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Atmospheric carbon dioxide (CO2) concentrations have continually increased to levels that far exceed pre-industrialization, largely due to our dependence on fossil fuels as a source of energy. This demands a renewable and clean source of energy for energy production; the sun is the largest resource at our disposal. The cost of electricity from renewable sources has seen a decline in recent years, leaving an open question of energy storage. To that end, we would like to take nature's design and use this renewable energy to produce solar fuels, similar to the natural process of photosynthesis. Ideally, we would like to reduce CO2 to valuable hydrocarbon products, especially to C2 compounds, which are more energy dense. Decades of research on the electrochemical CO2 reduction reaction (CO2RR) has showed that Cu is the only metal catalyst that can produce a variety of hydrocarbon products but at a low efficiency. Since these early findings, there have been many experimental and theoretical studies to both understand the catalysis on simple transition metal (TM) surfaces and design improved catalysts through a variety of techniques. This thesis is composed of 7 chapters. Chapter 1 is an introduction and details the current energy problem our planet is facing, a potential solution to this problem in the CO2RR, and prior research in CO2RR. Chapter 2 is the methodology and details the methods employed in this work. This includes density functional theory (DFT) to obtain energies of various adsorbed species during catalysis and analysis of this data using methods such as scaling relations and the computational hydrogen electrode. Chapter 3 involves calculations of C-C coupling barriers on various Cu facets at the electrochemical interface, including effects of electric fields, coverage effects, and strain effects. Chapter 4 uses some of these calculations as well as free energy diagrams to elucidate C-C coupling pathways on three facets of Cu: (100), (111), and (211). From our calculations, we advised our experimental collaborators to preferentially expose the (100) facet of Cu nanocubes, which showed an enhanced activity and selectivity towards C2 products. Chapter 5 includes some of these calculations as well as other barriers in the pathways to C2 compounds. These calculations are combined into a microkinetic model to predict reaction rates and compare well with experimental results. After gaining insight into C-C coupling on Cu, the best-known transition metal catalyst, chapter 6 then explores the metal-oxide etal interface for CO hydrogenation, an important step in CO2RR. Metal supported TiO nanostripes are investigated to both gain a fundamental understanding of this complex interface and attempt to break scaling to discover more active and selective catalysts towards CO2RR. Chapter 7 details conclusions drawn from each chapter of the thesis.

RNA plays critical roles in fundamental biological processes, including transcription, translation, post-transcriptional regulation of genetic expression, and catalysis as enzymes. These critical RNA functions are determined by the structures and dynamics of the RNA molecules. Computational methods might be used to predict the structures and dynamics of RNA. Unfortunately, the prediction accuracies of current computational methods are still inferior compared to experiments. In this dissertation, I discuss recent advances I made in improving and developing computational methods to make accurate predictions on the RNA structures and dynamics. The dissertation contains three individual research projects. In the first part, I present a protocol for Enumerative Real-space Refinement ASsisted by Electron density under Rosetta (ERRASER). ERRASER combined RNA structure prediction algorithm with experimental constraints from crystallography, to correct the pervasive ambiguities in RNA crystal structures. On 24 RNA crystallographic datasets, ERRASER corrects the majority of steric clashes and anomalous backbone geometries, improves the average Rfree by 0.014, resolves functionally important structural discrepancies, and refines low-resolution structures to better match higher resolution structures. In the second part, I present HelixMC, a package for simulating kilobase-length double-stranded DNA and RNA (dsDNA and dsRNA) under external forces and torques, which is typical in single-molecule tweezers experiments. It recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNA's "spring-like" conformation would give a two-fold decrease of stretch modulus relative to dsDNA. In the third part, I developed a framework of Reweighting of Energy-function Collection with Conformational Ensemble Sampling (RECCES), to predict the folding free energies of RNA duplexes. With efficient sampling and reweighting, RECCES allows comprehensive exploration of the prediction power of Rosetta energy function, and provides a powerful platform for testing future improvement of the energy function. In all the projects above, I leveraged rich datasets from previous experiments to develop novel algorithms that gave predictions with unprecedented accuracies, which were validated by independent blind tests. These computational methods I developed could also serve as a solid foundation for future efforts of improving prediction accuracies of RNA computational algorithms.

An atomic level understanding of how biological molecules and proteins function is an ongoing challenge in chemical biology. Spectroscopic methods are useful for providing information about stable states and overall transition processes but it is impossible to directly observe these processes at an atomistic level of detail. Here, computational techniques are used to supplement experimental measurements in order to provide a more complete picture of a bacterial resistance to antibiotics at the atomic scale. Markov Models are used to analyze Molecular Dynamics simulations in order to propose an activation pathway for the conformational change in a key bacterial signaling protein NtrC. Similar techniques are applied again, in combination with a novel clustering algorithm, to compute the binding affinity of vancomycin to its targets. In sum, the present work demonstrates how simulations can contribute to a better understanding of important biological systems.

Polymer mechanochemistry is an emerging area of research that investigates the use of mechanical force to promote chemical transformations. Embedding properly designed mechanochemically active motifs, termed mechanophores into polymers can induce various force triggered responses including coloration, chemiluminescence, small molecule release, and catalysis. However, most examples in this field only incorporate a tiny amount of mechanophores into a mechanochemically inert polymer. The low content of mechanophore loadings limits the magnitude of mechanical response of the materials. In this thesis, we introduce a family of mechanophore monomers based on fused bicyclohexene structure, which can be efficiently polymerized through ring-opening metathesis polymerization (ROMP) into well-controlled polymechanophores with unprecedented mechanophore loading. The modular mechanophore design can be divided into three parts: a readily polymerizable cyclobutene head, a mechanochemically active cyclobutane core and a fused cycle with different functionalities. Upon force activation, cyclobutane can readily isomerize into a pair of alkenes, while revealing the "hidden" functionalities attached and incorporating them into polymer main chain. Through the choice of different functionalized cycles, we can achieve dramatic changes in polymer properties including conjugation state, degradability, contour length and binding affinity towards certain guest molecules. In Chapter 1, we will introduce the history, recent developments, and current challenges in this field. Chapter 2 and 3 summarize our efforts to develop mechanochemically generated conjugated polymers with better scalability and structural tunability. Chapter 4 introduces a polymer system with force-enhanced degradability by mechanochemically transforming hydrolytically stable cyclic ether into acid labile enol ether. Chapter 5 describes the development of a rotaxane based mechanophore that exhibits controlled force-triggered release behavior. Chapter 6 describes a side project I worked on about the controlled synthesis of metathesis degradable polycyclohexenes through the alternating copolymerization between butadiene and methyl methacrylate

The ability to reliably design molecular systems for targeted applications has the potential to revolutionize drug and material discovery. Quantum chemistry can now accurately predict useful chemical properties for a wide range of molecules and continues to enhance its predictive power as new algorithms and hardware come online. However, several major hurdles remain. In this work, I focus on two outstanding challenges: building accessible tools for the wider chemistry community to interact with quantum chemistry, and designing photochemical systems with nonadiabatic dynamics simulations. I develop ChemPix, a hand-drawn hydrocarbon structure recognition software to provide an almost barrierless molecule input method for computational chemistry software. ChemPix and other accessible molecular input tools are combined with cloud-based GPU- accelerated quantum chemistry and extended reality visualization to build a series of interactive quantum chemistry tools. These tools can compute quantum mechanical properties in real-time from hand-drawn structures or voice input. Photochemical systems can be modeled with nonadiabatic dynamics simulations. However, the complex multi-reference electronic structure calculations required for these simulations means that modelling even simple photochemical processes demands domain expertise and substantial human effort. Here, I employ cis-stilbene, a prototypical photo- switch, to explore the path towards automated photochemical design. First, nonadiabatic dynamics simulations of cis-stilbene are performed and analyzed to uncover the cis-trans photoisomerization and photocyclization mechanisms. Next, I propose the use of ensembled simulations to build stronger mechanistic predictors and link the predictions to the experimental ultrafast electron diffraction signal. Finally, a highly symmetric light- induced molecular motor is designed based on the simulations by photo-exciting cis- stilbene with circularly polarized light

Light exists as a propagating wave of electric and magnetic fields. Exploration of the interactions between resonant light and matter has provided us with a wealth of structural and dynamical information. Largely overlooked, however, is the impact of nonresonant radiation which, when introduced into a reaction system from an intense, pulsed laser beam, may induce changes in the system through its associated strong electric field. At field strengths of 10 MV/cm, off-resonant radiation can influence the course of a reaction by interacting with the molecular system's polarizability, α, to lower an activation barrier directly. Because there is no net consumption of nonresonant photons, they behave as catalysts rather than reactants in a process referred to as "laser-field catalysis, " or "photon catalysis." In the experimental work presented in this dissertation, I investigate the impact(s) of a nonresonant IR field (1064 nm) on photoreactions in both the gas and solution phases: the photodissociation of deuterium iodide (gas), the photodissociation of phenol (gas), and the photoisomerization of cis-stilbene to trans-stilbene (solution). Two possible roles of the electric field, alignment of reagent molecules and dynamic Stark shifting of potential energy surfaces, are considered. Theoretical calculations are used to support experimental interpretation of results

Photochemistry is the chemical response of molecules to light and drives many natural processes such as photosynthesis, vision, and bioluminescence. Nonradiative decay via intersystem crossing or internal conversion are mechanisms of particular interest due to the direct conversion of energy from light into mechanical motion. This presents unique difficulties to experiment due to the rapid time-scale on which these dynamics often occur. The involvement of multiple electronic states is a commensurate challenge to theoretical methods. Nonetheless, theoretical quantum chemistry approaches have had much success towards this objective and have been used in conjunction with experiment to produce powerful results. In particular, advancements such as GPU acceleration of electronic structure energy and gradient calculations have enabled on-the-fly simulation techniques such as ab initio multiple spawning (AIMS) to simulate these processes. In this dissertation, we use AIMS to produce one-to-one comparisons with experiment through direct modelling of experimental observables. We specifically highlight dynamics involved in photoisomerization of cis-stilbene and photodissociation of ortho-nitrophenol in comparison to time-resolved photoelectron spectra and ultrafast electron diffraction respectively. Additionally, we discuss enhancements of AIMS algorithms to address existing challenges in simulating excitations to higher lying electronic states

Atomistic simulations provide detailed information about a molecular system's dynamics at finer time and length scales than experiments can access. However, the modeling and interpretation of simulation datasets in an unbiased and statistically sound way require dedicated algorithms. One analysis method is the so-called variational approach to conformational dynamics, which introduces an objective framework for the ranking of models. One such class of models are Markov state models (MSMs), which separate the configuration space explored by a simulated molecule, such as a protein, into discrete, disjoint states between which the transitions can be modeled as Markovian. In Chapter 1, I summarize the essentials of MSM construction and the variational approach. In Chapters 2 and 3, I describe systematic studies in varying MSM parameters, in pursuit of general trends for variationally optimal models of proteins, with a focus on clustering into states. In Chapters 4 and 5, I present algorithmic advances in comparing multiple related models and in coarse-graining MSMs, also using clustering. In Chapter 6, I summarize the current state of the art in MSM methods and identify frontiers in their application and methods development. Finally, in Chapters 7 and 8, I apply clustering to different problems, such as in fluid dynamics. I discuss the possibility of extending the methods motivated here to a broader class of dynamical systems in Chapter 9. Overall, the methods presented in this work focus on interpretability and statistical robustness.

Understanding the structure and dynamics of biological macromolecules is a central focus of biological research. To be able to study and gain insights into these systems, it is first necessary to have an accurate and informative model for the system of interest. However, such a model is often difficult to build. For example, during protein folding, many proteins collapse into transient kinetic intermediates on timescales too fast for high-resolution experimental techniques to detect, preventing structural characterization of these species. Alternatively, current algorithms for RNA design (i.e. predicting a sequence that folds into a desired target structure) cannot accurately model structure-sequence relationships and rely primarily on brute force stochastic search, leading to poor performance on complex targets. Here, we show that it is possible to improve the quality of models for biological systems by applying a common Bayesian approach to building them, i.e. incorporating prior information to impose informative constraints on the model parameters. Through this approach, it is possible to build high-resolution models of protein dynamics given limited experimental data, as well as a state-of-the-art computational RNA design agent that outperforms all currently existing algorithms.

Molecular dynamics (MD) simulations are becoming essential tools for many different fields, including biology, chemistry, and materials science, that provide us with a molecular picture of what is really happening at the molecular level for many biophysical phenomena. With MD simulations, we can see how the molecule forms and moves and obtain insight into its mechanisms with higher resolution than experiments. Unfortunately, MD simulations are not without limitations. They are restricted in predictive power because the molecules routinely get "stuck" in metastable states and do not change their conformations for an extended period. Hence, there is currently a huge gap between what MD simulations can model and the timescales of biological processes. Consequently, many methods have been developed for MD simulations over the past few decades to overcome this timescale barrier between MD simulations and biological processes. These are referred to as enhanced sampling methods. We need these methods to overcome the timescale barrier so that critical biophysical phenomena can be observed in a computationally tractable period. Current enhanced sampling methods have demonstrated that they can efficiently obtain thermodynamic and/or kinetic properties. However, there is still a need for an enhanced sampling method that requires little a priori knowledge about the system, is less heuristic, can obtain both thermodynamic and kinetic properties, and can be easily parallelized over the available computational resources for computational efficiency. I will go over several classes of enhanced sampling methods before diving into my new enhanced sampling methods that aim to address the issues mentioned above.

Molecular dynamics (MD) simulations are a computational technique capable of providing detailed atomic level understanding of molecular processes. They do this by numerically solving the microscopic interactions that govern these processes. Akin to a "computational microscope", MD simulations are used to predict and understand these protein free-energy landscapes. Given simulations from related mutant proteins, MD simulations could even predict the atomic level effects of mutations. These mutations might be oncogenic thereby increasing cell proliferation or they might abrogate an inhibitors' binding affinity or they might be completely benign. A structural and quantitative model for these mutations would be invaluable in both understanding the system and potentially designing personal therapeutics. However, MD simulations are very computationally expensive to converge and it is not immediately obvious how to compare multiple mutant simulations to one another in a statistically significant and efficient manner. In this thesis, I describe methods for analyzing and sampling mutant free energy landscapes. The first few chapters are dedicated towards building Markov state models (MSMs) for multiple protein kinases using milliseconds of aggregate simulation data. Currently, these are some of the largest kinase simulation datasets ever recorded. The latter chapters present several methodological advances on how to more efficiently predict mutational effects using a combination of enhanced sampling simulations and existing MSMs. This thesis attempts to merge the fields of Machine learning, enhanced sampling, and Markov state modeling for the efficient analysis and sampling of protein mutation landscapes.

Bryostatin 1 is a marine natural product that has been of great interest to chemists and clinicians due to its highly complex structure and its remarkable biological activity. Bryostatin has been investigated for the treatment of many indications, most notably cancer, HIV, and Alzheimer's disease, collectively for which it has been entered into over 40 clinical trials. Notwithstanding the immense potential impact of bryostatin's biological activity, its current supply is nearly exhausted and future supply is uncertain. All bryostatin that has been used clinically has been sourced from one GMP isolation in 1991, and all subsequent efforts to isolate more (through isolation from source organism, aquaculture, engineered biosynthesis, or total synthesis) have been unsuccessful or not scalable. One solution to bryostatin's supply problem is the design of a shorter, supply-impacting synthesis. If accomplished, this solution would allow for a rapid replenishment of bryostatin's supply and an immediate clinical impact. A second solution to bryostatin's supply problem is the design of new bryostatin analogs. Because bryostatin was not optimized for its therapeutic use in humans, new analogs can be designed that retain or even improve upon bryostatin's biological activity while also reducing its immense complexity. Such an effort, however, is complicated by the fact that there exists little structural information about bryostatin's target, protein kinase C (PKC), in its active, membrane-associated state. Thus, a substantial portion of the work described here has used both molecular dynamics (MD) simulations and solid state NMR experiments to more fully understand the structure and function of membrane-associated PKC. Chapter 1 provides a survey of the structure, function, and membrane interactions of PKC. This chapter contains a brief overview of the different PKC isoforms, their various functions within the cell, and the biological indications that are tied to PKC regulation (such as cancer, HIV, and Alzheimer's disease). It examines the bryostatin analogs that have been synthesized in order to target these indications. Of particular emphasis is that design of new PKC activators has been complicated by the fact that while a few X-ray and NMR structures of PKC fragments exist, there are no structures of membrane-associated PKC. The importance of the membrane in PKC function is described, as are the efforts thus far to examine the role of the membrane in the activity of PKC activators. Chapter 2 details the use of molecular dynamics (MD) simulations in elucidating the membrane-associated structure of ligand-bound PKC. These simulations examine how different PKC activators differentially position the ligand-bound PKC complex in the membrane, and the role of waters and lipid headgroups at the interface of the membrane and cytosol. These simulations also provide an explanation for why bryostatin's northern region is important to its activity despite not being in contact with the binding pocket, thus providing a hypothesis for future design of new bryostatin analogs. Chapter 3 details the synthesis of a new library of greatly simplified bryostatin analogs, and the development and use of a new assay to test the PKC binding affinity of these and other compounds across all conventional and novel PKC isoforms. These greatly simplified compounds remove bryostatin's complex northern region entirely by replacing the A- and B-rings with a short diester chain, thus reducing a 20-membered macrocycle to a 14-membered one, and are synthesized in only 19 linear steps (20 total). It is also shown that while some compounds in this library bind to PKC as strongly as bryostatin 1 across all isoforms, others exhibit unprecedented selectivity between conventional and some novel PKCs. Chapter 4 addresses the lack of any existing experimental membrane-associated structure of the PKC-ligand complex. This problem is addressed through the use of solid-state REDOR NMR studies, in which interatomic distances are measured between different isotopes. These experiments used an isotopically-labeled bryostatin analog bound to the PKCδ C1b domain in the presence of phospholipid vesicles. In doing so, this represents the first experimental determination of the bound conformation of any PKC activator in a phospholipid membrane. These experiments are coupled with MD simulations to use the measured interatomic distances to construct a full picture of the ensemble of conformations that exist in this PKC-ligand-membrane complex. Chapter 5 details the total synthesis of bryostatin 1. Through the collaborative work of 8 co-workers in the Wender lab, we have accomplished the shortest reported synthesis of bryostatin 1 at 19 linear steps (29 total). My key contributions to this collaborative effort are highlighted. This short synthesis is scalable and has thus far produced more than 2 grams of bryostatin 1. This chapter also describes how such a synthesis fundamentally alters the landscape of bryostatin supply; all bryostatin that had ever been used in the clinic was from one GMP isolation in 1991 and is almost entirely exhausted. Subsequent efforts to isolate bryostatin and replenish this supply have proven either unsuccessful or not scalable. Our accomplishment in producing a short, scalable synthesis breaks through this barrier and finally provides a new, renewable source of bryostatin 1.

Biology is the ultimate emergent phenomenon, and we largely lack a full picture of its function at the smallest scales. Molecular dynamics purports to model biomolecules like proteins with all-atom resolution. Among other challenges, merely analyzing the large quantities of data that result from a simulation has become a bottleneck. In this dissertation, I present my work towards building reduced-complexity models that faithfully capture the relevant functional dynamics of biomolecular simulations. In chapter 1, I introduce a mathematical language for dealing with stochastic processes and show the connection to established modeling methods like Markov modeling and tICA. Chapter 2 develops and characterizes a method for including solvent degrees of freedom in Markov state models. In chapter 3, we apply state-of-the-art MSM modeling to understand multi-scale conformational dynamics of a potassium ion channel. Chapter 4 provides an overview of a curated selection of modeling building blocks accessible through our carefully designed software package. Chapter 5 introduces a new non-linear basis which unites the MSM and tICA approaches. Finally, in chapter 6, I introduce parameterized sets of basis functions and use the variational principle directly to optimize the basis set itself. It is my hope that these novel algorithms aided by well-engineered software implementations and validated by characterization on real biomolecular systems will lead the field closer towards truly robust dynamical models of biomolecules.

Often, the computation of molecular properties requires an accurate description of the electronic wavefunction. Unfortunately, the accuracy and computational demands of a method are typically at odds with each other. In order to reduce the computational demands of ab initio methods, we have developed the tensor hypercontraction approach. In this work, we will show how the tensor hypercontraction approximation improves the efficiency of electronic structure methods while maintaining the accuracy of the underlying ab initio approach. This work focuses on the use of tensor hypercontraction in second-order Møller-Plesset perturbation theory (MP2), second-order approximate coupled cluster singles and doubles (CC2), and the extension of CC2 for excited state computations. Recently, the high performance computing industry has incorporated the use of graphics processing units (GPUs) for general purpose computing. GPUs are massively parallel architectures that are being used to accelerate computationally intensive approaches in a variety of fields, including quantum chemistry. I will show that the tensor hypercontraction methods are highly amenable to parallelization techniques and demonstrate a performance improvement for parallel tensor hypercontraction via parallelization across compute nodes and acceleration with GPUs. I will demonstrate that the use of parallel approaches allows us to extend the applicability of tensor hypercontraction CC2 and excited state computations to chemical system sizes that are challenging for canonical CC2.

Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) --- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics --- is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, statically-sound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.

This thesis develops theoretical methods and simulation approaches to elucidate the dynamics of quantum mechanical processes in heterogeneous condensed phase systems. In the first part of the thesis, we consider non-equilibrium relaxation dynamics. These dynamics play a key role in many problems in chemistry, from exciton transfer in photosynthetic light harvesting to electron transfer in photocatalysis. We use the generalized quantum master equation (GQME) formalism in conjunction with quantum-classical methods to treat nonequilibrium relaxation for systems containing many quantum states, as well as fully atomistic systems. In these systems, we show that the GQME approach is highly accurate while also being more efficient than the same quantum-classical method used directly, sometimes by as much as three orders of magnitude. We present an importance sampling algorithm which efficiently generates the memory kernel of the GQME for systems with many quantum states. We then consider a series of cases where chemically tailoring a heterogeneous environment allows one to control reactivity and dynamics. In particular, we show that simulation can be used to elucidate the role of the environment in driving vibrational relaxation in functionalized self-assembled monolayers on gold. We find that vibrational dynamics measured in experiments on self-assembled monolayers arise from a variety of molecular motions including conformational rearrangement of gauche defects in the monolayer. We also show theoretically how the regioselectivity of a catalyst can be controlled by changing the solvent to enforce ion pairing. We find that mutual polarization of the ion pair and the solvent is a key source of the stabilization of ion-paired transition states that gives rise to selectivity that is observed in experiments. Overall, the theoretical methods and simulations presented allow us to accurately capture and elucidate the dynamics in a variety of heterogeneous condensed phase systems.

This thesis discusses the role of analysis in the application of molecular dynamics (MD) simulations for studying phenomena at an atomic level of detail. Many interesting questions can be asked of the physical world: How do proteins fold? Are there two liquid phases of supercooled water? How does a drug inhibit an enzyme? Many of these questions, however, are not quantitatively specific, meaning that the answers depend on scientists' interpretation. Researchers tend to describe these phenomena through hand-chosen functions, or order parameters, that are based on physical intuition. However, there is an immense amount of information contained in a large-scale simulation that is typically not used. Here, we attempt to illustrate the usefulness of data-driven analysis in the study of physical phenomena with MD. In all cases, the strategy begins by reformulating the nebulous physical questions into a specific and quantitative question, which can be used to derive an appropriate unsupervised learning method for the given problem. This strategy rests on physical intuition, because the problem formulation must be done in a physically meaningful way, but the advantage is that the specific solutions are driven by the data itself, allowing for interesting phenomena to be discovered rather than required to be known a priori.

This dissertation develops techniques to accelerate quantum chemistry calculations using commodity graphical processing units (GPUs). As both the principle bottleneck in finite basis calculations and a highly parallel task, the evaluation of Gaussian integrals is a prime target for GPU acceleration. Methods to tailor quantum chemistry algorithms from the bottom up to take maximum advantage of massively parallel processors are described. Special attention is taken to make maximum use of performance features typical of modern GPUs, such as high single precision performance. After developing an efficient integral direct self-consistent field (SCF) procedure for GPUs that is an order of magnitude faster than typical CPU codes, the same machinery is extended to the configuration interaction singles (CIS) and time- dependent density functional theory (TDDFT) methods. Finally, this machinery is applied to molecular dynamics (MD) calculations. To extend the time scale accessible to MD calculations of large systems, an ab initio multiple time steps (MTS) approach is developed. For small systems, up to a few dozen atoms, an interactive interface enabling a virtual molecular modeling kit complete with realistic ab initio forces is developed.

The diverse physical principles that govern living things conform to one common precept: all biological processes operate, to some extent, out-of equilibrium. As our understanding of biological pathways advances at the nanoscale, theoretical and simulation techniques that function under non-equilibrium conditions will play an important role in elucidating the working environment of the cell. In this research, large-scale molecular dynamics simulations, discrete dynamical network models, and sophisticated non-equilibrium theories are synthesized to study glassy and dissipative processes facilitated by protein molecules. Leveraging atomistic molecular dynamics data derived from the Folding@home distributed computing project, a number of detailed biophysical systems are examined. I first describe glassy solvent structures that emerge as functional components of a protein chaperone, and I connect such observations to the theory of disordered systems. By coupling Folding@home data, Markov state models of biomolecular dynamics, and the theory of large deviations, I go on to characterize [beta] sheet-rich, amyloid-like misfolded states that appear on protein folding landscapes; I explore the relationship between these misfolded states and so-called dynamical glass transitions. Applying theory related to the Crooks fluctuation theorem, I next explicate the dissipative dynamics in detailed models of signaling proteins, and I illustrate how the input of external energy harmonizes with equilibrium fluctuations to yield functional signaling components. Lastly, I discuss methods by which protein landscapes can be sampled in an adaptive fashion and means for recovering equilibrium kinetics from biased, non-equilibrium simulation data.

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.