Prediction of Hot Spots at Myeloid Cell Leukemia-1–Inhibitor Interface Using Energy Estimation and Alanine Scanning Mutagenesis

Abstract

Myeloid cell leukemia 1 (Mcl1) is an antiapoptotic protein that plays central role in apoptosis regulation. Also, Mcl1 has the potency to resist apoptotic cues resulting in up-regulation and cancer cell protection. A molecular probe that has the potential to specifically target Mcl1 and thereby provoke its down-regulatory activity is very essential. The aim of the current study is to probe the internal conformational dynamics of protein motions and potential binding mechanism in response to a series of picomolar range Mcl1 inhibitors using explicit-solvent molecular dynamics (MD) simulations. Subsequently, domain cross-correlation and principal component analysis was performed on the snapshots obtained from the MD simulations. Our results showed significant differences in the internal conformational dynamics of Mcl1 with respect to binding affinity values of inhibitors. Further, the binding free energy estimation, using three different samples, was performed on the MD simulations and revealed that the predicted energies (ΔGmmgbsa) were in good correlation with the experimental values (ΔGexpt). Also, the energies obtained using all sampling models were efficiently ranked. Subsequently, the decomposition energy analysis highlighted the major energy-contributing residues at the Mcl1 binding pocket. Computational alanine scanning performed on high energy-contributing residues predicted the hot spot residues. The dihedral angle analysis using MD snapshots on the predicted hot spot residue exhibited consistency in side chain conformational motion that ultimately led to strong binding affinity values. The findings from the present study might provide valuable guidelines for the design of novel Mcl1 inhibitors that might significantly improve the specificity for new-generation chemotherapeutic agents.

Investigating the Molecular Basis of N-Substituted 1-Hydroxy-4-Sulfamoyl-2-Naphthoate Compounds Binding to Mcl1

“…In our previous investigation, MD simulation was widely applied to Bcl-2 family proteins to gain an understanding on the mechanism of action [37,39,40,56]. Likewise, the current study is an attempt to extend our understanding of the molecular mechanism of Mcl1 with the highly active compound from the dataset-C40-used to construct the 3D-QSAR model.…”

“…Taking advantage of this, the current study is an attempt to understand the mechanistic behavior of the current compound series with Mcl1. To achieve this, a combination of in silico approaches-pharmacophore-based 3D-Quantitative Structure Activity Relationship, docking, and Molecular Dynamics (MD) simulation-is widely used [34][35][36][37][38][39][40]. Correspondingly, a pharmacophore-based 3D-QSAR model was constructed based on known inhibitors obtained from the literature [33], with the aim of gaining knowledge on the critical chemical features responsible for its maximum activity.…”

Extraction of molecular features for the drug discovery targeting protein‐protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

“…However, mutational analyses of PPI interfaces revealed that the binding affinity is not evenly distributed across the binding interfaces, but instead contributed by the small patch of amino acid residues know as hot spot . Most of the hot spots are enough compact in size to be filled by the peptide or small molecules . Identification of hot spot residues at the interface by experiments is time consuming and costly.…”

“…[25][26][27][28] Most of the hot spots are enough compact in size to be filled by the peptide or small molecules. [29][30][31] Identification of hot spot residues at the interface by experiments is time consuming and costly. Therefore, several computational techniques, such as machine learning approach, molecular docking, and computational alanine scanning, have been developed with high accuracy to predict hot spot residues.…”

Effect of double mutations T790M/L858R on conformation and drug-resistant mechanism of epidermal growth factor receptor explored by molecular dynamics simulations

“…Molecular dynamics (MD) simulations have been a universal tool to study the conformational changes of proteins and ligand-protein binding mechanisms. [38][39][40][41][42][43][44][45][46][47] Moreover, MD simulation studies of EGFR have attracted wide attention in recent years. [48][49][50] Several methods have been proposed to calculate binding free energies of inhibitors to proteins, such as free energy perturbation (FEP), [51][52][53] thermodynamics integration (TI) 54,55 and molecular mechanics Poisson-Boltzmann/ generalized Born surface area (MM-PBSA/GBSA).…”