Cytochrome P450 1A-ligand interactions: Implications for substrate specificity and inhibitor susceptibility

Cytochromes P450 are heme-containing enzymes that are involved in the metabolism of a variety of clinically important drugs, endogenous and exogenous compounds, including a number of procarcinogens. P450 1A subfamily has two members: 1A1 and 1A2. P450 1A1 and 1A2 show high sequence identity (\u3e70%), but display different substrate specificity and inhibitor susceptibility. P450 1A2 is one of the major hepatic P450s, which metabolizes more than 11% of drugs currently on the market. Thus, we focused our attention on studies of this particular P450.;The five key active site residues that are different between P450 1A1 and 1A2 have been proposed to play an important role in determining the substrate binding orientation. We adopted phenacetin, an important substrate marker for P450 1A2, to investigate this role. Kinetic studies have shown that the L382V mutant and other mutants containing the L382V substitution exhibited markedly higher catalytic efficiency than the wild type enzyme, while other four single mutants displayed much lower activity. Stoichiometry studies indicated that the higher coupling occurred due to decreased water formation in the catalytic cycle by L382V and mutants containing the L382V substitution. Docking and molecular dynamic simulations suggested that the L382V substitution enabled the oxidation site of phenacetin to move closer to the ferryl oxygen of heme, thereby promoting phenacetin metabolism.;In order to verify the above mechanism, NMR T1 relaxation measurements were utilized to estimate the distance between protons of phenacetin and ferryl oxygen of oxo-heme of P450 wild type or mutants. The results showed that the time-averaged orientations of phenacetin in the active site were very similar in P450 1A2 wild type and mutants. However, the protons at the site of oxidation of phenacetin were closer to the ferryl oxygen in P450 1A2 L382V and L382V/N312L mutants than P450 1A2 WT, which is consistent with the findings from molecular modeling.;To extend our studies, we explored the interactions between inhibitors and P450 1A2 WT and mutants. Molecular modeling techniques, including docking and molecular dynamic simulations, have been extensively used to predict possible inhibitor-enzyme interactions and describe the docking energy involved. In some cases, for example with residue Phe226, pi-pi stacking might play a major role in these interactions. Good correlations between docking scores and inhibition constants Ki were obtained using AutoDock program.;The combination of molecular modeling and experimental techniques helped us to thoroughly investigate the structure-function relationships of P450 1A2. The insight we gained into the catalytic and inhibition mechanism(s) of this enzyme stresses the importance of the active site topology for P450 activity and provides important implications for the rational design of anticancer drugs

Explicit Analytic Solution of Vibration Equation for large domain by mean of the Elzaki projected Differential Transform Method

The aim of this paper is to present a reliable and efficient algorithm Elzaki projected differential transform method (EPDTM) to obtain the explicit solution of vibration equation for a very large membrane with given initial conditions. By using initial conditions, explicit series solutions for six different cases have been derived for the fast convergence of the solution. Numerical results show the reliability, efficiency and accuracy of Elzaki projected differential transform method (EPDTM). Numerical results for the six different cases are presented graphically

N-ANNULATED RYLENE BASED CHROMOPHORES AND THEIR APPLICATION

Ph.DDOCTOR OF PHILOSOPH

Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i.e., the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.Comment: Accepted by ICRA 202

Graph Neural Networks for Decentralized Multi-Robot Path Planning

Effective communication is key to successful, de- centralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among robots. To side-step these issues and move beyond hand-crafted heuristics, we propose a combined model that automatically synthesizes local communication and decision-making policies for robots navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations by navigating teams of robots to their destinations in 2D cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model’s capability to generalize to previously unseen cases (involving larger environments and larger robot teams).We gratefully acknowledge the support of ARL grant DCIST CRA W911NF-17-2-0181. A. Prorok was supported by the Engineering and Physical Sciences Research Council (grant EP/S015493/1). We gratefully acknowledge their support