Binding mechanism of spinosine and venenatine molecules with p300 HAT enzyme: Molecular screening, molecular dynamics and free-energy analysis

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the propensity for false negatives. Therefore, we propose EADTN, a simple and efficient ensemble model. We have designed an innovative feature adaptation technique to automatically extract local weights of drugs and targets, and we utilize clustering-enhanced parameter fine-tuning to overcome the issue of false negatives, thereby enhancing its reliability in drug discovery. Based on EADTN, we also propose a Shapley value-based method for identifying key drug substructures, effectively enhancing the model's interpretability. Additionally, we utilized EADTN to reveal potential interactions between NQO1 targets and the drugs SIRT-IN-1 and LY2183240, which were subsequently validated through wet-lab experiments. Experimental evidence demonstrates that EADTN consistently outperforms existing best-performing models across various data sets, promising significant benefits in fields such as drug repositioning.

Investigation of intermolecular interactions and binding mechanism of PU139 and PU141 molecules with p300 HAT enzyme via molecular docking, molecular dynamics simulations and binding free energy analysis

The p300 histone acetyltransferase (HAT) enzyme acetylates the lysine residue of histone promotes the transcription reaction. The abnormal function of p300 HAT enzyme causes various diseases such as Cancer, Asthma, Alzheimer, Diabetics, and AIDS. In the recent years, several studies have been conducted to design potential drug to inhibit this enzyme. Recently, an in vitro study has been performed on the synthetic molecules PU139 and PU141 to inhibit the p300 HAT enzyme. The present study aims to understand the binding affinity, intermolecular interactions, conformational stability and binding energy of PU139 and PU141 molecules in the active site of p300 HAT enzyme from the in silico studies. The molecular docking and molecular dynamics (MD) simulations were carried out for both ligands with the p300 HAT enzyme. The molecular docking and MD simulations reveals that both molecules forms expected interactions with the catalytic site key residues of p300 enzyme. The MD simulation shows the maximum RMSD value for the PU141 is 2.3 Å, whereas for PU139 is 3.3 Å; these low RMSD values indicate that both molecules are highly stable in the active site of p300. The calculated binding free energy of PU141 (-20.62 kcal/mol) is higher than the molecule PU139 (-17.67 kcal/mol). Among the results, PU141 shows the high binding affinity with p300 while comparing with PU139. The results of this in-silico study coupled with the findings reported in the in vitro study confirm that PU141 may be suitable for clinical study.Communicated by Ramaswamy H. Sarma.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

Binding mechanism of naringenin with monoamine oxidase - B enzyme: QM/MM and molecular dynamics perspective

The reduced level of dopamine at midbrain (substantia nigra) leads to Parkinson disease by the influence of monoamine oxidation process of monoamine oxidase B (MAO-B) enzyme. This disease mostly affects the aged people. Reports outline that the naringenin molecule acts as an inhibitor of MAO-B enzyme and it potentially prevents the development of PD. To elucidate the binding mechanism of naringenin with MAO-B, we performed the molecular docking, QM/MM and molecular dynamics (MD) simulations. The molecular docking results confirm that the naringenin strongly binds with the substrate binding site of MAO-B enzyme (-12.0 kcal/mol). The low values of RMSD, RMSF and Rg indicate that the naringenin - MAO-B complex is stable over the entire period of MD simulation. Naringenin forms strong interaction with the orient keeper residue Tyr326 and other binding site residues Leu171, Glu206 and these interactions were maintained throughout the MD simulation. It is also important to block the function of MAO-B enzyme. The QM/MM study coupled with the charge density analysis reveals the charge density distribution and the strength of intermolecular interactions of naringenin-MAO-B complex. The above results suggest that this molecule is a potential inhibitor of MAO-B enzyme.