Hydrogen-activation mechanism of [Fe] hydrogenase revealed by multi-scale modeling

When investigating the mode of hydrogen activation by [Fe] hydrogenases, not only the chemical reactivity at the active site is of importance but also the large-scale conformational change between the so-called open and closed conformations, which leads to a special spatial arrangement of substrate and iron cofactor. To study H2 activation, a complete model of the solvated and cofactor-bound enzyme in complex with the substrate methenyl-H4MPT+ was constructed. Both the closed and open conformations were simulated with classical molecular dynamics on the 100 ns time scale. Quantum-mechanics/molecular-mechanics calculations on snapshots then revealed the features of the active site that enable the facile H2 cleavage. The hydroxyl group of the pyridinol ligand can easily be deprotonated. With the deprotonated hydroxyl group and the structural arrangement in the closed conformation, H2 coordinated to the Fe center is subject to an ionic and orbital push-pull effect and can be rapidly cleaved with a concerted hydride transfer to methenyl-H4MPT+. An intermediary hydride species is not formed

Mechanistic insights into allosteric regulation of the A2A adenosine G protein-coupled receptor by physiological cations.

Cations play key roles in regulating G-protein-coupled receptors (GPCRs), although their mechanisms are poorly understood. Here, 19F NMR is used to delineate the effects of cations on functional states of the adenosine A2A GPCR. While Na+ reinforces an inactive ensemble and a partial-agonist stabilized state, Ca2+ and Mg2+ shift the equilibrium toward active states. Positive allosteric effects of divalent cations are more pronounced with agonist and a G-protein-derived peptide. In cell membranes, divalent cations enhance both the affinity and fraction of the high affinity agonist-bound state. Molecular dynamics simulations suggest high concentrations of divalent cations bridge specific extracellular acidic residues, bringing TM5 and TM6 together at the extracellular surface and allosterically driving open the G-protein-binding cleft as shown by rigidity-transmission allostery theory. An understanding of cation allostery should enable the design of allosteric agents and enhance our understanding of GPCR regulation in the cellular milieu

Simulating protein-ligand binding with neural network potentials

Computational methods have been developed to predict the structures and energetics of protein-ligands complexes. However these methods are limited by the accuracy and transferability of the molecular mechanical (MM) models used to calculate the potential energy. Neural network potentials (NNPs) eliminate the need for parameterization and avoid many of the limiting assumptions of MM models. We evaluated the accuracy of ANI-type NNP models for predicting the potential energy surface of biaryl torsions. The ANI-2X and ANI-1ccX NNPs were found to be more accurate and reliable than popular molecular mechanical models. We then developed a new method where the NNP is used to describe the intramolecular terms of a ligand while a conventional MM model is used to describe the environment. This method was found to be effective for predicting the binding pose of ligands bound to proteins and could be used to calculate the conformational component of the binding energy. We also show that these methods can be used to re�ne low-resolution cryo-EM structures of protein-ligand complexes

UNDERSTANDING INHIBITION OF A BIODESULFURIZATION ENZYME TO IMPROVE SULFUR REMOVAL FROM PETROLEUM

The biodesulfurization 4S-pathway is a promising complementary enzymatic approach to remove sulfur from recalcitrant thiophenic derivatives in petroleum products that remain from conventional hydrodesulfurization method without diminishing the calorific value of oil. The final step of this pathway involves the carbon-sulfur bond cleavage from HBPS, and the production of the final products 2-hydroxybiphenyl (HBP) and sulfite, has been recognized as the rate-limiting step, partially as a result of product inhibition. However, the mechanisms and factors responsible for product inhibition in the last step have not been fully understood. In this work, we proposed a computational investigation using molecular dynamic simulations and free energy calculations on 2’-hydroxybiphenyl-2-sulfinate (HBPS) desulfinase (DszB) with different bound ligands as well as different solvent conditions to develop a fundamental understanding of the molecular-level mechanism responsible for product inhibition. Based on available crystal structures of DszB and biochemical characterization, we proposed a “gate” area close to substrate binding site of DszB is responsible for ligand egress and plays a role in product inhibition. We have conducted biphasic molecular dynamic simulations to evaluate the proposed gate area functionality. Non-bonded interaction energy analysis shows that hydrophobic residues around the gate area produce van der Waals interactions inhibiting translocation through the gate channel, and therefore, the molecules are easily trapped inside the binding site. Umbrella sampling molecular dynamics was performed to obtain the energy penalty associated with gate conformational change from open to close, which was 2.4 kcal/mol independent of solvent conditions as well as bound ligands. Free energy perturbation calculations were conducted for a group of six selected molecules bound to DszB. The selections were based on functional group representation and to calculate binding free energies that were directly comparable to experimental inhibition constants, KI. Our work provides a fundamental molecular-level analysis on product inhibition for the biodesulfurization 4S-pathway

Doctor of Philosophy

dissertationThe characterization of novel and reactive Phase I metabolites of xenobiotics, such as those frequently produced by P450 enzymes, is an area of interest that has led to increased research efforts during preclinical drug-testing and development. A key interest is improving our understanding of factors that contribute to competing Phase I reaction mechanisms, some of which produce stable products that can be further metabolized and excreted, and others that produce reactive metabolites capable of causing toxicities. Due to the highenergy nature of the P450 catalytic oxyferryl heme species, Compound I, P450 enzymes can also catalyze different oxidation reaction mechanisms, including dehydrogenation reactions. Dehydrogenation reactions are more difficult to predict than the more common P450 oxygenation and dealkylation reactions. Moreover, dehydrogenation mechanisms can compete with hydroxylation mechanisms to produce unstable desaturated electrophilic metabolites capable of forming potentially toxic biomolecular adducts. The work presented here focuses on improving existing computational tools for the prediction of P450 metabolism of two model substrates, raloxifene and 4-hydroxy-tamoxifen. These two compounds are FDA-approved selective estrogen receptor modulators currently used in the treatment of breast cancer. In Chapter 2 the development, iv testing and refinement of molecular mechanics parameters for key species of the heme prosthetic group during the P450 catalytic cycle is presented. It is shown that the assignment of atomic partial charges for key heme species improves the identification of the sites of metabolism of raloxifene by CYP3A4. Building on this work, in Chapter 3 it is shown that despite using these new heme parameters, extensive quantum mechanics calculations to probe substrate reactivity, molecular dynamics of the enzyme structure to find representative active site conformations makes the greatest improvement in the identification of the sites of metabolism for 4-hydroxy-tamoxifen. In summary, this work identifies that heme electrostatics and enzyme conformational dynamics play important roles in enzyme function and that the ability to predict sites of metabolism for P450- substrates requires the integration of both for the improvement of future in silico tools

Molecular dynamics simulations and drug discovery

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role