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Ferruz N.,University Pompeu Fabra | Harvey M.J.,Barcelona Biomedical Research Park PRBB | Mestres J.,University Pompeu Fabra | De Fabritiis G.,University Pompeu Fabra | De Fabritiis G.,Catalan Institution for Research and Advanced Studies
Journal of Chemical Information and Modeling | Year: 2015

Novel bioactive molecules can be rationally designed by growing and linking small fragments. Because fragments are fast and promiscuous, it is common to have contradictory hit results between different experimental screening techniques. Here, we simultaneously determine fragment binding poses, affinities, and kinetics on a focused library of 42 fragments against the serine protease factor Xa using multimillisecond molecular dynamics simulations. We predict experimental poses of 12 over 15 S1 crystal structures, and affinities are recovered for 4 out of 6. A kinetic map of protein cavities is computed in terms of on- and off-rates as well as insights into secondary ligand poses. The results suggest that the approach can be useful to recapitulate discordant fragment screening data. © 2015 American Chemical Society. Source


Harvey M.J.,Barcelona Biomedical Research Park PRBB | De Fabritiis G.,University Pompeu Fabra | De Fabritiis G.,Catalan Institution for Research and Advanced Studies
Journal of Chemical Information and Modeling | Year: 2015

We present AceCloud, an on-demand service for molecular dynamics simulations. AceCloud is designed to facilitate the secure execution of large ensembles of simulations on an external cloud computing service (currently Amazon Web Services). The AceCloud client, integrated into the ACEMD molecular dynamics package, provides an easy-to-use interface that abstracts all aspects of interaction with the cloud services. This gives the user the experience that all simulations are running on their local machine, minimizing the learning curve typically associated with the transition to using high performance computing services. © 2015 American Chemical Society. Source


Farres J.,Hospital del Mar Medical Research Institute IMIM | Llacuna L.,Hospital del Mar Medical Research Institute IMIM | Martin-Caballero J.,Barcelona Biomedical Research Park PRBB | Martinez C.,CIBER ISCIII | And 13 more authors.
Cell Death and Differentiation | Year: 2015

Erythropoiesis is a tightly regulated process in which multipotential hematopoietic stem cells produce mature red blood cells. Here we show that deletion of poly(ADP-ribose) polymerase-2 (PARP-2) in mice leads to chronic anemia at steady state, despite increased erythropoietin plasma levels, a phenomenon not observed in mice lacking PARP-1. Loss of PARP-2 causes shortened lifespan of erythrocytes and impaired differentiation of erythroid progenitors. In erythroblasts, PARP-2 deficiency triggers replicative stress, as indicated by the presence of micronuclei, the accumulation of γ-H2AX (phospho-histone H2AX) in S-phase cells and constitutive CHK1 and replication protein A phosphorylation. Transcriptome analyses revealed the activation of the p53-dependent DNA-damage response pathways in PARP-2-deficient cells, culminating in the upregulation of cell-cycle and cell death regulators, concomitant with G2M arrest and apoptosis. Strikingly, while loss of the proapoptotic p53 target gene Puma restored hematocrit levels in the PARP-2-deficient mice, loss of the cell-cycle regulator and CDK inhibitor p21 leads to perinatal death by exacerbating impaired fetal liver erythropoiesis in PARP-2-deficient embryos. Although the anemia displayed by PARP-2-deficient mice is compatible with life, mice die rapidly when exposed to stress-induced enhanced hemolysis. Our results pinpoint an essential role for PARP-2 in erythropoiesis by limiting replicative stress that becomes essential in the absence of p21 and in the context of enhanced hemolysis, highlighting the potential effect that might arise from the design and use of PARP inhibitors that specifically inactivate PARP proteins. © 2015 Macmillan Publishers Limited. All rights reserved. Source


Doerr S.,University Pompeu Fabra | Harvey M.J.,Barcelona Biomedical Research Park PRBB | Noe F.,Free University of Berlin | De Fabritiis G.,Catalan Institution for Research and Advanced Studies
Journal of Chemical Theory and Computation | Year: 2016

Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features. © 2016 American Chemical Society. Source


Perez-Hernandez G.,Free University of Berlin | Paul F.,Free University of Berlin | Paul F.,Max Planck Institute of Colloids and Interfaces | Giorgino T.,CNR Institute of Biomedical Engineering | And 2 more authors.
Journal of Chemical Physics | Year: 2013

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered pep-tide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis. © 2013 AIP Publishing LLC. Source

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