Computational chemistry offers one the ability to develop a better understanding of the complex physical and chemical interactions that are fundamental to macro- and mesoscopic processes that are seen in laboratory experiments, industrial processes, and ordinary, everyday life. For many systems, the physics of interest occur at the molecular or atomistic levels, and in these cases, computational modeling and two well refined simulation techniques become invaluable: Monte Carlo (MC) and molecular dynamics (MD). In this work, two well established problems were tackled. First, models and potentials for various gas molecules were produced and refined from first principles. These models, although based on work done previously by Belof et al., are novel due to the inclusion of many-body van der Waals interactions, advanced r-12 repulsion combining rules for treating unlike intra- and intermolecular interactions, and highly-efficient treatment of induction interactions. Second, a multitude of models were developed and countless MD simulations were performed in order to describe and understand the giant frictional anisotropy of d-AlCoNi, first observed by Park et al. in 2005.

Specific recognition of ligands including metal ions by proteins is the key of many crucial biological functions and systems. Accurate prediction of the binding strength not only sheds light on the mechanism of the molecular recognition but also provides the most important prerequisite of drug discovery. Computational modeling of molecular binding has gained a great deal of attentions in the last few decades since the advancement of computer power and availability of high-resolution crystal structures. However there still exist two major challenges in the field of molecular modeling, i.e. sampling issue and accuracy of the models. In this work, I have dedicated to tackling these two problems with a noval polarizable force field which is believed to produce more accurate description of molecular interactions than classic non-polarizable force fields. We first developed the model for divalent cations Mg²⁺ and Ca²⁺, deriving the parameters from quantum mechanics. To understand the hydration thermodynamics of these ions we have performed molecular dynamics simulations using our AMOEBA force field. Both the water structures around ions and the solvation free energies were in great accordance with experiment data. We have also simulated and calculated the binding free energies of a series of benzamidine-like inhibitors to trypsin using explicit solvent approach by free energy perturbation. The calculated binding free energies are well within the accuracy of experimental measurement and the direction of change is predicted correctly in all cases. Finally, we computed the hydration free energies of a few organic molecules and automated the calculation procedure. / text

A central goal of computational biophysics and biochemistry is to understand the behavior, interactions, and reactions of molecules, and to interpret and facilitate experimental design. The objective of this thesis research is to use the molecular modeling and simulation techniques to advance our understanding of principles in molecular structure properties, recognition and interaction at the atomic level. First, a physical molecular mechanics model is built to study the conformational properties of depsipeptide, which shows potential for engineered protein mimetics with controllable structure and function. We explore the possible kinase-substrate binding modes and the likelihood of an [alpha]-helix docking interaction within a kinase active site. Finally, efficient physical models based on a polarizable potential function are developed to describe the structural properties and calculate protein-ligand binding affinities accurately for both trypsin and matrix metalloproteinase. / text

A novel approach of using near infrared region (NIR) dyes is applied to elucidate the binding interaction between human serum albumin (HSA) and mangiferin (MGF). HSA is a blood carrier protein used for drug delivery, while mangiferin is a natural polyphenol found in mangoes that possesses numerous beneficial health properties. The NIR dyes are used as a probe to investigate MGF binding interaction with HSA via monitoring fluorescence of Trp-214 residue. Molecular modeling is used for docking and semi-empirical analysis. The investigation of the binding interaction between Trp-214 and MGF is significant, for it may offer broader pharmacological insight and applications for the polyphenol. Mangiferin in proposed to bind with a 2:1 stoichiometric ratio with HSA to the Trp-214 residue in subdomain IIA and another possible binding site to be determined in future studies. Spectral changes suggest a stabilized protein conformation upon mangiferin binding with the addition of NIR dye E-06 and MHI-06.

In this thesis, we propose a new design of privacy and session control for improving a collaborative molecular modeling CVR system AMMP-VIS [1]. The design mainly addresses the issue of competing user interests and privacy protection coordination. Based on our investigation of AMMP-VIS, we propose a four-level access control structure for collaborative sessions and dynamic action priority specification for manipulations on shared molecular models. Our design allows a single user to participate in multiple simultaneous sessions. Moreover, a messaging system with text chatting and system broadcasting functionality is included. A 2D user interface [2] for easy command invocation is developed in Python. Two other key aspects of system implementation, the collaboration Central deployment and the 2D GUI for control are also discussed. Finally, we describe our system evaluation plan which is based on an improved cognitive walkthrough and heuristic evaluation as well as statistical usage data.

The current work reports on structural studies of ribonucleoprotein complexes, Escherichia coli and Thermomyces lanuginosus ribosomes, and Pariacoto virus (PaV) using molecular modeling. Molecular modeling is the integration and representation of the structural data of molecules as models. Integrating high-resolution crystal structures available for the E. coli ribosome and the cryo-EM density maps for the PRE- and POST- accommodation states of the translational cycle, I generated two all-atom models for the ribosome in two functional states of the cycle. A program for flexible fitting of the crystal structures into low-resolution maps, YUP.scx, was used to generate the models. Based on these models, we hypothesize that the kinking of the tRNA plays a major role in cognate tRNA selection during accommodation. Secondly, we proposed all-atom models for the eukaryotic ribosomal RNA. This is part of a collaboration between Joachim Frank, Andrej Sali, and our lab to generate an all-atom model for the eukaryotic ribosome based on a cryo-EM density map of T. lanuginosus available at 8.9Å resolution. Homology modeling and ab initio RNA modeling were used to generate the rRNA components. Finally, we propose a first-order model for a T=3, icosahedral, RNA virus called Pariacoto virus. We used the structure available from x-ray crystallography as the starting model and modeled all the unresolved RNA and protein residues. Only 35% of the total RNA genome and 88% of the protein were resolved in the crystal structure. The generated models for the virus helped us determine the location of the missing N-terminal protein tails. The models were used to propose a new viral assembly pathway for small RNA viruses. We propose that the basic N-terminal tails make contact with the RNA genome and neutralize the negative charges in RNA and subsequently collapse the RNA/protein complex into a mature virus. This process is reminiscent of DNA condensation by positively charged ions.

The recent determination of several crystal structures of voltage-gated ion channels has catalyzed computational efforts of studying these remarkable molecular machines that are able to conduct ions across biological membranes at extremely high rates without compromising the ion selectivity. Starting from the open crystal structures, we have studied the gating mechanism of these channels by molecular modeling techniques. Firstly, by applying a membrane potential, initial stages of the closing of the channel were captured, manifested in a secondary-structure change in the voltage-sensor. In a follow-up study, we found that the energetic cost of translocating this 310-helix conformation was significantly lower than in the original conformation. Thirdly, collaborators of ours identified new molecular constraints for different states along the gating pathway. We used those to build new protein models that were evaluated by simulations. All these results point to a gating mechanism where the S4 helix undergoes a secondary structure transformation during gating. These simulations also provide information about how the protein interacts with the surrounding membrane. In particular, we found that lipid molecules close to the protein diffuse together with it, forming a large dynamic lipid-protein cluster. This has important consequences for the understanding of protein-membrane interactions and for the theories of lateral diffusion of membrane proteins. Further, simulations of the simple ion channel antiamoebin were performed where different molecular models of the channel were evaluated by calculating ion conduction rates, which were compared to experimentally measured values. One of the models had a conductance consistent with the experimental data and was proposed to represent the biological active state of the channel. Finally, the underlying methods for simulating molecular systems were probed by implementing the CHARMM force field into the GROMACS simulation package. The implementation was verified and specific GROMACS-features were combined with CHARMM and evaluated on long timescales. The CHARMM interaction potential was found to sample relevant protein conformations indifferently of the model of solvent used. / At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Traditional bacterial disease therapies utilize compounds that ultimately kill the target bacteria but it exerts a strong selective pressure on the bacteria to develop multi-drug resistance mutants. The increasing occurrence of resistance in common pathogens has highlighted the need to identify new anti-microbials that target the control of bacterial pathogenicity in a non-extermination manner to reduce the incidence of bacteria resistance. One new strategy exploits the discrete signaling molecules that regulate the various bacterial signaling pathways, which are responsible for the expression of pathogenicity traits. Halogenated furanones (fimbrolides) from the marine red alga, Delisea pulchra have been shown to interfere with the key signaling pathway present in Gram-negative bacteria by competitively displacing the cognate signaling molecule from the transcription protein. This project focused on the design and synthesis of 1,5-dihydropyrrol-2-ones, a new class of fimbrolide derivatives capable of displaying strong antagonistic properties of the fimbrolides. Primary synthetic methodologies examined include the halolactamization of allenamides and the direct lactone-lactam transformation. No doubt, both methodologies yielded the lactam ring, the former failed to introduce the crucial C-5 bromomethylene group essential for bioactivity. A facile high yielding two-step lactone-lactam transformation method was developed and using this method, a wide range of substituted 5-bromomethyl- and 5-dibromomethylene-1,5-dihydropyrrol-2-ones were synthesized. Furthermore, a new class of tricyclic crown-ether type compounds with no literature precedent were discovered. To vary the diversity of the compounds, a related class of compounds, 5,6-dihydroindol-2-ones, were examined. A general versatile method for the synthesis of 7-substituted 5,6-dihydroindol-2-ones was developed. The synthetic strategy proceeds via the established Suzuki-Miyaura cross-coupling reaction of halogenated dihydroindol-2-ones with arylboronic acids/esters. The Suzuki methodology was found to be reliable in furnishing a wide range of 7-substituted products in high yields. A preliminary molecular modeling approach was used to assist in the design of new anti-microbials via the ligand-docking analyses of the TraR and LasR protein. A positive correlation was observed between the docking scores and biological activity and the methodology was further developed into an initial screening tool to filter potential active and non-active compounds. The newly synthesized compounds were analysed for their efficacy in reducing the expression of the Green Fluorescent Protein (GFP) in the presence of natural AHL signaling molecules in an AHL-monitor strain, indicative of the inhibition of bacterial phenotype expression. The dihydropyrrol-2-one class of compounds showed significant biological activity and this highlighted their potential for further development.

Recently, there has been increasing focus toward the development of small molecules designed to target a specific sequences of double stranded DNA for therapeutic purposes1. Minor groove binding compounds have been shown to be capable of selectivity target GC sites in AT tract DNA2. In this research, binding selectivity was investigated using absorption, fluorescence and circular dichroic properties of selected DB minor groove binders in the presence of two unique DNA sequences. Further insight was gained by comparing the electrostatic potential maps and optimized structures of the compounds of interest. Using the results presented, potential selective minor groove binders can be selected for further investigation and kinetic studies.

abstract: Peptides offer great promise as targeted affinity ligands, but the space of possible peptide sequences is vast, making experimental identification of lead candidates expensive, difficult, and uncertain. Computational modeling can narrow the search by estimating the affinity and specificity of a given peptide in relation to a predetermined protein target. The predictive performance of computational models of interactions of intermediate-length peptides with proteins can be improved by taking into account the stochastic nature of the encounter and binding dynamics. A theoretical case is made for the hypothesis that, because of the flexibility of the peptide and the structural complexity of the target protein, interactions are best characterized by an ensemble of possible bound configurations rather than a single “lock and key” fit. A model incorporating these factors is proposed and evaluated. A comprehensive dataset of 3,924 peptide-protein interface structures was extracted from the Protein Data Bank (PDB) and descriptors were computed characterizing the geometry and energetics of each interface. The characteristics of these interfaces are shown to be generally consistent with the proposed model, and heuristics for design and selection of peptide ligands are derived. The curated and energy-minimized interface structure dataset and a relational database containing the detailed results of analysis and energy modeling are made publicly available via a web repository. A novel analytical technique based on the proposed theoretical model, Virtual Scanning Probe Mapping (VSPM), is implemented in software to analyze the interaction between a target protein of known structure and a peptide of specified sequence, producing a spatial map indicating the most likely peptide binding regions on the protein target. The resulting predictions are shown to be superior to those of two other published methods, and support the validity of the stochastic binding model. / Dissertation/Thesis / Ph.D. Bioengineering 2010