Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors
| Autor: | Gianni De Fabritiis, Matthew J. Harvey, Gerard Martínez-Rosell |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
General Chemical Engineering Chemical structure In silico Computational biology Library and Information Sciences Molecular Dynamics Simulation Ligands 01 natural sciences Small Molecule Libraries 03 medical and health sciences Molecular dynamics 0103 physical sciences Drug Discovery Humans Binding Sites 010304 chemical physics Chemistry Drug discovery Ligand binding assay General Chemistry Ligand (biochemistry) Affinities Chemical space Chemokine CXCL12 Computer Science Applications High-Throughput Screening Assays Molecular Docking Simulation 030104 developmental biology Drug Design Hydrophobic and Hydrophilic Interactions |
| Zdroj: | Journal of chemical information and modeling. 58(3) |
| ISSN: | 1549-960X |
| Popis: | Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities, and kinetics for the selected library and was able to predict 8 mM-affinity fragments with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic core and a positively charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a Markov state model (MSM) framework. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|