Drugs with desired kinetic properties have better efficacy. Non-covalent small molecule drugs can bind/unbind from their targets, which normally are macro biomolecules. Experimental methods can measure kinetics parameters at ensemble level, but they are unable to track the exact drug binding/unbinding pathways or explain whether drugs show different kinetic behaviors from different pathways. Investigation of ligand binding/unbinding pathways can deepen our understanding of ligand-protein molecular recognition. In this work, possible pathways of ligands-protein were sampled and post-analysis were conducted to investigate ligand binding kinetics (residence time), free energy landscape of ligand unbinding, and key factors that affect binding kinetics.

We are the first utilizing unbiased molecular dynamics to sample a pair of ligands, ritonavir and xk263, unbinding from HIV protease comprehensively, and classify the unbinding pathways based on the contacts between ligand-protein regions during dissociation. I identify key residues that form hydrogen bond with ligands that results in a meta-stable state for ligand-protein complex. Four distal mutation sites were observed forming interactions with ligands during unbinding, which explains why distal mutations in HIV protease affect drug binding affinity. Molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) was performed to calculate free energy landscape during ligands dissociation. HIV protease backbone conformations during ligands binding/unbinding were compared based on root-mean-square deviation (RMSD). High similarity of protein conformation during ritonavir binding/unbinding suggests that ritonavir follows conformation-selection mechanism during dissociation. In the contrast, low similarity of protein conformation during xk263 binding/unbinding suggests that xk263 follows induced-fit mechanism during dissociation.

Reconstruction of real free energy landscape of ritonavir-HIV protease dissociation was performed using Binding Kinetic Toolkit (BKiT) package. First two principal components (PC) of alpha carbon of protein along with heavy atoms in ritonavir were used as reaction coordinates to guide dissociation path on PC space. Energy barriers on free energy landscape under different unbinding pathways were explained by the molecular recognition of ritonavir-HIV protease. Furthermore, free energy landscape using ligand RMSD were computed, giving a much more accurate binding free energy and residence time approximation but in the lack of detailed ligand-protein interactions.

Molecular recognition is a fundamental part of chemical processes, especially those relevant to biology. It refers to the process by which two molecules diffuse and eventually bind with one another to form a complex. This can be broken down into to broad aspects: kinetics and thermodynamics. Kinetics refers to the motion of molecules and the rates of their reactions with each other. Thermodynamics refers to the transfers of energy that drive the reaction when molecules bind together. The work in this dissertation uses computational methods to study both aspects of molecular recognition in a range of systems, and it includes the application of existing methods and development of new tools for simulation and analysis.A strong focus is given to protein-ligand systems, in which the ligand is an inhibitory drug designed to shut down function of its target protein. The concept of developing drugs (inhibitors) that bind with and disrupt the activity of their targets is the basis for much of modern medicine, and has had incredible success. The development of more effective inhibitors is a constant challenge. The physical and chemical principles that predict an inhibitor’s effectiveness have a complex interplay, and an understanding of these principles is challenging and highly sought after. Two projects described here use molecular dynamics (MD) simulations to elucidate these principles through better understanding inhibitor binding thermodynamics. These techniques are applied to a host of inhibitors for the carcinogenic CDK8 protein and to inhibitors of a protease (PLpro) of the recent SARS-CoV2 virus. Novel inhibitors of PLpro are also developed and validated. The other work described herein studies molecular binding kinetics in both natural and engineered systems. Brownian dynamics simulation software is described which has been developed by the author and other group members. Its goal is to provide a robust tool with which researchers can study molecular recognition and association in a range of systems under varied conditions. This program, called GeomBD3, has been applied to study association kinetics and mechanisms in enzyme bioconjugates, protein-ligand systems, and nucleic acid biosensors. These studies are included in this dissertation, and we thus demonstrate that Brownian dynamics simulations can aid in rational bio/chemical engineering design efforts and supplement experimental analysis.

In the conventional misalignment mechanism, the axion field is assumed to start with zero initial velocity. However, we introduce an alternative scenario in which the axion field possesses a nonzero initial velocity, potentially due to the breaking of the Peccei–Quinn (PQ) symmetry during the early Universe. Depending on the initial velocity and the sequence of events between PQ symmetry breaking and inflation, this novel scenario can amplify or diminish the expected axion relic abundance compared to the conventional prediction. Consequently, this opens up new parameter regions for axion dark matter models.

Global cosmic strings, anticipated in various non-standard models, generate primordial gravitational waves detectable by instruments. We refine the analytical Velocity-dependent One-Scale (VOS) model through recent simulation outcomes, revealing the gravitational wave spectrum produced by global string networks, including Goldstone emission. Our findings present a technique to detect signals from the early universe before Big Bang nucleosynthesis, impacted by the non-standard pre-BBN equation of state and new relativistic particles.

Early dark energy, relieving the Hubble tension, imprints discernible characteristics on the primordial stochastic gravitational wave background originating from cosmic string networks. This signal stands out in planned gravitational wave experiments, distinctly separate from other cosmological and astrophysical signals in the gravitational wave frequency spectrum.

In the context of axion-like particle (ALP) dark matter theories, we explore enhanced early galaxy formation through the kinetic misalignment mechanism. This has potential relevance to the excess observed by the James Webb Space Telescope (JWST) while adhering to constraints. Viable parameter space is identified for ALP mass within the range of $10^{-22}{\rm eV}

Through advanced simulations and analytical modeling, we conduct an updated analysis of long-lived axion domain wall (DW) networks. By scrutinizing energy loss mechanisms and calculating axion emissions from the DW network, we determine their contribution to axion dark matter density. While our results are consistent with prior research, disparities arise, particularly in predicting DM abundance. These disparities could profoundly impact axion phenomenology on a larger scale.

Computer-aided drug design (CADD) represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. It has been applied at almost all stages in the drug discovery pipeline. CADD has the advantage of reducing the cost and time for the expensive and extensive laboratory-based experiment. In CADD, protein flexibility has long been recognized as a complicating factor. Proteins are in constant motions in their physiological environment and their conformational dynamics play important roles in various biological functions and regulating ligand binding. My research focuses on modeling of protein flexibility and inter-molecular interactions and their applications to CADD. Detailed projects include method development for analyzing protein conformational changes using Perl and applications of various computing tools to study protein dynamics and protein-ligand interactions. In addition, ligand-specific molecular model was constructed on cannabinoid CB1 receptor, a G-protein coupled receptor, with protein flexibility considered. To discover novel antimicrobial agents against tryptophan synthase, another project was carried out using structure-based virtual screening method. Recently, nanoparticles have been applied to therapeutic use. To promote safer implementation of nanotechnology and reduce nanotoxicity for drug delivery, dynamics and interactions between human serum albumin and nanoparticles were studied.

The Casimir effect has become very important in modern physics since its prediction in 1948. Not only is it the most accessible evidence of vacuum fluctuations in macroscopic systems but also has important applications in many areas such as condensed matter physics, atomic physics, cosmology and nano-technology. Therefore, the theoretical activity in the analysis of the Casimir effect has dramatically increased in the past 60 years. To provide deeper insights, precise Casimir force measurements are necessary.

Here, we develop a high precision dynamic Casimir force measurement system based on short coherence length fiber-optics interferometer in UHV. The frequency modulation (FM) technique and precise calibration are the key points to achieve a high precision in the Casimir force measurement. It is also the first time in our group, random errors are reduced to be smaller than systematic errors.

The Casimir force was measured between a Au-coated sphere and Au-coated plate using FM technique. A comparison to the theory where the description of the permittivity using the generalized Plasma and Drude model is done. In addition , Casimir Pressure measurement between a Au-coated sphere and Ito plate will be presented.

Molecular recognition is fundamentally important in biological chemistry. Nowadays, with the rapid development of computational technology and algorithm, molecular modeling has become a powerful tool in studying molecular recognition, such as exploring molecular interactions and understanding biological dynamic processes, making significant contributions to modern biology and drug discovery. The state-of-the-art techniques of computational chemistry and molecular modeling can be applied to study a wide range of chemical and biological systems of interest. This enables us to study structural details at the atomic level and obtain chemical/biological information which is not available by experimental measurements. This dissertation project focused on modeling the recognition mechanisms of biomolecules and their conjugated ligands. Multiple computational techniques, such as molecular dynamics simulation, enhanced sampling methods and free energy calculation were applied. The model systems included signaling domains (BRCT domain), kinase (p38 kinase), enzyme system (TRPS) and small biomolecular system (cyclodextrin). The details of protein-ligand interactions, including both enthalpic and entropic contribution within protein domain-phosphopeptide systems were investigated, based on which new inhibitors were proposed. Several enhanced sampling methods like accelerated molecular dynamics simulation, pathway search guided by internal motions (PSIM) and umbrella sampling, were applied to explore the dissociation pathway of kinase-ligand systems and the motions of kinase during dissociation process were studied both thermodynamically and kinetically, protein conformational rearrangement was found to differentiate slow and fast unbinding inhibitors, casting light on high efficacy inhibitor design. Furthermore, using full structural molecular modeling, we explored how the position of a single proton can change the overall protein dynamics and further activate or inactivate enzyme catalysis, elucidating the catalytic mechanism of TRPS. In addition, we performed systematically evaluation to the performance of umbrella sampling, investigated the influence of subtle changes in the dissociation pathways and conformational sampling methods that provide the initial conformations, paving way for future improvement of umbrella sampling. This project studies the details of receptor-ligand interaction and provides a more complete picture of molecular recognition.