First-Principles-Based Simulations for G Protein-Coupled Receptor Activation and for Large-Scale Nonadiabatic Electron Dynamics

This thesis focuses on simulating large molecular systems within and beyond the Born-Oppenheimer framework from first principles. Two approaches have been developed for very different but important applications. The first one is a hybrid method based on classical force fields that predicts the high-energy ensemble of three-dimensional structures of a class of proteins critical in human physiology: the G protein-coupled receptors (GPCRs). GPCRs' functions rely on their activation marked by a series of conformational changes related to binding of certain ligands, but the short of experimental structures has hampered the study of their activation mechanism and drug discovery. Our method, combining homology modeling, hierarchical sampling, and nanosecond-scale molecular dynamics, is one of the very few computational methods that can predict their active-state conformations and is one of the most computationally inexpensive. It enables the conformational landscape and the first quantitative energy landscape of GPCR activation to be efficiently mapped out. This method, named ActiveGEnSeMBLE, allows the inactive- and active-state conformations of GPCRs without an experimental structure to be systematically predicted. We have validated the method with one of the most well-studied GPCRs, human &#946;2 adrenergic receptor (h&#946;2AR), and applied the method on a GPCR without an experimental structure, human somatostatin receptor 5 (hSSTR5). Insights on GPCR activation as well as structure prediction methods are discussed. The second one is a semiclassical approach for large-scale nonadiabatic dynamics of condensed systems in extreme conditions, termed Gaussian Hartree Approximated Quantum Mechanics (GHA-QM). Many nonadiabatic processes related to important applications (e.g. renewable energy) happen in large systems, but existing excited state dynamics methods are too computationally demanding for their long timescale simulations. GHA-QM is based on the electron force field (eFF) framework where we model electrons as Gaussian wavepackets and nuclei as classical point charges, and obtain a simplified solution to the time-dependent Schrödinger equation as the equation of motion. We employ a force field philosophy approximating the total energy as a sum of electronic kinetic energies, electrostatic energies and a Pauli correction, which corrects for the lack of explicit antisymmetry in the wavefunctions. New designs of the Pauli potential and preliminary results on hydrogen systems are discussed. With the new development, we hope to improve the accuracy and range of applications of eFF to simulate the nonadiabatic dynamics of hundreds of thousands of electrons on nanosecond timescale.</p

Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

We present a hybrid computational methodology to predict multiple energetically accessible conformations for G protein-coupled receptors (GPCRs) that might play a role in binding to ligands and different signaling partners. To our knowledge, this method, termed ActiveGEnSeMBLE, enables the first quantitative energy profile for GPCR activation that is consistent with the qualitative profile deduced from experiments. ActiveGEnSeMBLE starts with a systematic coarse grid sampling of helix tilts/rotations (∼13 trillion transmembrane-domain conformations) and selects the conformational landscape based on energy. This profile identifies multiple potential active-state energy wells, with the TM3–TM6 intracellular distance as an approximate activation coordinate. These energy wells are then sampled locally using a finer grid to find locally minimized conformation in each energy well. We validate this strategy using the inactive and active experimental structures of β_2 adrenergic receptor (hβ_2AR) and M2 muscarinic acetylcholine receptor. Structures of membrane-embedded hβ_2AR along its activation coordinate are subjected to molecular-dynamics simulations for relaxation and interaction energy analysis to generate a quantitative energy landscape for hβ_2AR activation. This landscape reveals several metastable states along this coordinate, indicating that for hβ_2AR, the agonist alone is not enough to stabilize the active state and that the G protein is necessary, consistent with experimental observations. The method’s application to somatostatin receptor SSTR5 (no experimental structure available) shows that to predict an active conformation it is better to start from an inactive structure template based on a close homolog than to start from an active template based on a distant homolog. The energy landscape for hSSTR5 activation is consistent with hβ_2AR in the role of the G protein. These results demonstrate the utility of the ActiveGEnSeMBLE method for predicting multiple conformations along the pathways for activating GPCRs and the corresponding energy landscapes, thereby providing detailed structural insights into the initial molecular events of GPCR function that are not easily accessible by experiments

Electronic Structures of Group 9 Metallocorroles with Axial Ammines

The electronic structures of metallocorroles (tpfc)M(NH_3)_2 and (tfc)M(NH_3)_2 (tpfc is the trianion of 5,10,15-(tris)pentafluorophenylcorrole, tfc is the trianion of 5,10,15-trifluorocorrole, and M = Co, Rh, Ir) have been computed using first principles quantum mechanics [B3LYP flavor of Density Functional Theory (DFT) with Poisson−Boltzmann continuum solvation]. The geometry was optimized for both the neutral systems (formal M^(III) oxidation state) and the one-electron oxidized systems (formally M^(IV)). As expected, the M^(III) systems have a closed shell d^6 configuration; for all three metals, the one-electron oxidation was calculated to occur from a ligand-based orbital (highest occupied molecular orbital (HOMO) of B_1 symmetry). The ground state of the formal M^(IV) system has M^(III)-Cπ character, indicating that the metal remains d^6, with the hole in the corrole π system. As a result the calculated M^(IV/III) reduction potentials are quite similar (0.64, 0.67, and 0.56 V vs SCE for M = Ir, Rh and Co, respectively), whereas the differences would have been large for purely metal-based oxidations. Vertically excited states with substantial metal character are well separated from the ground state in one-electron-oxidized cobalt (0.27 eV) and rhodium (0.24 eV) corroles, but become closer in energy in the iridium (0.15 eV) analogues. The exact splittings depend on the chosen functional and basis set combination and vary by ~0.1 eV

Computational predictions of corroles as a class of Hsp90 inhibitors

Corroles have been shown experimentally to cause cell cycle arrest, and there is some evidence that this might be attributed to an inhibitory effect of corroles on Heat shock protein 90 (Hsp90), which is known to play a vital role in cancer cell proliferation. In this study, we used molecular dynamics to examine the interaction of gallium corroles with Hsp90, and found that they can bind preferentially to the ATP-binding N-terminal site. We also found that structural variations of the corrole ring can influence the binding energies and affinities of the corrole to Hsp90. We predict that both the biscarboxylated corrole (4-Ga) and a proposed 3,17-bis-sulfonated corrole (7-Ga) are promising alternatives to Ga(III) 5,10,15-tris(pentafluorophenyl)-2,17-bis(sulfonic acid)-corrole (1-Ga) as anti-cancer agents

The Predicted Ensemble of Low-Energy Conformations of Human Somatostatin Receptor Subtype 5 and the Binding of Antagonists

Human somatostatin receptor subtype 5 (hSSTR5) regulates cell proliferation and hormone secretion. However, the identification of effective therapeutic small-molecule ligands is impeded because experimental structures are not available for any SSTR subtypes. Here, we predict the ensemble of low-energy 3D structures of hSSTR5 using a modified GPCR Ensemble of Structures in Membrane BiLayer Environment (GEnSeMBLE) complete sampling computational method. We find that this conformational ensemble displays most interhelical interactions conserved in class A G protein-coupled receptors (GPCRs) plus seven additional interactions (e.g., Y2.43–D3.49, T3.38–S4.53, K5.64–Y3.51) likely conserved among SSTRs. We then predicted the binding sites for a series of five known antagonists, leading to predicted binding energies consistent with experimental results reported in the literature. Molecular dynamics (MD) simulation of 50 ns in explicit water and lipid retained the predicted ligand-bound structure and formed new interaction patterns (e.g. R3.50–T6.34) consistent with the inactive μ-opioid receptor X-ray structure. We suggest more than six mutations for experimental validation of our prediction. The final predicted receptor conformations and antagonist binding sites provide valuable insights for designing new small-molecule drugs targeting SSTRs

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

: Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe-Salpeter equation (BSE) for electron-hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties

Towards an energy landscape of G protein-coupled receptor (GPCR) activation using hybrid methods

The prediction of various GPCR conformations along their activation pathways has been a challenge for computational biophysicists. The shortage of exptl. active structures has impaired the study of GPCR activation mechanisms. We have developed a hybrid computational method that predicts multiple GPCR conformations systematically, including the active ones. This is one of the very few methods that can predict the high-energy active conformations, capable of coupling to the G protein, starting from an inactive conformation. We are also able to generate, to the best of our knowledge, the first quant. energy profile of GPCR activation consistent with the qual. energy landscape from expts. Our hybrid approach starts with conformational sampling over a large landscape using a coarse grid of helix tilts and rotations in the membrane. It then selects lowest- energy conformations in the inactive- state and potential active-state energy wells defined by the TM3-TM6 intracellular end distance, which is a simple but reasonable activation coordinate. These conformations are then subjected to local sampling on a fine grid of helix tilts and rotations. This hierarchical sampling is able to identify high-energy active- state conformations seen in crystal structures, because those conformations still reside in their local energy wells. The lowest-energy conformations in each of the distinct energy wells are subjected to mol. dynamics (MD) simulation in explicit membrane for local relaxation. We have validated the method with β_2 adrenergic (hβ_2AR) and M2 muscarinic acetylcholine receptors, which have both active- and inactive- state crystal structures available. Interaction energy anal. of MD trajectories is able to reproduce key features of the qual. energy landscape of hβ_2AR activation presented in exptl. studies [Manglik et al., 2015, Cell 161, 1101]. We have also applied this methodol. to a GPCR with unknown exptl. structure, the human somatostatin receptor subtype 5. We are able to identify the agonist- GPCR and Gα-GPCR interactions crit. in its activation, and also generate a quant. energy profile consistent with exptl. observations that both the agonist and the G protein are needed to stabilize the active state. These results demonstrate our method's ability to predict the active conformations and the energy landscape of activation of GPCRs, which provides detailed structural insights into GPCR function