No abstract

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein–ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT.

Neonicotinoids are a widely used class of insecticides that target the acetylcholine recognition site of the nicotinic acetylcholine receptors in the central nervous system of insects. Although neonicotinoids display a high specificity for insects, their use has been recently debated since several studies led to the hypothesis that they may have adverse ecological effects and potential risks to mammals and even humans. Due to their hydrophobic nature, neonicotinoids need specific carriers to allow their distribution in body fluids. Human serum albumin (HSA), the most abundant plasma protein, is a key carrier of endogenous and exogenous compounds. The in silico docking and ligand binding properties of acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam to HSA are here reported. Neonicotinoids bind to multiple fatty acid (FA) binding sites, preferentially to the FA1 pocket, with high affinity. Values of the dissociation equilibrium constant for neonicotinoid binding FA1 of HSA (i.e., calcKn) derived from in silico docking simulations (ranging between 3.9 × 10−5 and 6.3 × 10−4 M) agree with those determined experimentally from competitive inhibition of heme‐Fe(III) binding (i.e., expKn; ranging between 2.1 × 10−5 and 6.9 × 10−5 M). Accounting for the HSA concentration in vivo (~7.5 10−4 M), values of Kn here determined suggest that the formation of the HSA:neonicotinoid complexes may occur in vivo. Therefore, HSA appears to be an important determinant for neonicotinoid transport and distribution to tissues and organs, particularly to the liver where they are metabolized.

The anticoagulant therapy is widely used to prevent and treat thromboembolic events. Until the last decade, vitamin K antagonists were the only available oral anticoagulants; recently, direct oral anticoagulants (DOACs) have been developed. Since 55% to 95% of DOACs are bound to plasma proteins, the in silico docking and ligand‐binding properties of drugs apixaban, betrixaban, dabigatran, edoxaban, and rivaroxaban and of the prodrug dabigatran etexilate to human serum albumin (HSA), the most abundant plasma protein, have been investigated. DOACs bind to the fatty acid (FA) site 1 (FA1) of ligand‐free HSA, whereas they bind to the FA8 and FA9 sites of heme‐Fe(III)‐ and myristic acid‐bound HSA. DOACs binding to the FA1 site of ligand‐free HSA has been validated by competitive inhibition of heme‐Fe(III) recognition. Values of the dissociation equilibrium constant for DOACs binding to the FA1 site (ie, calcKDOAC) derived from in silico docking simulations (ranging between 1.2 × 10−8 M and 1.4 × 10−6 M) agree with those determined experimentally from competitive inhibition of heme‐Fe(III) binding (ie, expKDOAC; ranging between 2.5 × 10−7 M and 2.2 × 10−6 M). In addition, this study highlights the inequivalence of rivaroxaban binding to mammalian serum albumin. Given the HSA concentration in vivo (~7.5 × 10−4 M), values of KDOAC here determined indicate that the formation of the HSA:DOACs complexes in the absence and presence of FAs and heme‐Fe(III) may occur in vivo. Therefore, HSA appears to be an important determinant for DOACs transport.