Investigation of ligand selectivity and activation dynamics of G protein-coupled receptors using enhanced sampling simulations

G protein-coupled receptors (GPCRs) are a large superfamily of transmembrane proteins found in eukaryotes. They play a crucial role in the transduction of signals across the plasma membrane of cells, and are involved in the regulation of a plethora of processes. Due to their function in countless biological pathways they have a primary role in many pathological conditions, and are thus therapeutic targets of great importance. Notwithstanding the growing availability of X-ray and cryo-EM structures and the intense involvement of the scientific community, many gaps are still present in our understanding of the mechanisms of ligand binding, receptor activation and allostery. Computational methods open the possibility for the study of the dynamics of such processes at atomistic resolution, complementing experimental findings. In this work key processes of a number of different GPCRs are explored with the use of computational approaches. Molecular dynamics and enhanced sampling methods are leveraged for sampling rare events of great interest and for the calculation of the associated free energy landscapes. In the first place our study of ligand binding and the selectivity mechanism in adenosine A2a and A1 receptors is reported, elucidating how selectivity arises from an interplay of structural factors. The activation mechanism of glucagon receptor and the coupling with a G protein is then investigated, highlighting the cooperative action of glucagon and G protein in the process. A detailed overview of allosteric antagonism in chemokine receptors is built by mining databases of experimental data and complementing this picture with insights on the dynamics of these receptors. Finally, the performance of TS-PPTIS (Transition State-Partial Path Transition State Sampling), a method for the calculation of kinetic rate constants, is studied for the prediction of ligand binding kinetic rates. The findings of this study add to the understanding of the mechanism of signal transduction through GPCRs, and detail this process from its origin outside the cell to the intracellular medium

1996 Laboratory directed research and development annual report

This report summarizes progress from the Laboratory Directed Research and Development (LDRD) program during fiscal year 1996. In addition to a programmatic and financial overview, the report includes progress reports from 259 individual R&D projects in seventeen categories. The general areas of research include: engineered processes and materials; computational and information sciences; microelectronics and photonics; engineering sciences; pulsed power; advanced manufacturing technologies; biomedical engineering; energy and environmental science and technology; advanced information technologies; counterproliferation; advanced transportation; national security technology; electronics technologies; idea exploration and exploitation; production; and science at the interfaces - engineering with atoms

Molecular Dynamics Simulation

Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate ‘first-principles’ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardly—dealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...

A Chirality-Based Quantum Leap

There is increasing interest in the study of chiral degrees of freedom occurring in matter and in electromagnetic fields. Opportunities in quantum sciences will likely exploit two main areas that are the focus of this Review: (1) recent observations of the chiral-induced spin selectivity (CISS) effect in chiral molecules and engineered nanomaterials and (2) rapidly evolving nanophotonic strategies designed to amplify chiral light-matter interactions. On the one hand, the CISS effect underpins the observation that charge transport through nanoscopic chiral structures favors a particular electronic spin orientation, resulting in large room-temperature spin polarizations. Observations of the CISS effect suggest opportunities for spin control and for the design and fabrication of room-temperature quantum devices from the bottom up, with atomic-scale precision and molecular modularity. On the other hand, chiral-optical effects that depend on both spin- and orbital-angular momentum of photons could offer key advantages in all-optical and quantum information technologies. In particular, amplification of these chiral light-matter interactions using rationally designed plasmonic and dielectric nanomaterials provide approaches to manipulate light intensity, polarization, and phase in confined nanoscale geometries. Any technology that relies on optimal charge transport, or optical control and readout, including quantum devices for logic, sensing, and storage, may benefit from chiral quantum properties. These properties can be theoretically and experimentally investigated from a quantum information perspective, which has not yet been fully developed. There are uncharted implications for the quantum sciences once chiral couplings can be engineered to control the storage, transduction, and manipulation of quantum information. This forward-looking Review provides a survey of the experimental and theoretical fundamentals of chiral-influenced quantum effects and presents a vision for their possible future roles in enabling room-temperature quantum technologies.ISSN:1936-0851ISSN:1936-086

Oak Ridge National Laboratory Institutional Plan, FY 1997--FY 2002

Three major initiatives are described, which are proposed to strengthen ORNL`s ability to support the missions of the Department: neutron science, functional genomics, and distributed computing at teraflop speeds. The laboratory missions, strategic plan, scientific and technical programs, enterprise activities, laboratory operations, and resource projections are also described

Ab initio machine learning in chemical compound space

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an {\em ab initio} view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics