Loss of signalling via Gα13 in germinal centre B-cell-derived lymphoma.

Germinal centre B-cell-like diffuse large B-cell lymphoma (GCB-DLBCL) is a common malignancy, yet the signalling pathways that are deregulated and the factors leading to its systemic dissemination are poorly defined. Work in mice showed that sphingosine-1-phosphate receptor-2 (S1PR2), a Gα12 and Gα13 coupled receptor, promotes growth regulation and local confinement of germinal centre B cells. Recent deep sequencing studies of GCB-DLBCL have revealed mutations in many genes in this cancer, including in GNA13 (encoding Gα13) and S1PR2 (refs 5,6, 7). Here we show, using in vitro and in vivo assays, that GCB-DLBCL-associated mutations occurring in S1PR2 frequently disrupt the receptor's Akt and migration inhibitory functions. Gα13-deficient mouse germinal centre B cells and human GCB-DLBCL cells were unable to suppress pAkt and migration in response to S1P, and Gα13-deficient mice developed germinal centre B-cell-derived lymphoma. Germinal centre B cells, unlike most lymphocytes, are tightly confined in lymphoid organs and do not recirculate. Remarkably, deficiency in Gα13, but not S1PR2, led to germinal centre B-cell dissemination into lymph and blood. GCB-DLBCL cell lines frequently carried mutations in the Gα13 effector ARHGEF1, and Arhgef1 deficiency also led to germinal centre B-cell dissemination. The incomplete phenocopy of Gα13- and S1PR2 deficiency led us to discover that P2RY8, an orphan receptor that is mutated in GCB-DLBCL and another germinal centre B-cell-derived malignancy, Burkitt's lymphoma, also represses germinal centre B-cell growth and promotes confinement via Gα13. These findings identify a Gα13-dependent pathway that exerts dual actions in suppressing growth and blocking dissemination of germinal centre B cells that is frequently disrupted in germinal centre B-cell-derived lymphoma.

Overcoming potential energy distortions in constrained internal coordinate molecular dynamics simulations.

The Internal Coordinate Molecular Dynamics (ICMD) method is an attractive molecular dynamics (MD) method for studying the dynamics of bonded systems such as proteins and polymers. It offers a simple venue for coarsening the dynamics model of a system at multiple hierarchical levels. For example, large scale protein dynamics can be studied using torsional dynamics, where large domains or helical structures can be treated as rigid bodies and the loops connecting them as flexible torsions. ICMD with such a dynamic model of the protein, combined with enhanced conformational sampling method such as temperature replica exchange, allows the sampling of large scale domain motion involving high energy barrier transitions. Once these large scale conformational transitions are sampled, all-torsion, or even all-atom, MD simulations can be carried out for the low energy conformations sampled via coarse grained ICMD to calculate the energetics of distinct conformations. Such hierarchical MD simulations can be carried out with standard all-atom forcefields without the need for compromising on the accuracy of the forces. Using constraints to treat bond lengths and bond angles as rigid can, however, distort the potential energy landscape of the system and reduce the number of dihedral transitions as well as conformational sampling. We present here a two-part solution to overcome such distortions of the potential energy landscape with ICMD models. To alleviate the intrinsic distortion that stems from the reduced phase space in torsional MD, we use the Fixman compensating potential. To additionally alleviate the extrinsic distortion that arises from the coupling between the dihedral angles and bond angles within a force field, we propose a hybrid ICMD method that allows the selective relaxing of bond angles. This hybrid ICMD method bridges the gap between all-atom MD and torsional MD. We demonstrate with examples that these methods together offer a solution to eliminate the potential energy distortions encountered in constrained ICMD simulations of peptide molecules.

GneimoSim: a modular internal coordinates molecular dynamics simulation package.

The generalized Newton-Euler inverse mass operator (GNEIMO) method is an advanced method for internal coordinates molecular dynamics (ICMD). GNEIMO includes several theoretical and algorithmic advancements that address longstanding challenges with ICMD simulations. In this article, we describe the GneimoSim ICMD software package that implements the GNEIMO method. We believe that GneimoSim is the first software package to include advanced features such as the equipartition principle derived for internal coordinates, and a method for including the Fixman potential to eliminate systematic statistical biases introduced by the use of hard constraints. Moreover, by design, GneimoSim is extensible and can be easily interfaced with third party force field packages for ICMD simulations. Currently, GneimoSim includes interfaces to LAMMPS, OpenMM, and Rosetta force field calculation packages. The availability of a comprehensive Python interface to the underlying C++ classes and their methods provides a powerful and versatile mechanism for users to develop simulation scripts to configure the simulation and control the simulation flow. GneimoSim has been used extensively for studying the dynamics of protein structures, refinement of protein homology models, and for simulating large scale protein conformational changes with enhanced sampling methods. GneimoSim is not limited to proteins and can also be used for the simulation of polymeric materials.

Protein structure refinement of CASP target proteins using GNEIMO torsional dynamics method.

A longstanding challenge in using computational methods for protein structure prediction is the refinement of low-resolution structural models derived from comparative modeling methods into highly accurate atomistic models useful for detailed structural studies. Previously, we have developed and demonstrated the utility of the internal coordinate molecular dynamics (MD) technique, generalized Newton-Euler inverse mass operator (GNEIMO), for refinement of small proteins. Using GNEIMO, the high-frequency degrees of freedom are frozen and the protein is modeled as a collection of rigid clusters connected by torsional hinges. This physical model allows larger integration time steps and focuses the conformational search in the low frequency torsional degrees of freedom. Here, we have applied GNEIMO with temperature replica exchange to refine low-resolution protein models of 30 proteins taken from the continuous assessment of structure prediction (CASP) competition. We have shown that GNEIMO torsional MD method leads to refinement of up to 1.3 Å in the root-mean-square deviation in coordinates for 30 CASP target proteins without using any experimental data as restraints in performing the GNEIMO simulations. This is in contrast with the unconstrained all-atom Cartesian MD method performed under the same conditions, where refinement requires the use of restraints during the simulations.

Structure and dynamics of G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are seven helical transmembrane proteins that mediate cell-to-cell communication. They also form the largest superfamily of drug targets. Hence detailed studies of the three dimensional structure and dynamics are critical to understanding the functional role of GPCRs in signal transduction pathways, and for drug design. In this chapter we compare the features of the crystal structures of various biogenic amine receptors, such as ß1 and ß2 adrenergic receptors, dopamine D3 receptor, M2 and M3 muscarinic acetylcholine receptors. This analysis revealed that conserved residues are located facing the inside of the transmembrane domain in these GPCRs improving the efficiency of packing of these structures. The NMR structure of the chemokine receptor CXCR1 without any ligand bound, shows significant dynamics of the transmembrane domain, especially the helical kink angle on the transmembrane helix6. The activation mechanism of the ß2-adrenergic receptor has been studied using multiscale computational methods. The results of these studies showed that the receptor without any ligand bound, samples conformations that resemble some of the structural characteristics of the active state of the receptor. Ligand binding stabilizes some of the conformations already sampled by the apo receptor. This was later observed in the NMR study of the dynamics of human ß2-adrenergic receptor. The dynamic nature of GPCRs leads to a challenge in obtaining purified receptors for biophysical studies. Deriving thermostable mutants of GPCRs has been a successful strategy to reduce the conformational heterogeneity and stabilize the receptors. This has lead to several crystal structures of GPCRs. However, the cause of how these mutations lead to thermostability is not clear. Computational studies are beginning to shed some insight into the possible structural basis for the thermostability. Molecular Dynamics simulations studying the conformational ensemble of thermostable mutants have shown that the stability could arise from both enthalpic and entropic factors. There are regions of high stress in the wild type GPCR that gets relieved upon mutation conferring thermostability.

Advanced techniques for constrained internal coordinate molecular dynamics.

Internal coordinate molecular dynamics (ICMD) methods provide a more natural description of a protein by using bond, angle, and torsional coordinates instead of a Cartesian coordinate representation. Freezing high-frequency bonds and angles in the ICMD model gives rise to constrained ICMD (CICMD) models. There are several theoretical aspects that need to be developed to make the CICMD method robust and widely usable. In this article, we have designed a new framework for (1) initializing velocities for nonindependent CICMD coordinates, (2) efficient computation of center of mass velocity during CICMD simulations, (3) using advanced integrators such as Runge-Kutta, Lobatto, and adaptive CVODE for CICMD simulations, and (4) cancelling out the "flying ice cube effect" that sometimes arises in Nosé-Hoover dynamics. The Generalized Newton-Euler Inverse Mass Operator (GNEIMO) method is an implementation of a CICMD method that we have developed to study protein dynamics. GNEIMO allows for a hierarchy of coarse-grained simulation models based on the ability to rigidly constrain any group of atoms. In this article, we perform tests on the Lobatto and Runge-Kutta integrators to determine optimal simulation parameters. We also implement an adaptive coarse-graining tool using the GNEIMO Python interface. This tool enables the secondary structure-guided "freezing and thawing" of degrees of freedom in the molecule on the fly during molecular dynamics simulations and is shown to fold four proteins to their native topologies. With these advancements, we envision the use of the GNEIMO method in protein structure prediction, structure refinement, and in studying domain motion.