Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

The potency of blumeatin and luteolin as caspase-1 inhibitor by molecular docking

COVID-19 infection induces inflammation by increasing cytokines such as IL-1b, IL-6, IL-18, IFN-γ, and TNF-α. IL-1b is generated by the involvement of caspase-1. Therefore, caspase-1 inhibitor can be potential for inflammation therapy caused by COVID-19 infection. This study aims to determine the potential of blumeatin and luteolin as anti-inflammatory agents by inhibiting caspase-1 using a molecular docking approach. This study was carried out by caspase-1 (PDB ID: 1RWK) preparation, blumeatin and luteolin structure optimization, docking protocol validation, and docking of blumeatin and luteolin on caspase-1. Bluematin and luteolin had a binding affinity of -5,63 kcal/mol and -5,93 kcal/mol, lower than Q158 native ligand (-3.92 kcal/mol). Similar amino acid residues in hydrogen bonds interaction were observed between Q158 native ligand, blumeatin, and luteolin with caspase-1 (GLN 283 and ARG 179). Blumeatin and luteolin are potentially anti-inflammation agents through the inhibition of the caspase-1 in silico.

Molecular docking of gallic acid as anti-photoaging in silico

Skin aging caused by excessive exposure to ultraviolet is known as photoaging. The mechanism underlying skin photoaging relates to collagen degradation in the extracellular matrix (ECM) by overexpression of matrix metalloproteinases-1 (MMP-1). Gallic acid is a phenolic antioxidant found in many types of plants and can be used as an anti-photoaging agent due to its antioxidant activity. This study aims to determine the potential effect of gallic acid as an anti-photoaging against MMP-1 using in silico molecular docking. The stages included gallic acid structure optimization using the HyperChem 8, preparation of protein target MMP-1 (PDB ID: 966C) using the Chimera1.10.1, validation the molecular docking protocol, and docking gallic acid on MMP-1 with the Autodock 1.5.6. The results showed that gallic acid had an affinity for MMP-1 with a binding energy of -6.0 kcal/mol. There are similar amino acid residues in hydrogen bonds between the native ligand RS2 with MMP-1 and gallic acid with MMP-1, namely ALA 182, LEU 181, and HIS 218. The results suggest that gallic acid has the potential as the anti-photoaging agent through the inhibition of the MMP-1 enzyme.

In silico study of lutein as anti-HER-2 receptors in breast cancer treatment

Human Epidermal Receptor-2 (HER-2) overexpression is implicated in breast cancer progression; thus, HER-2 is widely used as the target of anticancer therapy. Lapatinib is a drug widely used to inhibit the HER-2 receptor and tyrosine kinase; however, it develops drug resistance. Lutein is promising to be developed as breast cancer therapy. This study aims to determine the mechanism of inhibition of HER-2 receptor overexpression by lutein in silico. Molecular docking was carried out by optimizing the lutein and lapatinib, preparing of protein target HER-2 (PDB ID 3PP0), validating of molecular docking protocol, and docking of lutein and lapatinib on HER-2. The study resulted in the binding energy of -12.37 kcal/mol, while the binding energy of the native ligand and lapatinib to HER-2 was -10.43 kcal/mol and -12.25 kcal/mol, respectively. The binding energy showed that lutein has potential as breast anticancer suggested from the stronger affinity to HER2.

Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-Based Virtual Screening

Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era.

Improved protein docking by predicted interface residues.

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

Improved protein docking by predicted interface residues

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.