ABSTRACT
CD44 is a cell-surface glycoprotein and receptor for hyaluronan, one of the major components of the tumor extracellular matrix. There is evidence that the interaction between CD44 and hyaluronan promotes breast cancer metastasis. Recently, the molecule F-19848A was shown to inhibit hyaluronan binding to receptor CD44 in a cell-based assay. In this study, we investigated the mechanism and energetics of F-19848A binding to CD44 using molecular simulation. Using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) method, we obtained the binding free energy and inhibition constant of the complex. The van der Waals (vdW) interaction and the extended portion of F-19848A play key roles in the binding affinity. We screened natural products from a traditional Chinese medicine database to search for CD44 inhibitors. From combining pharmaceutical requirements with docking and molecular dynamics simulations, we found ten compounds that are potentially better or equal to the F-19848A ligand at binding to CD44 receptor. Therefore, we have identified new candidates of CD44 inhibitors, based on molecular simulation, which may be effective small molecules for the therapy of breast cancer.
Subject(s)
Antineoplastic Agents/chemistry , Hyaluronan Receptors/chemistry , Hyaluronic Acid/chemistry , Molecular Dynamics Simulation , Antineoplastic Agents/metabolism , Antineoplastic Agents/pharmacology , Binding Sites , Binding, Competitive/drug effects , Breast Neoplasms/drug therapy , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Humans , Hyaluronan Receptors/antagonists & inhibitors , Hyaluronan Receptors/metabolism , Hyaluronic Acid/metabolism , Hydrogen Bonding , Ligands , Molecular Structure , Protein Binding/drug effects , Protein Domains , ThermodynamicsABSTRACT
Understanding how ligands bind to G-protein coupled receptors (GPCRs) provides insights into a myriad of cell processes and is crucial for drug development. Here we extend a hybrid molecular mechanics/coarse-grained (MM/CG) approach applied previously to enzymes to GPCR/ligand complexes. The accuracy of this method for structural predictions is established by comparison with recent atomistic molecular dynamics simulations on the human ß2 adrenergic receptor, a member of the GPCRs superfamily. The results obtained with the MM/CG methodology show a good agreement with previous all-atom classical dynamics simulations, in particular in the structural description of the ligand binding site. This approach could be used for high-throughput predictions of ligand poses in a variety of GPCRs.