Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors?

Ribonucleic acids (RNAs), particularly the noncoding RNAs, play key roles in cancer, making them attractive drug targets. While conventional methods such as high throughput screening are resource-intensive, computational methods such as RNA-ligand docking can be used as an alternative. However, currently available docking methods are fine-tuned to perform protein-ligand and protein-protein docking. In this work, we evaluated three commonly used docking methods─AutoDock Vina, HADDOCK, and HDOCK─alongside RLDOCK, which is specifically designed for RNA-ligand docking. Our evaluation was based on several criteria including cognate docking, blind docking, scoring potential, and ranking potential. In cognate docking, only RLDOCK showed a success rate of 70% for the top-scoring docked pose. Despite this, all four docking methods did not achieve an overall success rate exceeding 50% amidst our attempt to refine the top-scoring docked poses using molecular dynamics simulations. Meanwhile, all four docking methods showed poor performance in scoring potential evaluation. Although AutoDock Vina achieved an area under the receiver operating characteristic curve of 0.70, it showed poor performance in terms of Matthews' correlation coefficient, precision, enrichment factors, and normalized enrichment factors at 1, 2, and 5%. These results highlight the growing need for further optimization of docking methods to assess RNA-ligand interactions.

Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach.

The impact of H. pylori resistance on patient's treatment failure is a major concern. Therefore, the development of novel or alternative therapies for H. pylori is urgently needed. The purpose of this study was to investigate the molecular interactions of various antimicrobial peptides (AMPs) to H. pylori proteins. We performed the peptide-protein molecular docking using HADDOCK 2.4 webserver. Fourteen AMPs were tested for their binding efficacy against four H. pylori proteins. Simulation of the peptide-protein complex was performed using molecular dynamic software package AMBER20. From molecular docking analysis, five peptides (LL-37, Tilapia piscidin 4, napin, snakin-1 and EcAMP1) showed strong binding interactions against H. pylori proteins. The strongest binding affinity was observed in the interactions between Snakin-1 and PBP2, TP4 and type I HopQ and EcAMP1 and type I HopQ with -11.1, -13.6 and -13.8 kcal/mol, respectively. The dynamic simulation was performed for two complexes (snakin1-PBP2 and EcAMP1-HopQ). Results of the dynamics simulation showed that EcAMP1 had stable interaction and binding to type I HopQ protein without significant structural changes. In conclusion, both results of docking and simulation showed that EcAMP1 might be useful as a potential therapeutic agent for H. pylori treatment. This molecular approach provides deep understanding of the interaction insights between AMPs and H. pylori proteins. It paves the way for the development of novel anti-H. pylori using antimicrobial peptides.

A data-driven machine learning approach for discovering potent LasR inhibitors.

The rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.

In-silico discovery of inhibitors against human papillomavirus E1 protein.

High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were -37.84 kcal/mol, -25.30 kcal/mol, and -29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.Communicated by Ramaswamy H. Sarma.