Suchergebnisse

The exploration of intrinsically disordered proteins in isolation is a crucial step to understand their complex dynamical behavior. In particular, the emergence of partially ordered states has not been explored in depth. The experimental characterization of such partially ordered states remains elusive due to their transient nature. Molecular dynamics mitigates this limitation thanks to its capability to explore biologically relevant timescales while retaining atomistic resolution. Here, millisecond unbiased molecular dynamics simulations were performed in the exemplar N-terminal region of p53. In combination with state-of-the-art Markov state models, simulations revealed the existence of several partially ordered states accounting for [Formula: see text] 40% of the equilibrium population. Some of the most relevant states feature helical conformations similar to the bound structure of p53 to Mdm2, as well as novel [Formula: see text]-sheet elements. This highlights the potential complexity underlying the energy surface of intrinsically disordered proteins. ; The authors thank volunteers at GPUGRID.net for contributing with computational resources and Acellera for funding. G.D.F. acknowledges support from MINECO (Unidad de Excelencia María de Maeztu MDM-2014-0370 and BIO2017-82628-P) and FEDER. This project received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 823712 (CompBioMed2 Project).

Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. We envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery. ; The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from MINECO (BIO2017-82628-P) and FEDER, as well as 'Unidad de Excelencia María de Maeztu', funded by MINECO (MDM-2014-0370). The authors thank the European Union's Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project).

While the therapeutic effect of opioids analgesics is mainly attributed to µ-opioid receptor (MOR) activation leading to G protein signaling, their side effects have mostly been linked to β-arrestin signaling. To shed light on the dynamic and kinetic elements underlying MOR functional selectivity, we carried out close to half millisecond high-throughput molecular dynamics simulations of MOR bound to a classical opioid drug (morphine) or a potent G protein-biased agonist (TRV-130). Statistical analyses of Markov state models built using this large simulation dataset combined with information theory enabled, for the first time: a) Identification of four distinct metastable regions along the activation pathway, b) Kinetic evidence of a different dynamic behavior of the receptor bound to a classical or G protein-biased opioid agonist, c) Identification of kinetically distinct conformational states to be used for the rational design of functionally selective ligands that may eventually be developed into improved drugs; d) Characterization of multiple activation/deactivation pathways of MOR, and e) Suggestion from calculated transition timescales that MOR conformational changes are not the rate-limiting step in receptor activation. ; This work was supported by National Institutes of Health grants DA026434, DA034049, and MH107053(to M.F.) and by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 675451 (CompBio Med project; to G.D.F). Computations were run on resources available through the Scientific Computing Facility at Mount Sinai, the Extreme Science and Engineering Discovery Environment under MCB080077, which is supported by National Science Foundation grant number ACI-1053575, and the high-throughput molecular dynamics (HTMD) platform.

Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction. ; This work was supported by the National Science Foundation (CHE-1265929, CHE-1738990, and PHY-1427654), the Welch Foundation (C-1570), the MATH+ excellence cluster (AA1-6, EF1-2), the Deutsche Forschungsgemeinschaft (SFB 1114/C03, SFB 958/A04, TRR 186/A12), the European Commission (ERC CoG 772230 "ScaleCell"), the Einstein Foundation Berlin (Einstein Visiting Fellowship to C.C.), and the Alexander von Humboldt foundation (Postdoctoral fellowship to S.O.). Simulations have been performed on the computer clusters of the Center for Research Computing at Rice University, supported in part by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF Grant CNS-1338099), and on the clusters of the Department of Mathematics and Computer Science at Freie Universität, Berlin. G.D.F. acknowledges support from MINECO (Unidad de Excelencia María de Maeztu MDM-2014-0370 and BIO2017-82628-P) and FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement 675451 (CompBioMed Project). We thank the GPUGRID donors for their compute time.

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823712 (CompBioMed2 Project).

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that KDEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools. ; The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from PID2020-116564GB-I00/MICIN/AEI/10.13039/501100011033 Ministerio de Ciencia e Innovación. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823712 (CompBioMed2) and from the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia.

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives. ; The authors thank Acellera Ltd. for funding. G. D. F. acknowledges support from MINECO (BIO2014-53095-P), MICINN (PTQ-17-09079) and FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project).

The androgen receptor is a transcription factor that plays a key role in the development of prostate cancer, and its interactions with general transcription regulators are therefore of potential therapeutic interest. The mechanistic basis of these interactions is poorly understood due to the intrinsically disordered nature of the transactivation domain of the androgen receptor and the generally transient nature of the protein-protein interactions that trigger transcription. Here, we identify a motif of the transactivation domain that contributes to transcriptional activity by recruiting the C-terminal domain of subunit 1 of the general transcription regulator TFIIF. These findings provide molecular insights into the regulation of androgen receptor function and suggest strategies for treating castration-resistant prostate cancer. ; This work was supported by IRB, ICREA (X.S.), Obra Social "la Caixa" (E.D.M., E.S., and X.S.), MINECO (BIO2012-31043 and BIO2015-70092-R to X.S., BIO2014-53095-P to G.D.F.), Marató de TV3 (102030 to X.S. and 102031 to E.E.-P), the COFUND program of the European Commission (C.D.S.), the European Research Council (CONCERT, contract number 648201 to X.S.), the Ramón y Cajal program of MICINN (RYC-2011-07873 to C.W.B.), the Serra Hunter Program (E.E.-P.), AGAUR (SGR-2014-56RR14 to E.E.-P), and FEDER (G.D.F.). I.H. was supported by funding from the Chief Scientist's Office of the Scottish Government (ETM-258, ETM-382). IRB Barcelona is the recipient of a Severo Ochoa Award of Excellence from MINECO (Government of Spain).