Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

Age-associated osteosarcopenia is an unresolved syndrome characterized by the concomitant loss of bone (osteopenia) and skeletal muscle (sarcopenia) tissues increasing falls, immobility, morbidity, and mortality. Unbalanced resorption of bone in the remodeling process and excessive protein breakdown, especially fast type II myosin heavy chain (MyHC-II) isoform and myofiber metabolic shift, are the leading causes of bone and muscle deterioration in the elderly, respectively. Equisetum arvense (EQ) is a plant traditionally recommended for many pathological conditions due to its anti-inflammatory properties. Thus, considering that a chronic low-grade inflammatory state predisposes to both osteoporosis and sarcopenia, we tested a standardized hydroalcoholic extract of EQ in in vitro models of muscle atrophy [C2C12 myotubes treated with proinflammatory cytokines (TNFα/IFNγ), excess glucocorticoids (dexamethasone), or the osteokine, receptor activator of nuclear factor kappa-B ligand (RANKL)] and osteoclastogenesis (RAW 264.7 cells treated with RANKL). We found that EQ counteracted myotube atrophy, blunting the activity of several pathways depending on the applied stimulus, and reduced osteoclast formation and activity. By in silico target fishing, IKKB-dependent nuclear factor kappa-B (NF-κB) inhibition emerges as a potential common mechanism underlying EQ\'s anti-atrophic effects. Consumption of EQ (500 mg/kg/day) by pre-geriatric C57BL/6 mice for 3 months translated into: i) maintenance of muscle mass and performance; ii) restrained myofiber oxidative shift; iii) slowed down age-related modifications in osteoporotic bone, significantly preserving trabecular connectivity density; iv) reduced muscle- and spleen-related inflammation. EQ can preserve muscle functionality and bone remodeling during aging, potentially valuable as a natural treatment for osteosarcopenia.

For understanding a chemical compound\'s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.

BACKGROUND: Breast cancer bone metastasis is closely associated with the bone microenvironment. Zuogui Pill (ZGP), a clinically approved formulation in China, effectively regulates the bone microenvironment for the prevention and treatment of osteoporosis.
OBJECTIVE: Few reports have utilized the ZGP for bone metastasis models. This study investigated the intervention and bone-protective properties of ZGP against breast cancer bone metastasis, explored the potential mechanism, and screened for its active compositions by molecules fishing.
METHODS: To investigate the intervention efficacy of ZGP and its protein-level mechanism of action, the mouse bone metastasis model and in vitro cell co-culture model were constructed. Affinity ultrafiltration, molecular docking, cellular thermal shift assay and physical scale detection were used to investigate the affinity components of the RANKL protein in ZGP.
RESULTS: The administration of ZGP combined with zoledronic acid inhibited the development of tumors and secondary lung metastasis in mice. This translated to a prolonged survival period and enhanced quality of life. ZGP could disrupt the malignant cycle by modulating the Piezo1-Notch-1-GPX4 signaling pathway in the \"bone-cancer\" communication in the cell co-culture model. Furthermore, 25 chemical components of ZGP were identified, with 10 active compounds exhibiting significant affinity for the RANKL protein.
CONCLUSIONS: The findings of this work highlighted ZGP\'s potential for intervening in the progression of breast cancer bone metastasis. Thus, this investigation served as an experimental foundation for expanding the application scope of ZGP and for advancing drug development efforts in bone metastasis treatment.

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The in silico tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, in silico studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of in silico tools used in MTDD for different NDs in combating the challenge of drug development and the application of various in silico tools to solve the problem of target fishing.

Gelsedine-type alkaloids are highly toxic plant secondary metabolites produced by shrubs belonging to the Gelsemium genus. Gelsenicine is one of the most concerning gelsedine-type alkaloids with a lethal dose lower than 1 mg/Kg in mice. Several reported episodes of poisoning in livestock and fatality cases in humans due to the usage of Gelsemium plants extracts were reported. Also, gelsedine-type alkaloids were found in honey constituting a potential food safety issue. However, their toxicological understanding is scarce and the molecular mechanism underpinning their toxicity needs further investigations. In this context, an in silico approach based on reverse screening, docking and molecular dynamics successfully identified a possible gelsenicine biological target shedding light on its toxicodynamics. In line with the available crystallographic data, it emerged gelsenicine could target the acetylcholine binding protein possibly acting as a partial agonist against α7 nicotinic acetylcholine receptor (AChR). Overall, these results agreed with evidence previously reported and prioritized AChR for further dedicated analysis.

Phytohormones represent a group of secondary metabolites with different chemical structures, in which belong auxins, cytokinins, gibberellins, or brassinosteroids. In higher plants, they cover active roles in growth or defense function, while their potential benefits for human health protection were noted for some phytohormones and little explored for many others. In this study, we developed a target fishing strategy on fifty-three selected naturally occurring phytohormones covering different families towards proteins involved in key cellular functions related to human metabolism and health protection/disease. This in silico analysis strategy aims to screen the potential human health-driven bioactivity of more than fifty phytohormones through the analysis of their interactions with specific targets. From this analysis, twenty-eight human targets were recovered. Some targets e.g., the proteins mitochondrial glutamate dehydrogenase (GLUD1) or nerve growth factor (NGF) bound many phytohormones, highlighting their involvement in amino acid metabolism and/or in the maintenance or survival of neurons. Conversely, some phytohormones specifically interacted with some proteins, e.g., SPRY domain-containing SOCS box protein 2 (SPSB2) or Inosine-5\'-monophosphate dehydrogenase 1 (IMPDH1), both involved in human immune response. They were then investigated with a molecular docking analysis approach. Our bioprospecting study indicated that many phytohormones may endow human health benefits, with potential functional role in multiple cellular processes including immune response and cell cycle progression.

Morin is a vasorelaxant flavonoid, whose activity is ascribable to CaV1.2 channel blockade that, however, is weak as compared to that of clinically used therapeutic agents. A conventional strategy to circumvent this drawback is to synthesize new derivatives differently decorated and, in this context, morin-derivatives able to interact with CaV1.2 channels were found by employing the potential of PLATO in target fishing and reverse screening. Three different derivatives (5a-c) were selected as promising tools, synthesized, and investigated in in vitro functional studies using rat aorta rings and rat tail artery myocytes. 5a-c were found more effective vasorelaxant agents than the naturally occurring parent compound and antagonized both electro- and pharmaco-mechanical coupling in an endothelium-independent manner. 5a, the series\' most potent, reduced also Ca2+ mobilization from intracellular store sites. Furthermore, 5a≈5c > 5b inhibited Ba2+ current through CaV1.2 channels. However, compound 5a caused also a concentration-dependent inhibition of KCa1.1 channel currents.

The pharmacodynamic substances of traditional Chinese medicine(TCM) are the basis for the research of TCM and the development of innovative drugs. However, the lack of clarity of targets and molecular mechanisms is the bottleneck problem that restricts the research of pharmacodynamic substances of TCM. Bioactive components are the material basis of the efficacy of TCM, which exert activity by regulating the corresponding targets. Therefore, it is very important to identify the targets of the bioactive components to elucidate the pharmacological mechanism of TCM. Proteins are the most important drug targets, and study of the interaction between the proteins and bioactive components of TCM plays a key role in the development of pharmacological mechanism of TCM. In recent years, the main techniques for detecting the interaction between the bioactive components and proteins include surface plasmon resonance, fluorescence resonance energy transfer, bio-layer interference, molecular docking, proteome chip, target fishing, target mutant, and protein crystallization techniques, etc. This review summarized the biological target detection techniques and their applications in locating the targets of the bioactive components in TCM in the last decade, and this paper will provide useful strategies to elucidate the pharmacological mechanisms of TCM.