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Ruxolitinib (RUX) is extensively used in myelofibrosis (MF). Despite its early efficacy, most patients lose response over time and, after discontinuation, have a worse overall survival (OS). Currently, response criteria able to predict OS in RUX-treated patients are lacking, leading to uncertainty regarding the switch to second-line treatments. In this study, we investigated predictors of survival collected after 6 months of RUX in 209 MF patients participating in the real-world ambispective observational RUXOREL-MF study (NCT03959371). Multivariable analysis identified the following risk factors: (1) RUX dose <20 mg twice daily at baseline, months 3 and 6 (hazard ratio [HR], 1.79; 95% confidence interval [CI], 1.07-3.00; P = .03), (2) palpable spleen length reduction from baseline ≤30% at months 3 and 6 (HR, 2.26; 95% CI, 1.40-3.65; P = .0009), (3) red blood cell (RBC) transfusion need at months 3 and/or 6 (HR, 1.66; 95% CI, 0.95-2.88; P = .07), and (4) RBC transfusion need at all time points (ie, baseline and months 3 and 6; HR, 2.32; 95% CI, 1.19-4.54; P = .02). Hence, we developed a prognostic model, named Response to Ruxolitinib After 6 Months (RR6), dissecting 3 risk categories: low (median OS, not reached), intermediate (median OS, 61 months; 95% CI, 43-80), and high (median OS, 33 months; 95% CI, 21-50). The RR6 model was validated and confirmed in an external cohort comprised of 40 MF patients. In conclusion, the RR6 prognostic model allows for the early identification of RUX-treated MF patients with impaired survival who might benefit from a prompt treatment shift.

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

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.

Protein–protein interactions are the basis of many important physiological processes and are currently promising, yet difficult, targets for drug discovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated interactions are pivotal for cancer cell survival; the interaction of the BIR1 domain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the TNF receptor, promoting the activation of the NF-κB survival pathway. In this work, using a combined in silico–in vitroapproach, we identified a drug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We demonstrated in vitroits ability to interfere with the assembly of the cIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the compound’s mode of action through 248 parallel unbiased molecular dynamics simulations of 300 ns (totaling almost 75 μs of all-atom sampling), which identified multiple binding modes to the BIR1 domain of cIAP2 viaclustering and ensemble docking. NF023 is, thus, a promising protein–protein interaction disruptor, representing a starting point to develop modulators of NF-κB-mediated cell survival in cancer. This study represents a model procedure that shows the use of large-scale molecular dynamics methods to typify promiscuous interactors.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework’s flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org.

The proper choice of collective variables (CVs) is central to biased-sampling free energy reconstruction methods in molecular dynamics simulations. The PLUMED 2 library, for instance, provides several sophisticated CV choices, implemented in a C++ framework; however, developing new CVs is still time consuming due to the need to provide code for the analytical derivatives of all functions with respect to atomic coordinates. We present two solutions to this problem, namely (a) symbolic differentiation and code generation, and (b) automatic code differentiation, in both cases leveraging open-source libraries (SymPy and Stan Math, respectively). The two approaches are demonstrated and discussed in detail implementing a realistic example CV, the local radius of curvature of a polymer. Users may use the code as a template to streamline the implementation of their own CVs using high-level constructs and automatic gradient computation. Program summary Program Title: Practical approaches to the differentiation of collective variables in free energy codes: computer-algebra code generation and automatic differentiation Program Files doi: http://dx.doi.org/10.17632/r4r67bvkdn.1 Licensing provisions: GNU Lesser General Public License Version 3 (LGPL-3) Programming languages: C++, Python Nature of problem: The C++ implementation of collective variables (CVs, functions of atomic coordinates to be used in biased sampling applications) in biasing libraries for atomistic simulations, such as PLUMED [1], requires computation of both the variable and its gradient with respect to the atomic coordinates; coding and testing the analytical derivatives complicate the implementation of new CVs. Solution method: The paper shows two approaches to automate the computation of CV gradients, namely, symbolic differentiation with code generation and automatic code differentiation, demonstrating their implementation entirely with open-source software (respectively, SymPy and the Stan Math Library). Additional comments: The paper’s accompanying code serves as an example and template for the methods described in the paper; it is distributed as the two modules curvature_codegen and curvature_autodiff integrated in PLUMED 2’s source tree; the latest version is available at https://github.com/tonigi/plumed2-automatic-gradients . [1] Tribello GA, Bonomi M, Branduardi D, Camilloni C, Bussi G. PLUMED 2: New feathers for an old bird. Computer Physics Communications. 2014 Feb;185(2):604-13. [ABSTRACT FROM AUTHOR]

Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry. Here, we review successful applications in the drug discovery domain developed over these recent years of GPU-based MD. We also retrospectively analyse limitations that have been overcome over the years and give a perspective on challenges that remain to be addressed.

Molecular dynamics (MD) simulations allow the exploration of the phase space of biopolymers through the integration of equations of motion of their constituent atoms. The analysis of MD trajectories often relies on the choice of collective variables (CVs) along which the dynamics of the system is projected. We developed a graphical user interface (GUI) for facilitating the interactive choice of the appropriate CVs. The GUI allows: defining interactively new CVs; partitioning the configurations into microstates characterized by similar values of the CVs; calculating the free energies of the microstates for both unbiased and biased (metadynamics) simulations; clustering the microstates in kinetic basins; visualizing the free energy landscape as a function of a subset of the CVs used for the analysis. A simple mouse click allows one to quickly inspect structures corresponding to specific points in the landscape. Program summary Program Title: METAGUI 3 Program Files doi: http://dx.doi.org/10.17632/wyxjndwkbp.1 Licensing provisions: GPLv3 Programming language: Tcl/Tk, Fortran Journal reference of previous version: METAGUI [1] Does the new version supersede the previous version?: No Nature of problem: Choose the appropriate collective variables for describing the thermodynamics and kinetics of a biomolecular system through biased and unbiased molecular dynamics. Solution method: Provide an environment to compute and visualize free energy surfaces as a function of collective variables, interactively defined. Additional comments: METAGUI 3 is not a standalone program but a plugin that provides analysis features within VMD (version 1.9.2 or higher). [1] X. Biarnés, F. Pietrucci, F. Marinelli, A. Laio, METAGUI. A VMD interface for analyzing metadynamics and molecular dynamics simulations, Computer Physics Communications 183 (2012) 203–211. [ABSTRACT FROM AUTHOR]

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative option for patients with primary myelofibrosis (PMF), but information on its net advantage over conventional therapies is lacking. Using ad hoc statistical analysis, we determined outcomes in 438 patients <65 years old at diagnosis who received allogenic SCT (n = 190) or conventional therapies (n = 248). Among patients at low risk per the Dynamic International Prognostic Scoring System (DIPSS) model, the relative risk of death after allogenic SCT vs those treated with nontransplant modalities was 5.6 (95% CI, 1.7-19; P = .0051); for intermediate-1 risk it was 1.6 (95% CI, 0.79-3.2; P = .19), for intermediate-2 risk, 0.55 (95% CI, 0.36-0.83; P = .005), and for high risk, 0.37 (95% CI, 0.21-0.66; P = .0007). Thus, patients with intermediate-2 or high-risk PMF clearly benefit from allogenic SCT. Patients at low risk should receive nontransplant therapy, whereas individual counseling is indicated for patients at intermediate-1 risk.

PLUMED-GUI is an interactive environment to develop and test complex PLUMED scripts within the Visual Molecular Dynamics (VMD) environment. Computational biophysicists can take advantage of both PLUMED’s rich syntax to define collective variables (CVs) and VMD’s chemically-aware atom selection language, while working within a natural point-and-click interface. Pre-defined templates and syntax mnemonics facilitate the definition of well-known reaction coordinates. Complex CVs, e.g. involving reference snapshots used for RMSD or native contacts calculations, can be built through dialogs that provide a synoptic view of the available options. Scripts can be either exported for use in simulation programs, or evaluated on the currently loaded molecular trajectories. Script development takes place without leaving VMD, thus enabling an incremental try–see–modify development model for molecular metrics. Program summary: Program title: PLUMED-GUI (Collective variable analysis plugin) Catalogue identifier: AERU_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AERU_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: 3-clause BSD Open Source No. of lines in distributed program, including test data, etc.: 2651 No. of bytes in distributed program, including test data, etc.: 32359 Distribution format: tar.gz Programming language: TCL/TK. Computer: Workstations, PCs. Operating system: Linux/Unix, OSX, Windows. RAM: Sufficient to run PLUMED [1] and VMD [2]. Classification: 3, 23. Subprograms used: Nature of problem: Compute and visualize values of collective variables on molecular dynamics trajectories from within VMD, and interactively develop biasing scripts for the estimation of free-energy surfaces in PLUMED. Solution method: A graphical user interface is integrated in VMD and allows the user to interactively develop and run analysis scripts. Menus and dialogs provide mnemonics and documentation on the syntax to define complex CVs. Restrictions: Tested on systems up to 100,000 atoms. Unusual features: VMD–PLUMED is not a standalone program but a plugin that provides access to PLUMED’s analysis features from within VMD. Additional comments: Distributed with VMD since version 1.9.0. Manual update may be required to access the latest features. Running time: Computations of the values of collective variables, performed by the underlying PLUMED code, depends on the size of the system and the length of the trajectory; it is generally negligible with respect to simulation time. References: [[1]] G. A. Tribello, M. Bonomi, D. Branduardi, C. Camilloni, G. Bussi, PLUMED 2: New feathers for an old bird, Computer Physics Communications 185 (2014) 604. [[2]] W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics, J Mol Graph 14 (1996) 33–38. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations have a prominent role in biophysics and drug discovery due to the atomistic information they provide on the structure, energetics and dynamics of biomolecules. Specialized software packages are required to analyze simulated trajectories, either interactively or via scripts, to derive quantities of interest and provide insight for further experiments. This paper presents the Density Profile Tool, a package that enhances the Visual Molecular Dynamics environment with the ability to interactively compute and visualize 1-D projections of various density functions of molecular models. We describe how the plugin is used to perform computations both via a graphical interface and programmatically. Results are presented for realistic examples, all-atom bilayer models, showing how mass and electron densities readily provide measurements such as membrane thickness, location of structural elements, and how they compare to X-ray diffraction experiments. Program summary: Program title: Density Profile Tool Catalogue identifier: AEQM_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEQM_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: yes No. of lines in distributed program, including test data, etc.: 1742 No. of bytes in distributed program, including test data, etc.: 12764 Distribution format: tar.gz Programming language: TCL/TK. Computer: Any, with or without graphical display. Operating system: Linux/Unix, OSX, Windows. RAM: VMD should be able to hold the trajectory in memory. Classification: 3, 23. External routines: VMD (version 1.9 or higher) (http://www.ks.uiuc.edu/Research/vmd/). Nature of problem: Compute and visualize one-dimensional density profiles of molecular dynamics trajectories in the VMD environment, either interactively or programmatically. Solution method: Density profiles are computed by binning the simulation space into slabs of finite thickness. A graphical user interface allows the choice of the atomic property (number, mass, charge, electrons) and the details of the binning. Restrictions: The current version only supports orthorhombic cells. Unusual features: The Density Profile Tool is not a standalone program but a plug-in that enhances VMD’s analysis features. Running time: A contemporary PC completes the analysis of 500 frames of the example system discussed in the paper (35,000 atoms) in under 1 min. [ABSTRACT FROM AUTHOR]

Steered molecular dynamics (SMD) simulations for the calculation of free energies are well suited for high-throughput molecular simulations on a distributed infrastructure due to the simplicity of the setup and parallel granularity of the runs. However, so far, the computational cost limited the estimation of the free energy typically over just a few pullings, thus impeding the evaluation of statistical uncertainties involved. In this work, we performed two thousand pulls for the permeation of a potassium ion in the gramicidin A pore by all-atom molecular dynamics in order to assess the bidirectional SMD protocol with a proper amount of sampling. The estimated free energy profile still shows a statistical error of several kcal/mol, while the work distributions are estimated to be non-Gaussian at pulling speeds of 10 Å/ns. We discuss the methodology and the confidence intervals in relation to increasing amounts of computed trajectories and how different permeation pathways for the potassium ion, knock-on and sideways, affect the sampling and the free energy estimation.

Abstract: Distributed computing (DC) projects tackle large computational problems by exploiting the donated processing power of thousands of volunteered computers, connected through the Internet. To efficiently employ the computational resources of one of world''s largest DC efforts, GPUGRID, the project scientists require tools that handle hundreds of thousands of tasks which run asynchronously and generate gigabytes of data every day. We describe RBoinc, an interface that allows computational scientists to embed the DC methodology into the daily work-flow of high-throughput experiments. By extending the Berkeley Open Infrastructure for Network Computing (BOINC), the leading open-source middleware for current DC projects, with mechanisms to submit and manage large-scale distributed computations from individual workstations, RBoinc turns distributed grids into cost-effective virtual resources that can be employed by researchers in work-flows similar to conventional supercomputers. The GPUGRID project is currently using RBoinc for all of its in silico experiments based on molecular dynamics methods, including the determination of binding free energies and free energy profiles in all-atom models of biomolecules. [Copyright &y& Elsevier]

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background: Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms (MPN) that can progress to post-PV (PPV) myelofibrosis (MF) and post-ET (PET) MF, from now on referred to as secondary myelofibrosis (SMF). Recent studies have shown an increased risk of developing solid tumors (ST) in MPN patients in comparison to the general population. Information on development of ST in SMF is scant.

Vannucchi: Novartis Pharmaceuticals Corporation: Consultancy, Honoraria, Research Funding.

No relevant conflicts of interest to declare.