Suchergebnisse

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. ; This work has the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. ; Peer Reviewed ; Postprint (author's final draft)

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. ; This work has the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. ; Peer Reviewed ; Postprint (author's final draft)

Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread contemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery. ; ACKNOWLEDGEMENTS The Structural Genomics Consortium is a registered charity (no: 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute [OGI-196], Janssen, Merck KGaA (aka EMD in Canada and US), Pfizer, Takeda and the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 875510. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Ontario Institute for Cancer Research, Royal Institution for ...