Steroid receptor drugs have been available for more than half a century, but details of the ligand binding mechanism have remained elusive. We solved X-ray structures of the glucocorticoid and mineralocorticoid receptors to identify a conserved plasticity at the helix 6-7 region that extends the ligand binding pocket toward the receptor surface. Since none of the endogenous ligands exploit this region, we hypothesized that it constitutes an integral part of the binding event. Extensive all-atom unbiased ligand exit and entrance simulations corroborate a ligand binding pathway that gives the observed structural plasticity a key functional role. Kinetic measurements reveal that the receptor residence time correlates with structural rearrangements observed in both structures and simulations. Ultimately, our findings reveal why nature has conserved the capacity to open up this region, and highlight how differences in the details of the ligand entry process result in differential evolutionary constraints across the steroid receptors.

Tyrosinases are responsible for melanin formation in all life domains. Tyrosinase inhibitors are used for the prevention of severe skin diseases, in skin-whitening creams and to avoid fruit browning, however continued use of many such inhibitors is considered unsafe. In this study we provide conclusive evidence of the inhibition mechanism of two well studied tyrosinase inhibitors, KA (kojic acid) and HQ (hydroquinone), which are extensively used in hyperpigmentation treatment. KA is reported in the literature with contradicting inhibition mechanisms, while HQ is described as both a tyrosinase inhibitor and a substrate. By visualization of KA and HQ in the active site of TyrBm crystals, together with molecular modeling, binding constant analysis and kinetic experiments, we have elucidated their mechanisms of inhibition, which was ambiguous for both inhibitors. We confirm that while KA acts as a mixed inhibitor, HQ can act both as a TyrBm substrate and as an inhibitor.

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.

In this study, we performed an extensive exploration of the ligand entry mechanism for members of the steroid nuclear hormone receptor family (androgen receptor, estrogen receptor α, glucocorticoid receptor, mineralocorticoid receptor, and progesterone receptor) and their endogenous ligands. The exploration revealed a shared entry path through the helix 3, 7, and 11 regions. Examination of the x-ray structures of the receptor-ligand complexes further showed two distinct folds of the helix 6-7 region, classified as "open" and "closed", which could potentially affect ligand binding. To improve sampling of the helix 6-7 loop, we incorporated motion modes based on principal component analysis of existing crystal structures of the receptors and applied them to the protein-ligand sampling. A detailed comparison with the anisotropic network model (an elastic network model) highlights the importance of flexibility in the entrance region. While the binding (interaction) energy of individual simulations can be used to score different ligands, extensive sampling further allows us to predict absolute binding free energies and analyze reaction kinetics using Markov state models and Perron-cluster cluster analysis, respectively. The predicted relative binding free energies for three ligands binding to the progesterone receptor are in very good agreement with experimental results and the Perron-cluster cluster analysis highlighted the importance of a peripheral binding site. Our analysis revealed that the flexibility of the helix 3, 7, and 11 regions represents the most important factor for ligand binding. Furthermore, the hydrophobicity of the ligand influences the transition between the peripheral and the active binding site.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein–ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Prolyl oligopeptidase (POP) is a large 80 kDa protease, which cleaves oligopeptides at the C-terminal side of proline residues and constitutes an important pharmaceutical target. Despite the existence of several crystallographic structures, there is an open debate about migration (entrance and exit) pathways for ligands, and their coupling with protein dynamics. Recent studies have shown the capabilities of molecular dynamics and classical force fields in describing spontaneous binding events and nonbiased ligand migration pathways. Due to POP's size and to the buried nature of its active site, an exhaustive sampling by means of conventional long enough molecular dynamics trajectories is still a nearly impossible task. Such a level of sampling, however, is possible with the breakthrough protein energy landscape exploration technique. Here, we present an exhaustive sampling of POP with a known inhibitor, Z-pro-prolinal. In >3000 trajectories Z-pro-prolinal explores all the accessible surface area, showing multiple entrance events into the large internal cavity through the pore in the β-propeller domain. Moreover, we modeled a natural substrate binding and product release by predicting the entrance of an undecapeptide substrate, followed by manual active site cleavage and nonbiased exit of one of the products (a dipeptide). The product exit shows preference from a flexible 18-amino acid residues loop, pointing to an overall mechanism where entrance and exit occur in different sites.

]. This article may be used for noncommercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. BOSS http://zarbi.chem.yale.edu/soft ware.html Jorgensen lab. General purpose molecular modeling system that performs molecular mechanics calculations, Metropolis MC statistical simulations, and semiempirical quantum mechanics calculations. Free for non-profit/commercial for profit. MCPRO http://zarbi.chem.yale.edu/soft ware.html Jorgensen lab. MC statistical mechanics simulations of peptides, proteins, and nucleic acids; it was derived from BOSS. Free for nonprofit/commercial for profit. PELE https:pele.bsc.es, www.nostrumbodiscvery.com Guallar lab. MC specialized in mapping protein-ligand interactions. Free for non profit/ commercial for profit. Sire http://siremol.org Christopher Woods, et al. C++/Python rewrite of ProtoMS. It now includes many more modeling modules. GNU General Public License. ICM http://www.molsoft.com/icm_pr o.html Molsoft, LLC Biased probability MC. Prediction of the threedimensional structure of peptides and proteins. Commercial. Tinker https://dasher.wustl.edu/tinker Ponder lab. General molecular modeling program including a MC minimization technique. Free of charge, registered under U.S. Copyright law. Macromodel https://www.schrodinger.com/ macromodel Schrödinger, LLC Mixed MC algorithms and other molecular modeling methods. Commercial. Schrodinger also distributes a (commercial) MCPRO+ version.

Normal mode methods are becoming a popular alternative to sample the conformational landscape of proteins. In this study, we describe the implementation of an internal coordinate normal mode analysis method and its application in exploring protein flexibility by using the Monte Carlo method PELE. This new method alternates two different stages, a perturbation of the backbone through the application of torsional normal modes, and a resampling of the side chains. We have evaluated the new approach using two test systems, ubiquitin and c-Src kinase, and the differences to the original ANM method are assessed by comparing both results to reference molecular dynamics simulations. The results suggest that the sampled phase space in the internal coordinate approach is closer to the molecular dynamics phase space than the one coming from a Cartesian coordinate anisotropic network model. In addition, the new method shows a great speedup (∼5-7×), making it a good candidate for future normal mode implementations in Monte Carlo methods.