Fundamental Design of Nanocatalysts Randall J. Meyer, Chemical Engineering Department Prime Grant Support: NSF, PRF Collaborations Technical Approach Future Goals Support effects in selective partial oxidation of propylene to propylene oxide Cheaper more efficient deNOx catalysts for lean burn exhaust using core/shell Pt catalysts CO hydrogenation to produce ethanol selectively Electronic structure/reactivity relationships in transition metal alloy catalysts Problem Statement and Motivation Thin Metal Oxide Film Metal Single Crystal Supported Metal Cluster Clusters are deposited on oxide substrates using organometallic precursors Density Functional Theory Calculations complement experimental work Michael Amiridis, University of South Carolina and Mike Harold, University of Houston, Optimizing bimetallic alloys in NOx storage reduction systems Bruce Gates, University of California at Davis, Support effects in reverse hydrogen spillover Jeff Miller, Argonne National Lab, Size and support effects in adsorption behavior of Pt nanoparticles Preston Snee, UIC (Chemistry), Synthesis of novel non-oxide visible light water splitting photocatalysts Mike Trenary, UIC (Chemistry), Reactions of N atoms and hydrocarbons on Pt(111) Finite fossil fuel reserves dictate that new solutions must be found to reduce energy consumption and decrease carbon use New processes must be developed to handle renewable feedstocks Current design of catalysts is often done through trial and error or through combinatorial methods without deep fundamental understanding Our group seeks to combine experimental and theoretical methods to provide rational catalyst design

Uncovering the mechanism of reversible membrane binding Investigators: Hui Lu, Ph.D., Bioengineering Primary Grant Support: Chicago Biomedical Consortium, NIH Problem Statement and Motivation Technical Approach Key Achievements and Future Goals To efficiently function, cells need to respond properly to external physical and physical and chemical signals in their environment. Identifying disease states and designing drugs require a detailed understanding of the internal signaling networks that are activated in responses to external stimuli. In the center of these process is a particular group of protein that translocate to the cell membrane upon external activation. Combine machine learning techniques with characterization of the protein surface to identify unknown membrane binding proteins. Atomic scale molecular dynamics simulation of the interactions between proteins and membranes Mathematical modeling is used for studying the spatial and dynamic evolution of the signal transduction networks within the cell when changes in the external environment occurs. Developed highly accurate prediction protocols for identifying novel cases of membrane binding proteins, based on properties calculated from molecular surface of the protein structure. Determining membrane binding of properties of C2 domains in response to changes in ion placements and membrane lipid composition. Goal: To model the network dynamics to understand how changes in membrane binding properties of certain domains changes the efficiency of signal transduction in the cell.

Machine learning and Datamining in Biomedical Informatics Investigators: Hui Lu, Ph.D., Robert Ezra Langlois, Ph.D.,Bioengineering; Grant Support: NIH, Bioinformatics online Problem Statement and Motivation Technical Approach Key Achievements and Future Goals Massive amount of biomedical data are available from high-throughput measurement, such as genome sequence, proteomics, biological pathway, networks, and disease data. Data processing become the bottleneck of biological discovery and medical analysis Problem: Protein function prediction, protein functional sites prediction, protein interaction prediction, disease network prediction, biomarker discovery. Formulate the problem in classification problem Derive features to represent biological objects Develop various classification algorithms Develop multiple-instance boosting algorithms Developed machine learning algorithms for protein- DNA, protein-membrane, protein structure prediction, disease causing SNP prediction, mass- spec data processing, DNA methylation prediction. Developed an open-source machine learning software MALIBU Goal: Biological network analysis and prediction.

Design principle of Protein’s Mechanical Resistance Investigator: Hui Lu, Ph.D., Bioengineering, Collaborators: Julio Fernandez (Columbia University), Hongbin Li (U of British Columbia) Problem Statement and Motivation Technical Approach Key Achievements and Future Goals Mechanical signals play key role in physiological processes by controlling protein conformational changes Uncover design principles of mechanical protein stability Relationship between protein structure and mechanical response; Deterministic design of proteins Atomic level of understanding is needed from biological understanding and protein design principles All-atom computational simulation for protein conformational changes – Steered Molecular Dynamics Free energy reconstruction from non-equilibrium protein unfolding trajectories Force partition calculation for mechanical load analysis Modeling solvent-protein interactions for different molecules Coarse-grained model with Molecular dynamics and Monte Carlo simulations Identified key force-bearing patch that controlled the mechanical stability of proteins. Discovered a novel pathway switch mechanism for tuning protein mechanical properties. Calculated how different solvent affect protein’s mechanical resistance. Goal: Computationally design protein molecules with specific mechanical properties for bio-signaling and bio- materials

Exploring Gas Permeability of Lipid Membranes Using Coarse-grained Molecular Dynamics Method Huajun Yuan, Cynthia J. Jameson, Sohail Murad Department of Chemical Engineering, University of Illinois at Chicago, 810 S. Clinton, Chicago, IL Primary Grant Support: US Department of Energy Simulation System Configuration: Problem Statement and Motivation: Understand the transport mechanism of gases through biological membranes Explain the effect of gas parameters and lipid membrane tail length on permeability Use above information to develop environment- friendly separation processes Technical Approach: Develop an effective Coarse-Grained method to simulate gas transport through a model membrane efficiently and accurately Compare transport process of different gases Find gas permeability in different lipid membranes Compare with experiment to validate our results Key Achievements and Future Goals: Explained the transport process of different small molecules through a lipid membrane Determined diffusion coefficients and permeability of small molecules through a lipid membrane. Compared diffusion coefficients and permeability of different gases through different lipid membranes. Compared with atomistic simulations and experiments. Diffusion Coefficient Measurement: Different Lipid Bilayer Memberanes: Simulation Systems: Results and Discussions: Comparison with experiment measurement: Angle Bending: u=k θ (cosθ- cosθ 0 ) 2 Bond Stretching: u=k r ( r- r eq ) 2 Interaction Potential : Lines are drawn for eye guidance Density Profile of Double DMPC bilayer: Permeability = D ┴ / D //, usually value from 0 ~ 1 Permeability Definition and Measurements: Ref: Witold Subczynski et al, J.Gen.Physiol Vol.100,69-87, 1992