In recent years, the awareness that pesticides can have other effects apart from generic toxicity is growing. In particular, several pieces of evidence highlight their influence on human fertility. In this study, we investigated, by a virtual screening approach, the binding between pesticides and proteins present in human gametes or associated with reproduction, in order to identify new interactions that could affect human fertility. To this aim, we prepared ligand (pesticides) and receptor (proteins) 3D structure datasets from online structural databases (such as PubChem and RCSB), and performed a virtual screening analysis using Autodock Vina. In the comparison of the predicted interactions, we found that famoxadone was predicted to bind Cellular Retinol Binding Protein-III in the retinol-binding site with a better minimum energy value of -10.4 Kcal/mol and an RMSD of 3.77 with respect to retinol (-7.1 Kcal/mol). In addition to a similar network of interactions, famoxadone binding is more stabilized by additional hydrophobic patches including L20, V29, A33, F57, L117, and L118 amino acid residues and hydrogen bonds with Y19 and K40. These results support a possible competitive effect of famoxadone on retinol binding with impacts on the ability of developing the cardiac tissue, in accordance with the literature data on zebrafish embryos. Moreover, famoxadone binds, with a minimum energy value between -8.3 and -8.0 Kcal/mol, to the IZUMO Sperm-Egg Fusion Protein, interacting with a network of polar and hydrophobic amino acid residues in the cavity between the 4HB and Ig-like domains. This binding is more stabilized by a predicted hydrogen bond with the N185 residue of the protein. A hindrance in this position can probably affect the conformational change for JUNO binding, avoiding the gamete membrane fusion to form the zygote. This work opens new interesting perspectives of study on the effects of pesticides on fertility, extending the knowledge to other typologies of interaction which can affect different steps of the reproductive process.

Cosolvent molecular dynamics (MD) simulations have proven to be powerful in silico tools to predict hotspots for binding regions on protein surfaces. In the current study, the method was adapted and applied to two Tudor domain-containing proteins, namely Spindlin1 (SPIN1) and survival motor neuron protein (SMN). Tudor domains are characterized by so-called aromatic cages that recognize methylated lysine residues of protein targets. In the study, the conformational transitions from closed to open aromatic cage conformations were investigated by performing MD simulations with cosolvents using six different probe molecules. It is shown that a trajectory clustering approach in combination with volume and atomic distance tracking allows a reasonable discrimination between open and closed aromatic cage conformations and the docking of inhibitors yields very good reproducibility with crystal structures. Cosolvent MDs are suitable to capture the flexibility of aromatic cages and thus represent a promising tool for the optimization of inhibitors.

The present study aims to promote network toxicology and molecular docking strategies for the efficient evaluation of the toxicity of food contaminants. With the example of liver injury induced by the food contaminant Aflatoxin B1(AFB1), this study effectively investigated the putative toxicity of food contaminants and the potentially molecular mechanisms. The study found that AFB1 regulates multiple signalling pathways by modulating core targets such as AKT1, BCL2, TNF, CASP3, SRC and EGFR. These pathways encompass Pathways in cancer, PI3K-Akt signalling pathway, Endocrine resistance, Lipid and atherosclerosis, Apoptosis and other pathways, subsequently impacting immunotoxicity, inflammatory responses, apoptosis, cytogenetic mutations, and ultimately leading to liver injury. We provide a theoretical basis for understanding the molecular mechanisms of AFB1 hepatotoxicity and for the prevention and treatment of cancers caused by the food contaminant AFB1. Furthermore, our network toxicology and molecular docking methods also provide an effective method for the rapid evaluation of the toxicity of food contaminants, which effectively solves the cost and ethical problems associated with the use of experimental animals.

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

This study critically reevaluates reported Biginelli-like reactions using a Kamlet-Abboud-Taft-based solvent effect model. Surprisingly, structural misassignments were discovered in certain multicomponent reactions, leading to the identification of pseudo three-component derivatives instead of the expected MCR adducts. Attempts to replicate literature conditions failed, prompting reconsideration of the described MCRs and proposed mechanisms. Electrospray ionization (tandem) mass spectrometry, NMR, melting points, elemental analyses and single-crystal X-ray analysis exposed inaccuracies in reported MCRs and allowed for the proposition of a complete catalytic cycle. Biological investigations using both pure and \"contaminated\" derivatives revealed distinctive features in assessed bioassays. A new cellular action mechanism was unveiled for a one obtained pseudo three-component adduct, suggesting similarity with the known dihydropyrimidinone Monastrol as Eg5 inhibitors, disrupting mitosis by forming monoastral mitotic spindles. Docking studies and RMSD analyses supported this hypothesis. The findings described herein underscore the necessity for a critical reexamination and potential corrections of structural assignments in several reports. This work emphasizes the significance of rigorous characterization and critical evaluation in synthetic chemistry, urging a careful reassessment of reported synthesis and biological activities associated with these compounds.

Mycotoxins are known environmental pollutants that may contaminate food and feed chains. Some mycotoxins are regulated in many countries to limit the trading of contaminated and harmful commodities. However, the so-called emerging mycotoxins are poorly understood and need to be investigated further. Fusaric acid is an emerging mycotoxin, noxious to plants and animals, but is known to be less toxic to plants when hydroxylated. The detoxification routes effective in animals have not been elucidated yet. In this context, this study integrated in silico and in vitro techniques to discover potential bioremediation routes to turn fusaric acid to its less toxic metabolites. The toxicodynamics of these forms in humans have also been addressed. An in silico screening process, followed by molecular docking and dynamics studies, identified CYP199A4 from the bacterium Rhodopseudomonas palustris HaA2 as a potential fusaric acid biotransforming enzyme. Its activity was confirmed in vitro. However, the effect of hydroxylation seemed to have a limited impact on the modelled toxicodynamics against human targets. This study represents a starting point to develop a hybrid in silico/in vitro pipeline to find bioremediation agents for other food, feed and environmental contaminants.

Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel Simulated Annealing (pSA) methods have been proposed, where the increase of searching threads can boost the speed of convergence. Although satisfactory solutions can be obtained by these methods, there is no rigorous mathematical analyses on their effectiveness. Thus, this article introduces a probabilistic model, on which a theorem about the effectiveness of multiple initial states parallel SA (MISPSA) has been proven. The theorem also demonstrates that the increasing parallelism in pSA algorithm with the reducing of search depth in each thread could obtain almost the same probability of finding the global optimal solution. We validated our theorem on AutoDock Vina, a widely used molecular docking tool with high accuracy and docking speed. AutoDock Vina uses a pSA strategy to find optimal molecular conformations. Under the premise that the total searching workload (i.e., thread number * iteration depth of each thread) remains unchanged, the docking accuracy from an aggressively parallelized SA searching method is almost the same or even better than those from the default exhaustiveness (parallelism degree) configuration of AutoDock Vina. Taking complex \'1hnn\' as an example,with the increase (125x) in the number of initial states (from 8 to 1000) and the decrease in the search depth for each thread (from 15540 to 124, or 1/125 of the original search depth), the mean energy is -7.80 and -7.94, while the mean RMSD is 3.4 and 3.14, respectively. The result also implies that a considerable speedup (in this case 125x in theory) can be obtained by a highly parallelized SA algorithm implementation.

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Molecular docking (MD) analysis is currently the most commonly used theoretical simulation method to investigate the interaction of aptamers (receptors) and small molecules (ligands) and understand the recognition mechanism between them at a molecular level. Using the specific aptamers of tetracycline antibiotics (tetracycline (TET), oxytetracycline (OTC), doxycycline (DOC)) as the docking models, three steady-state aptamers of tertiary structures (SATS) were established for each aptamer with the UNAFold and RNAComposer tools. The binding free energy (BFE), docking score (DS), and binding site (base) of the specific ligands (TET, OTC, and DOC) with their respective SATS were obtained by molecular docking. The results revealed one or more binding sites in the established SATS of the aptamers. The BFE and DS of different binding sites of one specific SATS varied significantly. The results also revealed that the site with the highest BFE represented the most dominant binding site, even if it was not the SATS with minimum energy. The BFE values could also be used to evaluate the affinity and specificity of the aptamer to its target. For the first time, this study proposes a method for MD analysis of the aptamer and its target based on different SATS, clarification of the binding mode, and prediction of the binding sites (bases). This study provides a theoretical basis for tailoring; structural optimization; and base modification of aptamers; identifying aptamers with high affinity and specificity.