scholarly journals Exploring the conformational space of a receptor for drug design: an ERα case study

2020 ◽  
Author(s):  
Melanie Schneider ◽  
Jean-Luc Pons ◽  
Gilles Labesse
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Molecular Dynamics Simulations ◽  
Crystal Structures ◽  
Estrogen Receptor Alpha ◽  
Binding Pocket ◽  
Conformational Space ◽  
Toxicity Tests ◽  
Static Structure ◽  
Dynamics Simulations

ABSTRACTMotivationProtein flexibility is challenging for both experimentalists and modellers, especially in the field of drug design. Estrogen Receptor alpha (ERα) is an extensively studied Nuclear Receptor (NR) and a well-known therapeutic target with an important role in development and physiology. It is also a frequent off-target in standard toxicity tests for endocrine disruption. Here, we aim to evaluate the degree to which the conformational space and macromolecular flexibility of this well-characterized drug target can be described. Our approach exploits hundreds of crystallographic structures by means of molecular dynamics simulations and of virtual screening.ResultsThe analysis of hundreds of crystal structures confirms the presence of two main conformational states, known as ‘agonist’ and ‘antagonist’, that mainly differ by the orientation of the C-terminal helix H12 which serves to close the binding pocket. ERα also shows some loop flexibility, as well as variable side-chain orientations in its active site. We scrutinized the extent to which standard molecular dynamics simulations or crystallographic refinement as ensemble recapitulate most of the variability features seen by the array of available crystal structure. In parallel, we investigated on the kind and extent of flexibility that is required to achieve convincing docking for all high-affinity ERα ligands present in BindingDB. Using either only one conformation with a few side-chains set flexible, or static structure ensembles in parallel during docking led to good quality and similar pose predictions. These results suggest that the several hundreds of crystal structures already known can properly describe the whole conformational universe of ERα’s ligand binding domain. This opens the road for better drug design and affinity [email protected]

scholarly journals 4094 Structural Determinants of Immunogenicity for Peptide-Based Immunotherapy

2020 ◽  
Vol 4 (s1) ◽  
pp. 16-16
Author(s):  
Jason Devlin ◽  
Jesus Alonso ◽  
Grant Keller ◽  
Sara Bobisse ◽  
Alexandre Harari ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
T Cell ◽  
Molecular Dynamics Simulations ◽  
Crystal Structures ◽  
T Cell Receptor ◽  
Tumor Regression ◽  
Recombinant Expression ◽  
Cell Receptor ◽  
Protein Crystal ◽  
Dynamics Simulations

OBJECTIVES/GOALS: Neoantigen vaccine immunotherapies have shown promise in clinical trials, but identifying which peptides to include in a vaccine remains a challenge. We aim to establish that molecular structural features can help predict which neoantigens to target to achieve tumor regression. METHODS/STUDY POPULATION: Proteins were prepared by recombinant expression in E. coli followed by in vitro refolding. Correctly folded proteins were purified by chromatography. Affinities of protein-protein interactions were measured by surface plasmon resonance (SPR) and thermal stabilities of proteins were determined by differential scanning fluorimetry. All experiments were performed at least in triplicate. Protein crystals were obtained by hanging drop vapor diffusion. The protein crystal structures were solved by molecular replacement and underwent several rounds of automated refinement. Molecular dynamics simulations were performed using the AMBER molecular dynamics package. RESULTS/ANTICIPATED RESULTS: A T cell receptor (TCR) expressed by tumor-infiltrating T cells exhibited a 20-fold stronger binding affinity to the neoantigen peptide compared to the self-peptide. X-ray crystal structures of the peptides with the major histocompatibility complex (MHC) protein demonstrated that a non-mutated residue in the peptide samples different positions with the mutation. The difference in conformations of the non-mutated residue was supported by molecular dynamics simulations. Crystal structures of the TCR engaging both peptide/MHCs suggested that the conformation favored by the mutant peptide was crucial for TCR binding. The TCR bound the neoantigen/MHC with faster binding kinetics. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results suggest that the mutation impacts the conformation of another residue in the peptide, and this alteration allows for more favorable T cell receptor binding to the neoantigen. This highlights the potential of non-mutated residues in contributing to neoantigen recognition.

scholarly journals Examinations of tRNA Range of Motion Using Simulations of Cryo-EM Microscopy and X-Ray Data

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Thomas R. Caulfield ◽  
Batsal Devkota ◽  
Geoffrey C. Rollins
Keyword(s):  
Electron Microscopy ◽  
Molecular Dynamics ◽  
Range Of Motion ◽  
Molecular Dynamics Simulations ◽  
Conformational Space ◽  
X Ray ◽  
Maxwell’S Demon ◽  
Density Maps ◽  
Maxwell's Demon ◽  
Dynamics Simulations

We examined tRNA flexibility using a combination of steered and unbiased molecular dynamics simulations. Using Maxwell's demon algorithm, molecular dynamics was used to steer X-ray structure data toward that from an alternative state obtained from cryogenic-electron microscopy density maps. Thus, we were able to fit X-ray structures of tRNA onto cryogenic-electron microscopy density maps for hybrid states of tRNA. Additionally, we employed both Maxwell's demon molecular dynamics simulations and unbiased simulation methods to identify possible ribosome-tRNA contact areas where the ribosome may discriminate tRNAs during translation. Herein, we collected >500 ns of simulation data to assess the global range of motion for tRNAs. Biased simulations can be used to steer between known conformational stop points, while unbiased simulations allow for a general testing of conformational space previously unexplored. The unbiased molecular dynamics data describes the global conformational changes of tRNA on a sub-microsecond time scale for comparison with steered data. Additionally, the unbiased molecular dynamics data was used to identify putative contacts between tRNA and the ribosome during the accommodation step of translation. We found that the primary contact regions were H71 and H92 of the 50S subunit and ribosomal proteins L14 and L16.

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