Biology subjects

Badaoui, M.

Publications and source records attributed to Badaoui, M..

3 recordsLinked to original sources

bioRxiv2023

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodylator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights of this inhibitor remain unclear, specifically, elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide show a consistent qualitative mechanism and allowed us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved at the transition state. Additionally, our results point at relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3s activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid towards drug design and lead optimization. Description O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/522558v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@2598b2org.highwire.dtl.DTLVardef@4bf425org.highwire.dtl.DTLVardef@89dfa8org.highwire.dtl.DTLVardef@1ba9f48_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical abstract of the work, showing the unbinding for ligands 1 (tiotropium, TTP), 2 (N-methylscopolamin, NMS) and 3 (homatropine methylbromide, CPD2). Using TTPs downhill simulations from its unbinding transition state, different protein-ligand and proteinprotein interactions were analyzed with MLTSA to find relevant CVs driving the different outcomes.

biophysics↗

bioRxiv2021

Combined free energy calculation and machine learning methods for understanding ligand unbinding kinetics

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive, and time-consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free energy profiles of the ligand unbinding process, focusing on the free energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (IC) that form contacts between the protein and the ligand, it then iteratively updates these interactions during a series of biased molecular-dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite temperature string simulations to obtain the free energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for potential further development of these compounds. We compared the free energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates, and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

biophysics↗

bioRxiv2020

Modelling the active SARS-CoV-2 helicase complex as a basis for structure-based inhibitor design

Having claimed over 1 million lives worldwide to date, the ongoing COVID-19 pandemic has created one of the biggest challenges to develop an effective drug to treat infected patients. Among all the proteins expressed by the virus, RNA helicase is a fundamental protein for viral replication, and it is highly conserved among the coronaviridae family. To date, there is no high-resolution structure of helicase bound with ATP and RNA. We present here structural insights and molecular dynamics (MD) simulation results of the SARS-CoV-2 RNA helicase both in its apo form and in complex with its natural substrates. Our structural information of the catalytically competent helicase complex provides valuable insights for the mechanism and function of this enzyme at the atomic level, a key to develop specific inhibitors for this potential COVID-19 drug target.

biochemistry↗