Dataset Information

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

ABSTRACT: Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

SUBMITTER: Kamenik AS

PROVIDER: S-EPMC5974701 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Kamenik Anna S AS Lessel Uta U Fuchs Julian E JE Fox Thomas T Liedl Klaus R KR

Journal of chemical information and modeling 20180420 5

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensembl ...[more]

PMID: 29652495

Similar Datasets

Project description:Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-rayppw), and (v) to the X-rayppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-rayppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-rayppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-rayppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Project description:Ligand-induced conformational perturbations in androgen receptor (AR) are important in coactivator recruitment and transactivation. However, molecular rearrangements in AR ligand-binding domain (AR-LBD) associated with agonist binding and their kinetic and thermodynamic parameters are poorly understood. We used steady-state second-derivative absorption and emission spectroscopy, pressure and temperature perturbations, and 4,4'-bis-anilinonaphthalene 8-sulfonate (bis-ANS) partitioning to determine the kinetics and thermodynamics of the conformational changes in AR-LBD after dihydrotestosterone (DHT) binding. In presence of DHT, the second-derivative absorption spectrum showed a red shift and a change in peak-to-peak distance. Emission intensity increased upon DHT binding, and center of spectral mass was blue shifted, denoting conformational changes resulting in more hydrophobic environment for tyrosines and tryptophans within a more compact DHT-bound receptor. In pressure perturbation calorimetry, DHT-induced energetic stabilization increased the Gibbs free energy of unfolding to 8.4 +/- 1.3 kcal/mol from 3.5 +/- 1.6 kcal/mol. Bis-ANS partitioning studies revealed that upon DHT binding, AR-LBD underwent biphasic rearrangement with a high activation energy (13.4 kcal/mol). An initial, molten globule-like burst phase (k approximately 30 sec(-1)) with greater solvent accessibility was followed by rearrangement (k approximately 0.01 sec(-1)), leading to a more compact conformation than apo-AR-LBD. Molecular simulations demonstrated unique sensitivity of tyrosine and tryptophan residues during pressure unfolding with rearrangement of residues in the coactivator recruitment surfaces distant from the ligand-binding pocket. In conclusion, DHT binding leads to energetic stabilization of AR-LBD domain and substantial rearrangement of residues distant from the ligand-binding pocket. DHT binding to AR-LBD involves biphasic receptor rearrangement including population of a molten globule-like intermediate state.

Dataset Information

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Publications

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets