WorldCat Identities

Pande, Vijay

Overview
Works: 62 works in 77 publications in 1 language and 526 library holdings
Roles: Editor, Thesis advisor, Author
Publication Timeline
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Most widely held works by Vijay Pande
An introduction to Markov state models and their application to long timescale molecular simulation by Gregory R Bowman( )

10 editions published in 2014 in English and held by 363 WorldCat member libraries worldwide

"The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models [and] 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states-sets of rapidly interconverting conformations-and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation"--Publisher's description
Protein folding, misfolding and aggregation : classical themes and novel approaches by Victor Muñoz( )

4 editions published in 2008 in English and held by 81 WorldCat member libraries worldwide

"Protein folding is the process by which newly synthesized proteins fold into the specific three-dimensional structures defining their biologically active states while avoiding aggregation into pathogenic assemblies. It has always been a major focus of research in biochemistry and has often been seen as the unsolved second part of the genetic code. Protein Folding, Misfolding and Aggregation: Classical Themes and Novel Approaches includes chapters in the areas that have witnessed major developments and are written by top experts in the field. The book is unique in its scope and in its coverage of all the new developments in this area by filling a much needed gap in the current literature." "The book is essential reading for graduate and postdoctoral students actively involved in protein folding research, other scientists interested in the recent progress of the field, and instructors revamping the protein folding section of their biochemistry and biophysics courses."--BOOK JACKET
Physical chemistry principles by Vijay Pande( Book )

2 editions published in 2012 in English and held by 7 WorldCat member libraries worldwide

Markov state models for protein and RNA folding by Gregory Ross Bowman( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Understanding the molecular bases of human health could greatly augment our ability to prevent and treat diseases. For example, a deeper understanding of protein folding would serve as a reference point for understanding, preventing, and reversing protein misfolding in diseases like Alzheimer's. Unfortunately, the small size and tremendous flexibility of proteins and other biomolecules make it difficult to simultaneously monitor their thermodynamics and kinetics with sufficient chemical detail. Atomistic Molecular Dynamics (MD) simulations can provide a solution to this problem in some cases; however, they are often too short to capture biologically relevant timescales with sufficient statistical accuracy. We have developed a number of methods to address these limitations. In particular, our work on Markov State Models (MSMs) now makes it possible to map out the conformational space of biomolecules by combining many short simulations into a single statistical model. Here we describe our use of MSMs to better understand protein and RNA folding. We chose to focus on these folding problems because of their relevance to misfolding diseases and the fact that any method capable of describing such drastic conformational changes should also be applicable to less dramatic but equally important structural rearrangements like allostery. One of the key insights from our folding simulations is that protein native states are kinetic hubs. That is, the unfolded ensemble is not one rapidly mixing set of conformations. Instead, there are many non-native states that can each interconvert more rapidly with the native state than with one another. In addition to these general observations, we also demonstrate how MSMs can be used to make predictions about the structural and kinetic properties of specific systems. Finally, we explain how MSMs and other enhanced sampling algorithms can be used to drive efficient sampling
Precursor-directed biosynthesis of macrolide antibiotics by Colin James Bell Harvey( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Macrolides have long been among the most widely used antibiotics. Despite this utility, development of new macrolides through traditional synthetic and semisynthetic approaches has been greatly hindered by the inherent structural complexity of these compounds. Precursor-directed biosynthesis is a technique which circumvents this difficulty by incorporating simple synthetic precursors into a biosynthetic pathway, allowing the bulk of the molecule to be constructed enzymatically. This dissertation describes the evolution and application of a system for the facile production of new macrolides through precursor-directed biosynthesis. The results of this work are the discovery of an unexpected macrolide structure-activity relationship and the ultimate discovery of a promising new lead for macrolide development
Statistical Characterization of Protein Ensembles (PREPRINT)( Book )

2 editions published in 2006 in English and held by 2 WorldCat member libraries worldwide

When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to represent the possible conformations as a discrete set of observed conformations, an ensemble. In this work, we follow a different richer approach, and introduce a framework for estimating probability density functions in very high dimensions, and then apply it to represent ensembles of folded proteins. This proposed approach combines techniques such as kernel density estimation, maximum likelihood, cross-validation, and bootstrapping. We present the underlying theoretical and computational framework and apply it to artificial data and protein ensembles obtained from molecular dynamics simulations, and compare the results with those obtained experimentally, illustrating the potential and advantages of this representation
Single-molecule measurements of transcript elongation and termination by RNA polymerase by Matthew Herbert Larson( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Transcription by RNAP is highly regulated in both prokaryotic and eukaryotic cells, and the ability of the cell to differentiate and respond to its environment is largely due to this regulation. During elongation, for example, RNAP is known to momentarily halt in response to certain cellular signals, and this pause state has been implicated in the regulation of gene expression in both prokaryotic and eukaryotic organisms. In addition, once RNAP reaches the end of a gene, it must reliably terminate and release the newly-transcribed RNA, providing another potential point of regulation within different cell types. Both of these steps are crucial to ensure proper gene expression. In this dissertation, I focus on transcription elongation by both prokaryotic and eukaryotic RNA polymerases, as well as their regulation through pausing and termination. To probe the role of RNA hairpins in transcriptional pausing, a novel single-molecule "RNA-pulling" assay was used to block the formation of secondary structure in the nascent transcript. Force along the RNA did not significantly affect transcription elongation rates, pause frequencies, or pause lifetimes, indicating that short "ubiquitous" pauses are not a consequence of RNA hairpins. Force-based single-molecule techniques were also used to study the mechanism and energetics of transcription termination in bacteria. The data suggest two separate mechanisms for termination: one that involves hypertranslocation of RNAP along the DNA, and one that involves shearing of the RNA:DNA hybrid within the enzyme. In addition, a quantitative energetic model is presented that successfully predicts the termination efficiency of both wild-type and mutant terminators. Finally, the implementation of a novel optical-trapping assay capable of directly observing transcription by eukaryotic RNA polymerase II (RNAPII) molecules is described. This approach was used to probe the RNAPII nucleotide-addition cycle, as well as the role of the trigger loop (a conserved subdomain) in elongation. The results are consistent with a Brownian ratchet model of elongation which incorporates a secondary NTP binding site, and the trigger loop was found to modulate translocation, NTP binding, and catalysis, as well as substrate selection and mismatch recognition by RNAPII
Fluorophores for single-molecule imaging in living cells : characterizing and optimizing DCDHF photophysics by Samuel Joseph Lord( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

The number of reports per year on single-molecule imaging experiments has grown roughly exponentially since the first successful efforts to optically detect a single molecule were completed over two decades ago. Single-molecule spectroscopy has developed into a field that includes a wealth of experiments at room temperature and inside living cells. The fast growth of single-molecule biophysics has resulted from its benefits in probing heterogeneous populations, one molecule at a time, as well as from advances in microscopes and detectors. There is a need for new fluorophores that can be used for single-molecule imaging in biological media, because imaging in cells and in organisms require emitters that are bright and photostable, red-shifted to avoid pumping cellular autofluorescence, and chemically and photophysically tunable. To this end, we have designed and characterized fluorescent probes based on a class of nonlinear-optical chromophores termed DCDHFs. This Dissertation describes various physical and optical studies on these emitters, from sensing local environment to photoactivation. Chapter 1 is a general introduction to fluorescence and single-molecule spectroscopy and imaging. Single-molecule experiments in living cells are discussed and probes used for such experiments are summarized and compared. Chapter 2 explores the basic photophysics of the DCDHF fluorophores and some general methods of measuring relevant spectroscopic parameters, including photostability. Chapter 3 discusses the various approaches we have taken to modify particular properties by changing the fluorophore's structure. We have redesigned the DCDHF fluorophore into a photoactivatable fluorogen--a chromophore that is nonfluorescent until converted to a fluorescent form using light--described in Chapter 4. Finally, a different, chemical route to fluorescence activation is presented in Chapter 5. The remainder of the Dissertation is the Appendix and a full Bibliography. The Appendix includes a table of photophysical parameter for DCDHF fluorophore, various protocols used in the experiments discussed, MatLab codes, and NMR spectra
Single-molecule studies of nucleic acid folding by Peter Caton Anthony( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Nucleic acids--DNA and RNA--are critical to life, involved in the storage and decoding of genetic information in the cell as well as the regulation and catalysis of specific biological processes. The function of a nucleic acid molecule is determined in large part by the structure it adopts, which in turn depends on its sequence. The mechanisms of sequence-directed nucleic acid folding remain incompletely understood, particularly for large RNA molecules. The work presented in this thesis uses single-molecule optical-trapping techniques to study nucleic acid folding, where reversible folding is induced and measured in individual molecules through the application of force. In Chapter 2 we demonstrate direct measurement of the full folding energy landscapes of DNA hairpins, which comprise a model system for studying nucleic acid secondary structure, and show how such landscapes are sensitive to sequence. In Chapter 3 we study the electrostatics of DNA hairpin folding by measuring trends in folding energies under different ionic conditions and comparing these trends with those predicted by Poisson-Boltzmann theory. Finally, in Chapter 4 we examine folding of the TPP riboswitch aptamer, an RNA molecule with complex secondary structure that also adopts tertiary structure upon binding a small-molecule ligand. We measured the folding energy landscape of the aptamer and perturbations of this landscape resulting from mutations and ligand binding, and propose a kinetic model to describe the coupling between aptamer folding and ligand binding. Taken together, the results presented here demonstrate the usefulness of the energy landscape framework for characterizing nucleic acid folding in conjunction with single-molecule measurements
Probing RNA folding through electrostatic and coarse-grained simulations by Vincent Bangping Chu( )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

The discovery by Cech and coworkers that structured RNA molecules could catalyze specific reactions has revolutionized our understanding of RNA's role and place in the biological machinery of life. The notion of understanding RNA folding from a biophysical perspective means understanding the formation of RNA structure in terms of the basic physical forces at play. This thesis describes the the use of electrostatic and coarse grain simulations and associated experiments to investigate different features of RNA folding. Chapter 1 gives an brief introduction to RNA folding, the primary physical forces that influence its formation, and a review of recent advances in our understanding of structure formation in RNA. Chapters 2 and 3 comprise the next section of the thesis and detail advances in our understanding of electrostatic effects around nucleic acids, a topic of great importance in RNA folding. Specifically, chapter 2 presents the development of a size-modified Poisson-Boltzmann theory to help account for the effects of ionic size while chapter 3 presents a critical assessment of the Poisson-Boltzmann description of electrostatic relaxation in tethered duplex model systems. Chapter 4 highlights a general theoretical framework for understanding the combined effects of electrostatics and junction topology on RNA folding stability and specificity. The last section focuses on the use of coarse grained simulation to understand the role of junction topology in shaping the allowed conformational space of the Transactivation Response (TAR) element from the genome of the Human Immunodeficiency Virus (HIV). Though the last section is not, strictly speaking, a study of RNA folding, understanding RNA conformational motion is of critical importance to the question of structure acquisition in RNAs
Application of novel sampling methods to the simulation of protein misfolding and oligomerization by Nicholas W Kelley( Book )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

The role of solvent in protein folding in vivo by Del Michael Lucent( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

How a protein folds from an unstructured heteropolymer into a unique native structure is an important unanswered question in the field of molecular biophysics. In recent years, there have been a number of experiments and computer simulations that have provided insight into the mechanism by which folding occurs. Most of these experiments and simulations measure the dynamics of proteins in infinite dilution. However, bulk solvent is different from the cellular environment in which proteins truly fold. In vivo, protein dynamics occur in the context of the crowded cellular milieu as well as in confined spaces such as the chaperonin cavity, the proteosome, the ribosome exit tunnel, the translocon, etc. When considering these factors it is reasonable to assume that proteins may experience different microenvironments when folding in vivo than in bulk, and these differences may constitute a significant piece of the folding puzzle. Here we show via molecular dynamics simulation, that the presence of solvent when confining a mini- protein affects the probability and mechanism of folding. In a separate study, we use computer simulation to calculate the physical properties of water confined to the ribosome exit tunnel. We find that this solvent is quite different from bulk water, possessing a highly heterogeneous free energy landscape containing extensive water structure, areas of reduced solvent entropy. Finally we show that the foldase activity of GroEL cavity mutants is highly correlated with the hydrophilicy of the inner cavity
Bayesian analysis for reversible time series with applications to molecular dynamics simulation by Sergio Andres Bacallado de Lara( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

A host of sequential models in probability and statistics are characterized by time reversibility, from Markov chain Monte Carlo samplers to queueing networks. In physics, this property arises naturally from Hamiltonian mechanics. Molecular dynamics simulations are computer experiments which approximate classical mechanics in a system of interacting particles; in consequence, they are frequently reversible. Recent technical progress has made it possible to investigate the dynamics of biological macromolecules in silico using molecular dynamics simulations. An active area of research within this field is concerned with modeling the output of a simulation stochastically. This dissertation deals with the problem of incorporating knowledge of reversibility into the estimation and testing of stochastic models. We define a range of Bayesian inference algorithms, which are motivated by specific problems in the analysis of molecular dynamics simulations
Computational modeling of solvent in structural biology by Gaurav Chopra( )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

Computational structural biology is a field that involves modeling of physical interactions between complex biological macromolecules in the aqueous environment in the cell. We model the solvent (water) environment around biological macromolecules, to better understand the physical interactions needed to improve methods of protein structure prediction and, more generally, for the protein folding problem. In this thesis, we model the effect of solvent environment on protein structure refinement using implicit and explicit water models. Specifically we used the Generalized Born Surface Area (GBSA) implicit water model and the SPC and TIP4P explicit water models with the all-atom OPLS force field. We also used the knowledge-based (KB) statistical potential functions, derived from high-resolution X-ray crystals of protein structures. The KB potentials include the affect of solvent implicitly, in that the distribution of distances between atoms in protein crystals is effected by the water in the unit cell. These potentials and water models were tested for refinement of an extensive set of protein structures, using energy minimization and molecular dynamics. Energy minimization with GBSA outperformed KB potential energy minimization, in that large magnitude of refinement was observed. Energy minimization with KB potential was more consistent, in that it refined more protein structures than GBSA. We also tested our computationally inexpensive KB energy minimization in the refinement category at the eight world-wide experiments on Critical Assessment of techniques for protein Structure Prediction (CASP) that performed well. We performed a consistency test on the all the predicted protein structure models by all groups at CASP that improved streorechemistry and refined models for the best performing groups. This warrants the use of this simple and computationally inexpensive, but consistent refinement protocol to act as a natural "end" step for all participating groups at CASP. Accurate description of the water structure around the solute of interest could improve our understanding of various biological processes such as protein folding. We study the hydration of hydrophobic solutes of varying sizes (methane, benzene, cyclohexane and Buckminsterfullerene) with Molecular Dynamics (MD) simulations using a recently introduced state-of-the-art quantum general purpose quantum mechanical polarizable force field (QMPFF3) fitted solely to high-level quantum mechanical data at MP2/cc-pVTZ level with a simple model correction using CCSD(T) data for higher accuracy of aromatic carbon atom type. We ask how well the hydrophobic affect is represented in classical force fields when compared to a more rigorous quantum mechanical force field. Polarization increases ordered water structure, in that the imprint of the hydrophobic surface extends to long range effect (up to 10Å for Buckminsterfullerene). Similar surface water affects, with less ordering are also observed for classical force fields. Most of the water molecules point their dipole moment away from the hydrophobic solutes but often one OH bond points towards the hydrophobic solute surface. The major conclusion from this study is that a quantum mechanical force field increases the strength of the hydrophobic effect; this could have a profound affect on protein folding
DNA-mediated fusion of lipid vesicles by Bettina Van Lengerich( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Vesicle fusion is a central process in transport and communication in biology. In neuronal transmission, synaptic vesicles carrying neurotransmitters dock and fuse to the plasma membrane of the neuron, a process mediated by a combination of several membrane anchored and soluble proteins. Fusion results in the merger of the two apposing lipid bilayers, leading to the exchange of both the lipidic and aqueous components. The fusion reaction is thought to proceed through several stages: first, the membranes are brought into close proximity (docking), second, the outer leaflets mix, but the inner leaflets and contents remain separate (hemi-fusion), and finally, the inner leaflet and contents exchange (full fusion). Due to the complex nature of the fusion reaction and the multitude of proteins involved, the mechanism of the fusion reaction is not well understood. Simplified model systems for vesicle fusion can bring insight into the mechanism by studying the fusion reaction in a more defined and controllable system. This thesis describes a DNA-based model for the protein fusion machinery. Previously, DNA-lipids were used to tether lipid vesicles to glass-supported lipid bilayers. These vesicles could be observed by fluorescence microscopy, and are laterally mobile along the plane parallel to the supported bilayer. DNA-mediated docking between vesicles was characterized, but fusion was not observed due to the fact that the DNA partners were both coupled at the 5' end, so antiparallel hybridization holds the membranes apart. In this work, a new synthesis of DNA-lipid conjugates is described which allows coupling at both the 3' and 5' end of the DNA. Incorporation of complementary DNA-lipids coupled at opposite ends mediates fusion between lipid vesicles. Vesicle fusion was measured in bulk fluorescence assays (Chapter 2 and 3), by both lipid mixing and content mixing assays. The rate of vesicle fusion showed a strong dependence on the number of DNA per vesicle, as well as the sequence of the DNA. Consistent with previous results measured for the docking reaction, fusion was faster for a repeating DNA sequence than for a non-repeating sequence that required full overlap of the strands for hybridization. The role of membrane proximity on the rate of vesicle fusion was investigated in Chapter 3 by insertion of a short spacer sequence at the membrane-proximal end of fusion sequences. The length of the spacer sequence was varied between two and 24 bases, corresponding to length scales of approximately 1-12 nm. Fusion, as measured in bulk assays by lipid and content mixing, decreases systematically as the membranes are held progressively further apart, demonstrating a clear dependence of the rate of the fusion reaction on membrane proximity. While the bulk vesicle fusion assays showed that DNA-lipids can mediate vesicle fusion, these ensemble measurements convolve the multiple steps (docking, hemifusion, and full fusion) of the fusion reaction, complicating any kinetic analysis. In order to image individual vesicle fusion events between tethered vesicles, a new tethering strategy was developed (Chapter 4). This strategy exploits the dependence of DNA hybridization on salt by covalently attaching lipid vesicles to a glass-supported lipid bilayer, then triggering DNA-mediated docking and fusion by spiking the salt concentration. The kinetics of individual vesicle fusion events were subsequently measured using a FRET-based lipid mixing assay for many vesicles (Chapter 6). An analysis of the distribution of waiting times from docking to fusion indicated that this transition occurs in a single step. A second model membrane architecture was used to study individual fusion events between vesicles and a planar bilayer (Chapters 5 and 6). This architecture uses a DNA-tethered planar free-standing bilayer as the target membrane. The kinetics of individual vesicle fusion events to this membrane patch were also consistent with a single step process, as for vesicle to vesicle fusion. In this system, it was also possible to observe content transfer of vesicles containing a self-quenched aqueous dye (Chapter 5). By analyzing the diffusion profile of the dye, it was shown that the dye indeed is transferred into the region below the planar membrane patch, and is not released into the solution above the patch due to vesicle rupture or leakage
The physical genome : structure, elasticity, and transport in packaged DNA by Elena F Koslover( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

The packaging and expression of the genome requires a cell to overcome elastic and entropic forces to form a highly compact structure that remains dynamically accessible to transcription machinery. The eukaryotic genome is packaged into a hierarchical structure collectively termed chromatin and the prokaryotic genome is also condensed and structured by the binding of architectural proteins along the DNA. We use a combination of analytic theory and computational techniques to study how the mechanical properties of DNA and associated proteins impact genome structure and dynamics across a wide range of length and time scales. We demonstrate that the elasticity of the DNA molecule can give rise to tension-mediated cooperative binding between DNA-bending proteins, allowing them to sense each other across a tunable length scale. At the lowest level of eukaryotic chromatin packing, DNA is wound around protein cores to form nucleosomes, which then condense into regular helical fibers under physiologic conditions. Using energy landscape optimization methods, we investigate the role of DNA mechanics in determining the structure of these compact chromatin fibers. We then proceed to examine how he statistical properties of DNA at long length scales are modulated by its interactions with proteins that modify its geometry. To this end, we develop a generalized approach for coarse-grained modeling of polymer systems by mapping to continuous and discrete elastic models. Moving into the realm of dynamics, we uncover an important role for force fluctuations in biomolecular kinetics, demonstrating how microsecond fluctuations can qualitatively alter nucleosomal transcription by RNA polymerase, an essential process for eukaryotic gene expression. Finally, we use a combination of analytic reaction-diffusion models and simulations to study the target site search process of DNA-binding proteins under a variety of conditions relevant to in vitro and in vivo systems, elucidating a key role for confinement and a surprising robustness to DNA configuration. These multi-scale studies further our fundamental understanding of how the complex hierarchy of genome packing and processing arises from the basic physical properties of DNA and interacting proteins
Inferring protein structure and dynamics from simulation and experiment by Kyle A Beauchamp( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

An atomic-scale understanding of biological molecules remains a grand challenge for the physical and biological sciences. Here, I describe how molecular dynamics simulations can be used to directly connect to biophysical experiments. I first describe the use of Markov state models to connect simulated and measured protein kinetics, allowing studies of protein folding at the atomic scale. I then introduce the use of NMR measurements, such as chemical shifts and scalar couplings, for the evaluation of molecular dynamics force field quality. Finally, I propose a new statistical technique that can be used to combine both simulation and experiment into accurate models of conformational ensembles. Such models are shown to be free of force field bias and can be used to investigate the structural and equilibrium properties of biomolecules. In sum, the present work demonstrates how statistically-sound methods of inference can forge a direct connection between simulation and experiment
Enabling ab initio molecular dynamics for large biological molecules. by Ivan Vasilʹevich Ufimt︠s︡ev( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

The role of atomistic modeling of molecules and organic compounds in biology and pharmaceutical research is constantly increasing, providing insights on chemical and biological phenomena at the highest resolution. To achieve relevant results, however, computational biology has to deal with systems containing at least 1000 atoms. Such big molecules cause large computational demands and impose limitations on the level of theory used to describe molecular interactions. Classical molecular mechanics based on various empirical relationships has become a workhorse of computational biology, as a practical compromise between accuracy and computational cost. Several decades of classical force field development have seen many successes. Nevertheless, more accurate treatment of bio-molecules from first principles is highly desirable. Hartree-Fock (HF) and density functional theory (DFT) are two low-level ab initio methods that provide sufficient accuracy to interpret experimental data. They are therefore the methods of choice to study large biological systems. Recently DFT has been applied to calculate single point energy of a solvated Rubredoxin protein. The system contained 2825 atoms and required more than two hours on a supercomputer with 8196 parallel cores. This study clearly demonstrates the scale of problems one has to tackle in first principles calculations of biologically relevant systems. Dynamical simulations requiring thousands of single point energy and force evaluations therefore appear to be completely out of reach. This fact has essentially prohibited the use of first principles methods for many important biological systems. Fortunately, the computer industry is evolving quickly and novel computing architectures such as graphical processing units (GPUs) are emerging. The GPU is an indispensable part any modern desktop computer. It is special purpose hardware responsible for graphics processing. Most problems in computer graphics are embarrassingly parallel, meaning they can be split into a large number of smaller subproblems that can be solved in parallel. This fact has guided GPU development for more than a decade; and modern GPUs evolved into a massively parallel computing v architecture containing hundreds of basic computational units, which all together can perform trillions of arithmetic operations per second. The large computational performance and low price of consumer graphics cards makes it tempting to consider using them for computationally intensive general purpose computing. This fact was recognized long ago and several groups of enthusiasts attempted to use GPUs for non-graphics computing in the early 2000's. One of the few successes from these attempts is now known as Folding@Home. These early attempts were primarily stymied by three major problems: lack of adequate development frameworks, limited precision available on GPUs, and the difficulty of mapping existing algorithms onto the new architecture. The two former impediments have been recently alleviated by the introduction of efficient GPU programming toolkits such as CUDA and the latest generation of graphics cards supporting full double precision arithmetic operations in hardware. These advances led to an explosion of interest in general purpose GPU computing and led to the development of many GPU-based high performance applications in various fields such as classical molecular dynamics, magnetic resonance imaging, and computational fluid dynamics. Most of the projects, however, lie far outside of quantum chemistry which is likely caused by the complexity of quantum chemistry algorithms and the associated difficulty of mapping them onto the GPU architecture. Various specific features of the hardware require complete redesign of conventional HF and DFT algorithms in order to fully benefit from the large computational performance of GPUs. We have successfully solved this problem and implemented the new algorithms in TeraChem, a high performance general purpose quantum chemistry package designed for graphical processing units from the ground up. Using TeraChem, we performed the first ab initio molecular dynamics simulation of an entire Bovine pancreatic trypsin inhibitor (BPTI) protein for tens of picoseconds on a desktop workstation with eight GPUs operating in parallel. Coincidently, this was also the first protein ever simulated on a computer using the classical molecular mechanics approach. BPTI binds to trypsin with a binding free energy of approximately 20 kcal/mol, making BPTI one of the strongest non-covalent binders. It vi is even more remarkable that a single BPTI amino acid LYS15 contributes half of the binding free energy by forming a salt bridge with one of the trypsin's negatively charged residues inside the binding pocket. In fact, the LYS15's contribution to the overall binding energy is approximately twice as large as what would be expected based on experimental measurements of salt bridge interactions in other proteins. Our simulation of BPTI demonstrated that substantial charge transfer occurs at the proteinwater interface, where between 2.0 and 3.5 electrons are transferred from the interfacial water to the protein. This effect decreases the net protein charge from +6e as observed in gas-phase experiments to +4e or less. We demonstrate how this effect may explain the unusual binding affinity of the LYS15 amino acid
Unraveling protein dynamics by Vijay Pande( Visual )

2 editions published in 2011 in English and held by 2 WorldCat member libraries worldwide

Single-molecule fluorescence and super-resolution imaging of Huntington's disease protein aggregates by Whitney Clara Duim( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Single-molecule, super-resolution fluorescence microscopy is a powerful technique for studying biological systems because it reveals details beyond the optical diffraction limit (on the 20-100 nm scale) such as structural and conformational heterogeneity. Further, single-molecule imaging measures distributions of behaviors directly through the interrogation of many individual molecules and reports on the nanoscale environment of molecules. Sub-diffraction imaging adds increased resolution to the advantages of fluorescence imaging over the techniques of atomic force microscopy and electron microscopy for studying biological structures, which include imaging of large fields of view in aqueous environments, specific identification of protein(s) of interest by fluorescent labeling, low perturbation of the system, and the ability to image living systems in near real-time (limited by the time required for super-resolution sequential imaging). This dissertation describes the application of single-molecule and super-resolution fluorescence imaging to studying the huntingtin (Htt) protein aggregates that are a hallmark of Huntington's disease and that have been implicated in the pathogenesis of the disease. The intricate nanostructures formed by fibrillar Htt aggregates in vitro and the sub-diffraction widths of individual fibers mark the amyloids as important targets for high-resolution optical imaging. The characterization of Htt aggregate species is critical for understanding the mechanism of Huntington's disease and identifying potential therapies. Following an introduction to single-molecule, super-resolution imaging and Huntington's disease in Chapter 1, Chapter 2 describes the single-molecule methods, experimental techniques, Htt protein sample preparations, and data analysis performed in this dissertation. Chapter 3 discusses the development of super-resolution imaging of Htt protein aggregates and the validation of the images by atomic force microscopy. Chapter 4 continues the study of Htt by one- and two-color super-resolution with imaging of Htt protein aggregates over time from the initial protein monomers to the large aggregate assemblies of amyloid fibers. In Chapter 5, I detail our progress to-date in studying the earliest stages of Htt aggregation using zero-mode waveguide technology. Chapter 6 concludes the dissertation with a discussion of the results from additional projects comprising the effect of chaperonin proteins on Htt aggregation, extension of super-resolution Htt imaging to three dimensions, and cellular imaging of Htt aggregates. The future directions for these exciting projects are summarized with the expectation that research efforts directed in these areas will contribute to our understanding of Htt aggregation and Huntington's disease
 
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Protein folding, misfolding and aggregation : classical themes and novel approaches
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