenges and future directions for computational techniques in conjunction with experimental data to advance personalized medicine. In conclusion, this review aims to highlight the prospective approaches of computational metabolomics and molecular docking that identify evolutionary adaptive metabolisms of multiscale biological systems through their synergistic utilization to overcome the key hurdles involved in both drug discovery and metabolomic research. Show less

Ferroptosis is a regulated form of cell death characterized by iron-dependent lipid peroxidation. It plays a crucial role in various pathological conditions, including neurodegenerative diseases, cancer, ischemia–reperfusion injury, and organ failure. This review systematically explores the key mechanisms underlying ferroptosis, including polyunsaturated fatty acidcontaining phospholipid (PUFA-PL) peroxidation, iron metabolism, and mitochondrial dysfunction. Additionally, we summarize major endogenous ferroptosis defense systems, including the SLC7A11-glutathione (GSH)-glutathione peroxidase Show less

Ferroptosis is evolving as a highly promising approach to combat difficult-to-treat tumour entities including therapy-refractory and dedifferentiating cancers^1-3. Recently, ferroptosis suppressor protein-1 (FSP1), along with extramitochondrial ubiquinone or exogenous vitamin K and NAD(P)H/H⁺ as an electron donor, has been identified as the second ferroptosis-suppressing system, which efficiently prevents lipid peroxidation independently of the cyst(e)ine-glutathione (GSH)-glutathione peroxidase 4 (GPX4) axis^4-6. To develop FSP1 inhibitors as next-generation therapeutic ferroptosis inducers, here we performed a small molecule library screen and identified the compound class of 3-phenylquinazolinones (represented by icFSP1) as potent FSP1 inhibitors. We show that icFSP1, unlike iFSP1, the first described on-target FSP1 inhibitor⁵, does not competitively inhibit FSP1 enzyme activity, but instead triggers subcellular relocalization of FSP1 from the membrane and FSP1 condensation before ferroptosis induction, in synergism with GPX4 inhibition. icFSP1-induced FSP1 condensates show droplet-like properties consistent with phase separation, an emerging and widespread mechanism to modulate biological activity⁷. N-terminal myristoylation, distinct amino acid residues and intrinsically disordered, low-complexity regions in FSP1 were identified to be essential for FSP1-dependent phase separation in cells and in vitro. We further demonstrate that icFSP1 impairs tumour growth and induces FSP1 condensates in tumours in vivo. Hence, our results suggest that icFSP1 exhibits a unique mechanism of action and synergizes with ferroptosis-inducing agents to potentiate the ferroptotic cell death response, thus providing a rationale for targeting FSP1-dependent phase separation as an efficient anti-cancer therapy. Show less

Three tridentate Schiff base ligands were synthesized from the reactions between 2-picolylamine and salicylaldehyde derivatives (3-ethoxy (OEt), 4-diethylamino (NEt₂) and 4-hydroxy (OH)). Complexes with the general formula Pt(N^N^O)Cl were obtained from reactions between the ligands and K₂PtCl₄. The ligands and their complexes were characterized by NMR spectroscopy, mass spectrometry and elemental analysis. Further confirmation of the structure of Pt-OEt was achieved by single-crystal X-ray diffraction. The DMSO/chlorido exchange process at Pt-OEt was investigated by monitoring the change in conductivity, revealing very slow dissociation in DMSO. Moreover, solvent/chlorido exchange for Pt-OEt and Pt-NEt₂ were investigated by NMR spectroscopy in DMSO and DMSO/D₂O; Pt-NEt₂ forms an adduct with DMSO while Pt-OEt forms adducts with DMSO and water. The DNA-binding behaviour of the platinum(ii) complexes was investigated by two techniques. Pt-NEt₂ has the best apparent binding constant. The intercalation mode of interaction with ct-DNA was suggested by molecular docking studies and the increase in the relative viscosity of ct-DNA with increasing concentrations of the platinum(ii) complexes. However, the gradual decrease in the relative viscosity over time at constant concentration of platinum(ii) complexes indicated a shift from intercalation to a covalent binding mode. Anticancer activities of the ligands and their platinum(ii) complexes were examined against two cell lines. The platinum(ii) complexes exhibit superior cytotoxicity to that of their ligands. Among the platinum(ii) complexes, Pt-OEt possesses the best IC₅₀ against both cell lines, its cytotoxicity being comparable to that observed for cisplatin. Cell cycle arrest in the HepG2 cell line upon treatment with Pt-OEt and Pt-NEt₂ was investigated and compared to that of cisplatin; the change in the cell accumulation patterns supports the presumption of an apoptotic cell death pathway. The optimized structures of the B-DNA trimer adducts with the platinum complexes showed hydrogen-bonding interactions between the ligands and nucleobases, affecting the inter-strand hydrogen bonding within the DNA, and highlighting the strong ability of the complexes to induce conformational changes in the DNA, leading to the activation of apoptotic cell death. In summary, the current study demonstrates promising new anticancer platinum(ii) complexes with highly flexible tridentate ligands; the functional groups on the ligands are important in tuning their DNA binding/anticancer properties. Show less

Computational modeling of inhibitors for metalloenzymes in virtual drug development campaigns has proven challenging. To overcome this limitation, a technique for predicting the binding pose of metal-binding pharmacophores (MBPs) is presented. Using a combination of density functional theory (DFT) calculations and docking using a genetic algorithm, inhibitor binding was evaluated in silico and compared with inhibitor-enzyme cocrystal structures. The predicted binding poses were found to be consistent with the cocrystal structures. The computational strategy presented represents a useful tool for predicting metalloenzyme-MBP interactions. Show less

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models. Academic Editors: Kyriakos Kachrimanis, David Barlow, Jakub Show less