Browsing by Subject "Computational chemistry"

Biological signaling via DNA-mediated charge transfer between high-potential [4Fe4S]2+/3+ clusters is widely discussed in the literature. Recently, it was proposed that for DNA replication on the lagging strand, primer handover from primase to polymerase α is facilitated by DNA-mediated charge transfer between the [4Fe4S] clusters housed in the respective C-terminal domains of the proteins. Using a theoretical-computational approach, I established that redox signaling between the clusters in primase and polymerase α cannot be accomplished solely by DNA-mediated charge transport, due to the unidirectionality of charge transfer between the [4Fe4S] cluster and the nucleic acid. I extended the study by developing an open-source electron hopping pathway search code to characterize hole hopping pathways in proteins and nucleic acids. I used this module to analyze protective hole escape routes in cytochrome p450, cytochrome c oxidase, and benzylsuccinate synthase. Next, I used the module to analyze molecular dynamics snapshots of a mutant primase, where the Y345C mutation (found in gastric tumors) attenuates charge transfer between the [4Fe4S] cluster and nucleic acid, which in turn, could disrupt the signaling process between primase and polymerase α. In another protein-nucleic acid system, I found that charge transfer in the p53-DNA complex plays an important role for p53 to differentiate Gadd45 DNA and p21 DNA in metabolic pathway regulation. Using density functional theory calculations on molecular dynamics snapshots, I found that hole transfer (HT) from Gadd45 DNA to the proximal cysteine residue in the DNA-binding domain of p53 is preferred over HT from p21 DNA to cysteine. This preference ensures that the p21 DNA remains bound to the transcription factor p53 which induces the transcription of the gene under cellular oxidative stress. This dissertation concludes with a study that demonstrates similar electron conductivities between an artificial nucleic acid, 2'-deoxy-2'-fluoro-arabinonucleic acid (2’F-ANA), and DNA. Compared to DNA, 2’F-ANA offers the additional benefit of chemical stability with respect to hydrolysis and nuclease degradation, thereby promoting its use as a sensor in biological systems and cellular environments.

Despite the best efforts of both academia and the pharma industry, most non-resectable cancers remain uncurable and lethal. The world health organization (WHO) believes cancer to be the second leading cause of death worldwide, with roughly 9.6 million deaths in 2018. Meanwhile, the emergence of antimicrobial resistance (AMR), or superbugs, is an increasingly large medical crisis, with estimates as high as 700,000 deaths for 2018 worldwide. This number is increasing rapidly. These unmet medical needs, although distinct, are intimately related by the need for better chemistry and intelligent drug design.

Both AMR and cancer could benefit from the expansion of the druggable proteome through the inhibition of protein-protein interactions (PPIs). PPIs drive both intra- and inter-cellular communication, and therefore their inhibition is vital for disease modulation. Moreover, both AMR and cancer therapeutics suffer from the rapid emergence of drug resistance. Even great drugs that function perfectly at first frequently lose effectiveness a few months later, due to the rapid emergence of drug resistance.

Here, I discuss my contributions towards developing a PPI inhibitor to KRas, the most commonly activated oncogene in cancer. Through the use of OSPREY, a state-of-the-art computational protein and drug design (CPDD) software, and using KRas’ native ligand Raf-1 RBD as a starting point, we developed a super-binder with single-digit nanomolar affinity for KRas. The development and validation of this biologic inhibitor required the development of four novel biochemical assays to study binding to KRas and the inhibition of the KRas:Raf interaction.

I also discuss my contributions towards enhancing our ability to predict resistance mutations through the use of OSPREY. This work focused on novel mechanisms of resistance in the dihydrofolate reductase of Staphylococcus aureus (SaDHFR). Specifically, we investigated the role of plasmid-borne resistance genes in Staph, as well as the mechanism of resistance due to the emergence of the F98Y and V31L resistance mutations. We discovered a potential new mechanism of resistance based on the formation of a tricyclic NADPH configuration, which we have named chiral evasion.

Finally, I discuss lessons learned from benchmarking OSPREY and share observations that can be used by drug designers using CPDD tools to enhance the accuracy and predictive potential of their results.

In conclusion, a combination of OSPREY and biochemical assays was used towards overcoming two of the largest limitations in drug development that directly affect global human health: the development of PPI inhibitors and overcoming drug resistance. We identified a novel hot-spot in the KRas:Raf interface that can successfully be used to optimize the PPI and develop a biologic inhibitor to KRas. We generated models that explain the mechanism of inhibition of both V31L and F98Y in the context of chiral evasion through a tricyclic NADPH configuration, and we benchmarked OSPREY and observed features that can contribute towards the predictive accuracy of CPDD tools.

Computational protein design is a leading-edge technology to design novel protein with novel functions, as well as study the structure and function of known protein. Conventionally, most of the existing computational protein design methods and softwares focus only on modeling proteinogenic amino acids. However, in reality most biochemical systems are far more complicated. Many kinds of protein not only consist of proteinogenic amino acids, but also contain non-natural amino acids or post-transnational modifications. For some protein, their function can only be fulfilled through the interaction with small molecule ligands or cofactors, which is also beyond the scope of proteinogenic amino acids. In order to expand the capability of computational protein design methods, in this dissertation we incorporated the the modeling of non-natural amino acids into OSPREY. OSPREY is a computational protein design software suite that based on provable algorithms and developed in our lab. Furthermore, 3 human health related designs involving non-natural amino acids or small molecule ligands are presented in this dissertation: (1) design of novel cystic fibrosis therapeutics using non-natural amino acids, (2) re-design of HIV-1 broadly neutralizing antibodies for better potency and breadth, and (3) development of novel antibiotics fighting methicillin-resistant Staphylococcus aureus and the analysis of its resistance mechanism. Through extensive computational results and experiential data, we are able to demonstrate the success of our above designs.

Photoactive materials spark interest for areas such as solar energy conversion, photo-catalytic energy production, efficient light displays, or control of quantum-mechanical spin phenomena by light. This dissertation work centers on two classes of materials, chalcogenides and metal halide perovskites, chosen for their promise in light-interactive applications. Current research on these novel semiconductors is focused on overcoming challenges in detailed material design. Tuning strategies, including manipulation of chemical composition, dimensionality, and structural distortions, stand as exciting opportunities for modulating electronic and spin properties. These avenues for advancement necessitate a deep understanding of the complex physics that underlies material behavior, calling for density functional theory (DFT) simulations to capture intricate electronic structures and complex particle interactions.

This dissertation work therefore employs DFT simulations to focus specifically on electronic structure and defects in efforts to strengthen our understanding of structure-property relationships in chalcogenide and perovskite semiconductors. The ability to tune energy band gaps and spin splitting via Se-alloying in the 3D chalcogenide, CuPbSbS3 is demonstrated. Next, transferred symmetry breaking from chiral organics to inorganic-sublattices, and resultant impact to electronic and spin properties is reported in hybrid organic-inorganic metal-halides. Following this, DFT strategies are employed to study H-bonding in a 2D hybrid perovskite, (2-BrPEA)2PbI4, uncovering (i) strategies to improve H-bonding analyses and (ii) formation mechanisms of spin-related properties. Finally, the potential for electronic doping via introduction of impurities in the 2D hybrid perovskite, PEA2PbI4, is examined. DFT calculations uncover the most promising candidates for extrinsic n- and p-type dopants, alongside formation mechanisms of defect complexes and compensating defects.

Perovskites are a category of semiconductors with outstanding optoelectronic properties. Especially in the last decades, three-dimensionally connected (“3D”) hybrid perovskites gained an important position as an innovative solar-cell material by including organic cations. Related molecularly engineered materials, for example, atomic-scale two-dimensionally connected (“2D”) layered crystals and nano-scale structures offer a wide range of compositional, structural, and electronic tunability. Based on quantum chemistry simulations (specifically, density functional theory), this dissertation aims to contribute to the understanding of the relationship between the components and structure of hybrid perovskites and their electronic properties, related to alloying, energy level alignment in quantum wells, impact of chiral organic constituents on the atomic structure of 2D perovskites and resulting spin character of the electronic levels, and on the structure of related perovskite nanostructures.First, to investigate the tunability of 2D hybrid perovskites, 1) the author simulated the Sn/Pb alloying at the central metal site and explained the corresponding “bowing effect” on the bandgap values with different contribution preferences towards the conduction bands versus valence bands from different elements; 2) taking the conjugation length in different oligothiophene cations and the inorganic layer thickness as two independent factors, the author confirmed a gradual change of quantum well types. Second, to gain an in-depth understanding of the spin properties of the energy bands (specifically, the spin-selectivity) in hybrid perovskites, 1) the author analyzed the frontier bands of the 2D hybrid perovskite S-1-(1-naphthyl)ethylammonium lead bromide and revealed a giant spin-splitting originated from the inorganic moiety; 2) the author (together with experimental collaborators) identified a difference in the inter-octahedron Pb-X-Pb (X stands for the halides) distortion angles as the crucial geometric descriptor for spin-splitting in 2D hybrid perovskites by a correlation analysis of 22 experimental and relaxed structures with various chiral or achiral organic cations; 3) for perovskite nano-crystals with chiral surface ligands, simulations by the author helped to attribute the chirality transfer between organic cations and inorganic substrate to the geometric distortions driven by hydrogen bonds. Third, the author investigated 2D hybrid perovskites containing oligoacene organic cations, validated the theoretical method for geometry evaluation and predicted the expected quantum well type, crystal symmetry, and detailed expected spin-splitting properties that determine the potential for spin-selective transport and optoelectronics Finally, driven by the computational needs of large-scale hybrid perovskites DFT simulations, the application of an innovative hardware, tensor processing units (designed by Google), to quantum chemistry calculations (specifically, to solve for the density matrix) was explored. The author removed the code bottleneck to facilitate the largest “end-to-end” O(N^3) DFT simulations ever reported and benchmarked the accuracy and performance of this new hardware with test cases from biomolecular systems to solid-state and nano-scale materials.

Proteins are incredibly varied in their biological function, and are therefore attractive targets for scientists and engineers to design new and improved functions. These functions are defined by a protein structure, which can be viewed as a probability distribution over a large conformation space. Many successful protein design methods construct and evaluate models of protein structure and physics in silico to design proteins. We apply the concept of protein structure as a probability distribution to design new protein design algorithms, study mechanisms of protein binding and antibiotic resistance, and design improved broadly-neutralizing antibodies This research highlights the utility of the distribution view of protein structure, and suggests future research in this direction.

Polymer nanocomposites have attracted considerable scientific and technological interest, as such composites combine desirable material properties of both the polymer and the nanoparticles (NPs). New applications of composites often require higher-order, low-dimensional (anisotropic) organization of NPs in polymers, e.g., 1d strings, percolating networks, or 2d sheets. While self-assembly provides a powerful bottom-up approach for fabricating higher-order nanostructures, achieving unique low-dimensional assemblies of NPs in polymers is challenging since NPs tend to self-assemble into three-dimensional close-packed aggregates to minimize their total free energy. In this dissertation, I tackle this challenge of achieving anisotropic NP assembly in polymers through molecular dynamics (MD) simulations along with global optimization and machine learning techniques. First, I present a new strategy for assembling NPs into anisotropic architectures in polymer matrices, which takes advantage of the interfacial tension between two mutually immiscible polymers forming a bilayer and differences in the relative miscibility of polymer grafts with the two polymer layers to trap NPs within 2d planes parallel to the interface. Coarse-grained MD simulations are used to demonstrate this strategy, where I illustrate the assembly of NP clusters, such as trimers with tunable bending angle and anisotropic macroscopic phases, including serpentine and branched structures, ridged hexagonal monolayers, and square-ordered bilayers. The above MD simulations are however inefficient for determining the equilibrium structures of NP assemblies, especially those with many particles or complex unit cells. I adapt the efficient Basin-hopping Monte Carlo algorithm to locate the global minimum-energy configurations of NPs at interface, which allows us to explore the full breadth of NP structures possible at interface and discover many unique NP, such as binary superlattices, several of which are yet to be experimentally realized. While exploring the assembly of polymer-grafted NPs at polymer interfaces using explicit coarse-grained MD simulations, we observe that multi-body effects play an important role in the formation of quasi-1d structures. Motivated by this observation, and by similar observations in bulk polymer, I introduce a general machine learning (ML) approach to develop an analytical potential that can describe many-body interactions between polymer-grafted NPs in a polymer matrix, where the high-dimensional energy landscape of NPs is fitted by permutationally invariant polynomials as a function of their interparticle distances. The developed potential reduces the computational cost by several orders of magnitude and thus allows us to explore NP assembly at large length and time scales. Lastly, I investigate the orientational behaviors of shaped NPs (cubic NPs) at interfaces. I demonstrate the possibility of tuning the orientations of nanocubes between all three orientation phases (face up, edge up and vertex up) through polymer grafts and then take advantage of their orientational effects to assemble them into unique clusters, such as rectilinear strings, close-packed sheets, bilayer ribbons, and perforated sheets. Furthermore, by using two species of grafts, where one is hydrophilic and the other is hydrophobic, I demonstrate that the interactions between nanocubes can be further manipulated by controlling the length and stoichiometry of the two grafts, leading to more open, reconfigurable NP assemblies. Overall, this dissertation suggests that interfacial assembly of NPs could be a promising approach for fabricating next-generation functional materials with potential applications in plasmonics, electronics, optics, and catalysis.

Chemicals in consumer products can influence our risk for developing adverse health conditions. This research addresses knowledge gaps in our ability to evaluate chemical safety, particularly for emerging substances on the market. Acknowledging the need for more high-throughput exposure and hazard models to support risk assessment, computational frameworks leveraging machine learning strategies and "big data" from public databases and mass social data sources were tested.

First, to understand consumer exposure, we require a better understanding of ingredient concentrations in products. A computational framework was developed to estimate chemical weight fractions for consumer products containing emerging substances. Nanomaterial-enabled products were used as a case study to represent such substances with limited physicochemical property data. Feature variables included chemical properties, functional use categories (e.g., antimicrobial), the type of product and its matrix. Weight fractions were classified as low, medium or high using a random forest or nonlinear support vector classifier. Performance of machine learning models was qualitatively compared with that of models from a second framework trained on data-rich, bulk-scale organic chemical product data. Models could roughly stratify material-product observations into weight fraction bins with moderate success. The best model achieved an average balanced accuracy of 73% on nanomaterials product data. Chemical functional use features served as particularly insightful predictors, suggesting that functional use data may be useful in evaluating the safety and sustainability of emerging chemicals. Investment in chemical and product data collection could see continued improvement of such machine learning models.

Shifting focus to the impact of chemicals on consumers, data on personal care products, ingredients, and customer reviews from online retailers and databases was collected to see if certain chemicals might increase risk of adverse reactions to products. The study scope was narrowed to shampoo products for hypothesis testing. Processing steps in the data pipeline included informatics and machine learning methods, namely, natural language processing for interpreting product reviews, text extraction from images of product labels, and feature reduction using chemical structure and ingredient source data. Fifty-one ingredient clusters were identified as having a significant correlation with higher adverse reaction rates in consumers when present in shampoos. Among these, there were a few common plant-based ingredients and synthetic preservatives known for causing skin sensitivity or irritation. In comparison with other constituents, however, the positively correlated ingredient groups had a general lack of published structural, physicochemical property and toxicity data. Results suggest an urgent need for targeted, higher-throughput chemical evaluations to safeguard consumers.

Together, these proof-of-concept studies progress our ability to quantify exposure and hazard of emerging and data-poor substances in consumer products. The outcomes of the computational frameworks can help prioritize potentially problematic substances for additional study to characterize risk.

Molecular dynamics (MD) simulation is an extremely powerful, highly effective, and widely used approach to understand the nature of chemical processes in atomic details for small molecules, biomolecules, and materials. The accuracy of MD simulation results is highly dependent on force fields. Quantum mechanical (QM) calculation has excellent accuracy, but the computational cost is not affordable for long MD simulations. Therefore, traditional molecular mechanical (MM) force fields, which divide energy into classical bond, angle, dihedral, electrostatic and van der Waals (vdW) terms, or hybrid QM/MM methods, which consider tradeoff between QM accuracy and MM efficiency, are generally utilized in MD simulations. Machine learning (ML) provides capability for generating an accurate potential at QM level without increasing much computational effort, and ML-based potentials had rapid development and widespread applications during the past decade. In this dissertation, we apply MD and ML techniques to develop new methods for simulating on small molecules and proteins. First, we train ML models to increase the accuracy of QM/MM from the semiempirical to the ab initio level. Active learning is performed to efficiently update ML models on the fly with gradient boosting technique, and new data from MD simulations are sampled according to the boundary of reference energies, distance-based clustering, and density-based clustering. Solvation free energies of small molecules obtained from QM/MM ML models show good agreement with experiment. Next, force fields based on neural network (NN) are constructed for QM/MM vdW interaction, which is normally described with Lennard-Jones (LJ) potential. We develop a new QM/MM NN architecture, dubbed QM-NN/MM-NN, and new input features based on center of mass for NN, which better describes non-bonded interactions than other descriptors. NN force fields greatly outperform LJ potentials and show good transferability to different small molecules. In addition, general and transferable NN force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins, according to residue-based systematic molecular fragmentation method. NN is based on atom types and new input features that are similar to MM inputs are proposed, which enhances the compatibility of CHARMM-NN with MM MD. The validations on geometric data, relative potential energies and reorganization energies demonstrate that the potential energy minima of CHARMM-NN are very similar to QM, but the simulations of peptides and proteins indicate that the solvent effects and non-bonded interactions should be modeled in future development of NN force fields. Finally, we develop a piecewise approach to run all-atom steered MD (SMD) simulations within small water box, avoiding the huge amounts of computational resources required to run all-atom SMD simulations using a large water box. The robustness of this approach is validated with a small protein NI3C. Compared to coarse-grained SMD, the all-atom SMD simulations on luciferase reveal more atomic resolution details on force-extension plots and the key secondary structures related to mechanical stability in unfolding pathway.

Proteins are biological macromolecules made up of amino acids. Proteins range from enzymes to antibodies and perform their functions through a variety of mechanisms, including through protein-protein interactions (PPIs). Computational structure-based protein design (CSPD) seeks to design proteins toward some specific or novel function by changing the amino acid composition of a protein and modeling the effects. CSPD is a particularly challenging problem since the size of the search space grows exponentially with the number of amino acid positions included in each design. This challenge is most often encountered when considering large designs such as the re-design of a PPI. Herein, we discuss how to use CSPD to predict resistance mutations in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus and we investigate the accuracy of an existing CSPD suite of algorithms, osprey. We have also developed novel algorithms and tools within osprey to more efficiently and accurately predict the effects of mutations. We apply these various algorithms and tools to three systems toward a variety of goals: predicting the affect on stability of mutations in staphylococcal protein A (SpA), re-designing HIV-1 broadly neutralizing antibody PG9-RSH toward improved potency, and designing toward a peptide inhibitor of KRas:effector PPIs.

Quantum dots (QDs) are promising building block materials for many emerging energy-harvesting applications. We theoretically investigated the influences of the QD-QD (CdTe-CdSe) charge transfer rates and mechanisms on QD solar cells power conversion efficiencies using multi-level modeling methods including the first principle quantum chemistry calculations of QD electronic and charge transfer properties and the kinetic modeling of solar cell performances.

We developed tight-binding electronic structure models to explore the QD electronic properties, and the charge transfer kinetics including their dependences on QD sizes and QD surface-to-surface distance. We found that the QD-QD charge transfer rates follow the non-adiabatic rate expression by Marcus. The QD-QD electronic coupling strength decays exponentially as the QD surface-to-surface distance increases. The QD-QD charge transfer rates generally increase (decay) as the acceptor (donor) QD radius increases. We found that the TS coupling mechanism can dominate the QD-QD coupling over the TB coupling. The difference between the TS and TB coupling size dependences results in a dominance switch between the TS and TB charge transfer mechanisms in the QD dyad as the QD sizes grow.

We further explored the use of an external charge to modulate the QD-QD coupling strength and the coupling mechanism. We found that a positively charged group in the bridge strengthens the D-A coupling for all QD sizes. A negatively charged group in the bridge causes the D-A coupling reduction in large QDs. For small QDs, the D-A coupling variation induced by the negative charge depends on the QD sizes. Compared to the neutral bridge, we found that through-solvent and through-bridge mechanisms switch their dominance at smaller (larger) QD sizes for the positively (negatively) charged group in the molecular bridge.

Using the computed charge transfer rates, we explored the power conversion efficiencies of QD solar cells based on QD dyads and QD triads. We found that the external and internal power conversion quantum efficiencies are significantly enhanced by introducing a third QD between the donor and acceptor QDs. The improvements in the efficiencies can be further enhanced by tuning the band-edge energy offset of the middle-position QD from its neighbors.

Electron transfer is an essential process for life to exist and is the working principle of electronics. Fundamental knowledge of electron transfer is crucial for understanding biological processes and developing future devices. Here, we present our theoretical and experimental efforts to design, synthesize and characterize the electronic properties of charge-transfer complexes and approaches to control their charge flow.

Computational methods, including wave function-based methods, density functional theory, quantum mechanics/molecular mechanics, and molecular dynamics, were combined with synthesis, ultrafast spectroscopy and conductance measurements to design and characterize engineered chemical systems for efficient charge and energy transfer.

Compelling foundational questions explored in this dissertation include: 1) Can we control the photoinduced electron transfer rate of molecules with chemically innocent vibrational excitations? 2) Can we create, sustain, and exploit chemical coherences to harvest and transmit energy and information? 3) What chemical modifications lead to enhancing intrinsic charge transport properties of molecular devices?

First, we discuss the photophysics of highly conjugated organic and organometallic systems consisting of electron donor and acceptor units. Electronic structure calculations combined with ultrafast spectroscopy elucidated the excited-state dynamics of molecular candidates for controlling charge transfer with infrared pulses and directing energy transfer through nonthermal routes. Second, we introduce strategies to fabricate efficient DNA-based molecular wires and obtain abundant semiconducting carbon nanotubes. Molecular dynamics simulations and kinetic modeling revealed the chemical interactions relevant for engineering charge transport in nanostructures. The synergy between our theoretical calculations and experimental measurements provides guidelines to tailor the electronic properties of chemical systems and control charge flow in optically active charge-transfer complexes and nanostructures.

In this dissertation we set out to describe the excited state properties of organic molecules as well as the inherent reactivity of organic molecules from a computational perspective. In order to compute excitation energies, we use the particle-particle random phase approximation (pp-RPA) to the pairing matrix fluctuation. We apply the pp-RPA to a set of organic molecules that exhibit thermally activated delayed fluorescence. The charge-transfer excited states are accurately reproduced with the pp-RPA. This class of molecules represent the largest molecules studied with the pp-RPA.

We also present method development to mitigate a shortcoming of the pp-RPA. Previously, the pp-RPA approach to computing excitation energies was limited to describing excitations that originate from the highest occupied molecular orbital (HOMO). We adopt a non-optimized pp-RPA reference, which allowed us to compute the valence excitation energies that originate from any orbital below the HOMO. This approach was applied to a set of benchmark organic molecules.

With respect to molecular transformations, we provide computational insight to the regioselective hydroamination of unsaturated organic molecules with the aid of density functional theory (DFT). Using concepts from conceptual DFT we uncover that the observed regioselectivity is driven by the inherent reactivity of the organic molecule of interest. Our analysis provides insight into the controllable addition of N―H bond across an unsaturated olefin.

Three-dimensional (3D) Hybrid Organic-Inorganic Perovskites (HOIPs) have been investigated intensively for application in photovoltaics in the last decade due to their extraordinary properties, including ease of fabrication, suitable band gap, large absorption, high charge carrier mobility, etc. However, the structure and properties of their two-dimensional (2D) counterparts, especially those with complex organic components, are not understood as deeply as the 3D HOIPs. Due to the easing of spatial constraints for the organic cations, 2D HOIPs potentially have more structural flexibility and thus higher tunability of their electronic properties compared to the 3D HOIPs. Motivated by a desire to demonstrate such flexibility and tunability, a series of 2D HOIPs with oligothiophene derivative as the organic cations and lead halide is investigated in the first part of this work. Initial computational models with variable organic and inorganic components are constructed from the experimental structure of 5,5''-bis(aminoethyl)-2,2':5',2''':5'',2'''-quaterthiophene lead bromide (AE4T\ch{PbBr4}). \textit{Ab initio} first-principles calculations are performed for these materials employing density functional theory with corrections for van der Waals interactions and spin-orbit coupling. The set of 2D HOIPs investigated is found to be understandable within a quantum-well-like model with distinctive localization and nature of the electron and hole carriers. The band alignment types of the inorganic and organic component can be varied by rational variation of the inorganic or organic component. With the computational protocol shown to work for the above series of oligothiophene-based lead halides, a more extensive family of the oligothiophene-based 2D HOIPs is then investigated to demonstrate their structural and electronic tunability. For AE2T\ch{PbI4}, the disorder of the organic cations are investigated systematically in synergy between theoretical techniques and experimental reference data provided by a collaborating group. A staggered arrangement of AE2T cations is revealed to be the most stable packing pattern with the correct band alignment types, in agreement with experiment results from optical spectroscopy. Another representative class of 2D HOIPs based on oligoacene derivatives is investigated to show structural and electronic tunability similar with their oligothiophene based counterparts. In the final part of the thesis, an all-electron implementation of Bethe-Salpeter equation (BSE) approach based on the $GW$ approximation is developed using numeric atom centered orbital basis sets, with the aim of developing first steps to a formal many-body theory treatment of neutral excitations, which goes beyond the independent-particle picture of density functional theory. Benchmarks of this implementation are performed for the low-lying excitation energies of a popular molecular benchmark set (``Thiel's" set) using results obtained using the Gaussian-orbital based MolGW code as reference values. The agreement between the BSE results computed by these two codes when using the same $GW$ quasiparticle energies validate our implementation. The impact of different underlying technical approximations to the $GW$ method is evaluated for the so-called ``two-pole" and ``Pad{\' e}" approximate evaluation techniques of the $GW$ self-energy and resulting quasiparticle energies. To reduce the computational cost in both time and memory, the convergence of the BSE results with respect to basis sets and unoccupied states is examined. An augmented numeric atom centered orbital basis set is proposed to obtain numerical converged results.

The complexity of protein-protein interactions enables proteins to self-assemble into a rich array of structures, such as virus capsids, amyloid fibers, amorphous aggregates, and protein crystals. While some of these assemblies form under biological conditions, protein crystals, which are crucial for obtaining protein structures from diffraction methods, do not typically form readily. Crystallizing proteins thus requires significant trial and error, limiting the number of structures that can be obtained and studied. Understanding how proteins interact with one another and with their environment would allow us to elucidate the physicochemical processes that lead to crystal formation and provide insight into other self-assembly phenomena. This thesis explores this problem from a soft matter theory and simulation perspective.

We first attempt to reconstruct the water structure inside a protein crystal using all-atom molecular dynamics simulations with the dual goal of benchmarking empirical water models and increasing the information extracted from X-ray diffraction data. We find that although water models recapitulate the radial distribution of water around protein atoms, they fall short of reproducing its orientational distribution. Nevertheless, high-intensity peaks in water density are sufficiently well captured to detect the protonation states of certain solvent-exposed residues.

We next study a human gamma D-crystallin mutant, the crystals of which have inverted solubility. We parameterize a patchy particle and show that the temperature-dependence of the patch that contains the solubility inverting mutation reproduces the experimental phase diagram. We also consider the hypothesis that the solubility is inverted because of increased surface hydrophobicity, and show that even though this scenario is thermodynamically plausible, microscopic evidence for it is lacking, partly because our understanding of water as a biomolecular solvent is limited.

Finally, we develop computational methods to understand the self-assembly of a two-dimensional protein crystal and show that specialized Monte Carlo moves are necessary for proper sampling.