Developing an effective polarizable bond method for small molecules with application to optimized molecular docking

Critical Evaluation of Polarizable and Nonpolarizable Force Fields for Proteins Using Experimentally Derived Nitrile Electric Fields

Molecular dynamics (MD) simulations are frequently carried out for proteins to investigate the role of electrostatics in their biological function. The choice of force field (FF) can significantly alter the MD results, as the simulated local electrostatic interactions lack benchmarking in the absence of appropriate experimental methods. We recently reported that the transition dipole moment (TDM) of the popular nitrile vibrational probe varies linearly with the environmental electric field, overcoming well-known hydrogen bonding (H-bonding) issues for the nitrile frequency and, thus, enabling the unambiguous measurement of electric fields in proteins (J. Am. Chem. Soc. 2022, 144 (17), 7562-7567). Herein, we utilize this new strategy to enable comparisons of experimental and simulated electric fields in protein environments. Specifically, previously determined TDM electric fields exerted onto nitrile-containing o-cyanophenylalanine residues in photoactive yellow protein are compared with MD electric fields from the fixed-charge AMBER FF and the polarizable AMOEBA FF. We observe that the electric field distributions for H-bonding nitriles are substantially affected by the choice of FF. As such, AMBER underestimates electric fields for nitriles experiencing moderate field strengths; in contrast, AMOEBA robustly recapitulates the TDM electric fields. The FF dependence of the electric fields can be partly explained by the presence of additional negative charge density along the nitrile bond axis in AMOEBA, which is due to the inclusion of higher-order multipole parameters; this, in turn, begets more head-on nitrile H-bonds. We conclude by discussing the implications of the FF dependence for the simulation of nitriles and proteins in general.

Investigation on toxicity and mechanism to Daphnia magna for 14 disinfection by-products: Enzyme activity and molecular docking

Exposure to disinfection by-products (DBPs) has been found to induce a range of toxic effects in aquatic organism. Previous studies have consistently demonstrated that a majority of DBPs have the ability to induce in vivo toxicity in aquatic organisms. However, the impact of DBPs on the metabolic processes of Daphnia magna (D. magna) and the underlying molecular toxicity mechanisms are still not well understood. Therefore, we investigated the effects of 14 DBPs on two oxidative stress enzymes and malondialdehyde (MDA) levels in D. magna. Additionally, we employed molecular docking to simulate the toxicity of DBPs to D. magna at the molecular level. This comprehensive analysis allowed us to gain further insights into the toxicity of DBPs on D. magna. The results showed that among the aliphatic DBPs, the more bromine substituents, the lower the toxicity effect, and it's opposite in the aromatic DBPs. In the detection of oxidative stress level, catalase (CAT) enzyme and superoxide dismutase (SOD) enzyme in D. magna under compound stress showed a low increase and decrease with the increase of concentration. The level of MDA showed a positive correlation with the concentration. In the last, molecular docking simulations have shown promise in predicting the toxicity of DBPs and providing insights into their toxic effects to a certain extent, and the docking situation of P53 is slightly different. Hence, it is imperative to further regulate the presence of aromatic DBPs due to their pronounced toxic effects on D. magna, and these simulations can be complemented with actual experiments to enhance our understanding of the toxicity mechanisms of DBPs.

Investigation on toxicity mechanism of halogenated aromatic disinfection by-products to zebrafish based on molecular docking and QSAR model

Halogenated aromatic disinfection by-products (DBPs) are a new type of DBPs that have been detected in various water bodies. Previous studies have shown that most of them can induce in vivo toxicity in aquatic organisms. In this study, in order to further investigate the toxic effects and mechanisms of aromatic DBPs, the toxicity and ecological risks of 10 halogenated aromatic DBPs were assessed using the model organism zebrafish. It was found that the toxicity of DBPs was related to the number, type, and position of halogen and the type of substituent, and the 24 h-toxicity value of DBPs in this experiment could replace their 96 h-toxicity value to reduce the test time and save the test cost. Halogenated phenol and halogenated nitrophenol were more toxic, but the current ecological risks of DBPs were relatively low. In addition, the toxicity mechanism of DBPs was analyzed based on molecular docking and quantitative structure-activity relationship (QSAR) models. The molecular docking results showed that all 10 DBPs could bind to zebrafish's catalase (CAT), cytochrome P450 (CYP450), p53, and acetylcholinesterase (AChE), thereby affecting their normal life activities. QSAR models indicated that the toxicity of halogenated aromatic DBPs to zebrafish mainly depended on their hydrophobicity (log D), the interaction with CAT (ECAT), and hydrogen bonding acidity (A).

<em>In silico</em> screening of potential compounds from begonia genus as 3CL protease (3Cl pro) SARS-CoV-2 inhibitors

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective: This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases “promising” for the anti-SARS-CoV-2 target. Methods: In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Results: Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only β-sitosterol-3-O-β-D-glucopyranoside with a lower docking score of - 9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 Å, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 Å and 0.773 Å, respectively. Conclusions: This result suggests that β-sitosterol-3-O-β-D- glucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

Me-Better Drug Design Based on Nevirapine and Mechanism of Molecular Interactions with Y188C Mutant HIV-1 Reverse Transcriptase

In this paper, the Y188C mutant HIV-1 reverse transcriptase (Y188CM-RT) target protein was constructed by homology modeling, and new ligands based on nevirapine (NVP) skeleton were designed by means of fragment growth. The binding activity of new ligands to Y188CM-RT was evaluated by structural analysis, ADMET prediction, molecular docking, energy calculation and molecular dynamics. Results show that 10 new ligands had good absorbability, and their binding energies to Y188CM-RT were significantly higher than those of wild-type HIV-1 reverse transcriptase(wt). The binding mode explained that fragment growth contributed to larger ligands, leading to improved suitability at the docking pocket. In the way of fragment growth, the larger side chain with extensive contact at terminal is obviously better than substituted benzene ring. The enhancement of docking activity is mainly due to the new fragments such as alkyl chains and rings with amino groups at NVP terminal, resulting in a large increase in hydrophobic bonding and the new addition of hydrogen bonding or salt bonding. This study is expected to provide reference for the research on non-nucleoside reverse transcriptase inhibitors resistance and AIDS treatment.

Synthesis, spectroscopic, and computational studies on molecular charge-transfer complex of 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1, 3-diol with chloranilic acid: Potential antiviral activity simulation of CT-complex against SARS-CoV-2

An innovative charge-transfer complex between the Schiff base 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1,3-diol [SAL-THAM] and the π-acceptor, chloranilic acid (CLA) within the mole ratio (1:1) was synthesized and characterized aiming to investigate its electronic transition spectra in acetonitrile (ACN), methanol (MeOH) and ethanol (EtOH) solutions. Applying Job`s method in the three solvents supported the 1:1 (CLA: SAL-THAM) mole ratio complex formation. The formation of stable CT- complex was shown by the highest values of charge-transfer complex formation constants, K_CT, calculated using minimum-maximum absorbance method, with the sequence, acetonitrile > ethanol > methanol DFT study on the synthesized CT complex was applied based on the B3LYP method to evaluate the optimized structure and extract geometrical and reactivity parameters. Based on TD-DFT theory, the electronic properties, ¹H and ¹³C NMR, IR, and UV-Vis spectra of the studied system in different solvents showing good agreement with the experimental studies. MEP map described the possibility of hydrogen bonding and charge transfer in the studied system. Finally, a computational approach for screening the antiviral activity of CT - complex towards SARS-CoV-2 coronavirus protease via molecular docking simulation was conducted and confirmed with molecular dynamic (MD) simulation.

Spectroscopic and in silico insight into the interaction between dicofol and human serum albumin

Dicofol, a broad-spectrum acaricide, has garnered considerable attention because of the potential harm to the environment and various organisms. Herein, this study applied spectroscopic and in silico methods to understand the interaction between human serum albumin (HSA) and dicofol. Fluorescence experiments demonstrated that dicofol formed a stable complex and the binding process occurred in Suldow's site I of HSA. Its binding constant was 2.26 × 10⁵ M^-1 at 298 K. Van der Waals forces and hydrogen bond were primarily facilitated the interaction between dicofol and HSA (ΔH < 0, ΔS < 0) according to thermodynamic experiments. Additionally, 3D fluorescence and circular dichroism (CD) spectra revealed a few conformational changes in HSA due to dicofol. Molecular docking analysis indicated that dicofol interacted with Ser192, Gln196, Leu481, Arg218, Leu238, and Phe211 via van der Waals forces and formed a hydrogen bond with His242. Molecular dynamics (MD) simulation showed that Lys195 and Arg218 residues contributed greater energy for forming the HSA-dicofol complex. MD simulation analysis also showed that dicofol can affect the HSA structure with a reduction in α-helix. This research is desired to facilitate a new perspective on the toxicity mechanism of dicofol in the human body.

Toxicity inhibition strategy of microplastics to aquatic organisms through molecular docking, molecular dynamics simulation and molecular modification

In the present study, the combined toxic effect of microplastics and their additives (five) on aquatic organisms (zebrafish) was studied using full factorial design method, molecular docking, and molecular dynamics (MD) simulation technology. The aquatic toxicity control programmer was designed to improve the optimal combination of plasticizer and microplastics based on the design of environment-friendly phthalic acid ester (PAE) derivatives. First, a total of 64 groups of microplastic-additives were designed using the full factorial design method. Next, the microplastic-additives and aquatic receptor protein were docked together, and the binding energy of these complexes was calculated using the MD simulation method. The results revealed that the aquatic toxicity effects of different microplastic-additive combinations were variable; therefore, the optimal combination of microplastics exhibiting the lowest aquatic toxicity effect could be screened out. Base on the analyzing the bonding effect and surrounded amino acid residues between the microplastic additives and receptor protein, the main driving forces for the binding of the microplastic-additive and the protein were hydrophobic force, hydrogen bonding force and electrostatic force. The main effects and the second-order interaction of the microplastic-additives combination were analyzed using the fixed-effect model. The main additives that affect the aquatic toxicity of the microplastics can be known. In addition, based on the MD simulation of the molecular replacement of PAE derivatives, the optimal level of component combination of low aquatic toxicity effect of microplastics was constructed.

An Analysis Based on Molecular Docking and Molecular Dynamics Simulation Study of Bromelain as Anti-SARS-CoV-2 Variants

The rapid spread of a novel coronavirus known as SARS-CoV-2 has compelled the entire world to seek ways to weaken this virus, prevent its spread and also eliminate it. However, no drug has been approved to treat COVID-19. Furthermore, the receptor-binding domain (RBD) on this viral spike protein, as well as several other important parts of this virus, have recently undergone mutations, resulting in new virus variants. While no treatment is currently available, a naturally derived molecule with known antiviral properties could be used as a potential treatment. Bromelain is an enzyme found in the fruit and stem of pineapples. This substance has been shown to have a broad antiviral activity. In this article, we analyse the ability of bromelain to counteract various variants of the SARS-CoV-2 by targeting bromelain binding on the side of this viral interaction with human angiotensin-converting enzyme 2 (hACE2) using molecular docking and molecular dynamics simulation approaches. We have succeeded in making three-dimensional configurations of various RBD variants using protein modelling. Bromelain exhibited good binding affinity toward various variants of RBDs and binds right at the binding site between RBDs and hACE2. This result is also presented in the modelling between Bromelain, RBD, and hACE2. The molecular dynamics (MD) simulations study revealed significant stability of the bromelain and RBD proteins separately up to 100 ns with an RMSD value of 2 Å. Furthermore, despite increases in RMSD and changes in Rog values of complexes, which are likely due to some destabilized interactions between bromelain and RBD proteins, two proteins in each complex remained bonded, and the site where the two proteins bind remained unchanged. This finding indicated that bromelain could have an inhibitory effect on different SARS-CoV-2 variants, paving the way for a new SARS-CoV-2 inhibitor drug. However, more in vitro and in vivo research on this potential mechanism of action is required.