Covalent Labeling-Mass Spectrometry for Characterizing Protein-Ligand Complexes

This dissertation focuses on applying covalent labeling (CL) and mass spectrometry (MS) for characterizing protein-ligand complexes. Understanding protein-ligand interactions has both fundamental and applied significance. Covalent labeling is a protein surface modification technique that selectively modifies solvent-exposed amino acid side chains of proteins. A covalent bond is formed between the functional groups of labeling reagent and protein’s side chain. One of the key factors that affects CL reactivity is a side chain’s solvent accessibility. Ligand binding protects residues on the protein surface from being labeled, and residues involved in ligand binding can be indicated via decreases in labeling extents. The main goal of this study is to develop strategies that apply CL-MS to characterize protein-ligand complexes. Diethyl pyrocarbonate (DEPC) is the labeling reagent we focused on. First, we developed a strategy that can identify ligand binding site as well as determine the ligand binding affinity to the protein. We characterized the complexes between β-2 microglobulin (β2m) and three amyloid inhibiting molecules under Cu(II)-induced amyloid forming conditions. The rest of the dissertation focused on comparing the information from two complementary MS-based methods, hydrogen deuterium exchange (HDX)-MS and CL-MS. Using three model protein-ligand systems, we demonstrate that the two labeling techniques can provide synergistic structural information about protein-ligand binding when reagents like DEPC are used for CL because of the differences in the intrinsic reaction rates of DEPC-based CL and HDX. This dissertation highlights the power of CL-MS for characterizing protein-ligand complexes. The understanding of how three amyloid inhibiting molecules bind to Cu(II)-β2M could facilitate future library screening for new drug candidates. Our work also indicates CL-MS is capable of characterizing protein-ligand complexes that are difficult to study by other methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy

RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the Avatar-GAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.Comment: Accepted by AAAI202

Auggie: Encouraging Effortful Communication through Handcrafted Digital Experiences

Digital communication is often brisk and automated. From auto-completed messages to "likes," research has shown that such lightweight interactions can affect perceptions of authenticity and closeness. On the other hand, effort in relationships can forge emotional bonds by conveying a sense of caring and is essential in building and maintaining relationships. To explore effortful communication, we designed and evaluated Auggie, an iOS app that encourages partners to create digitally handcrafted Augmented Reality (AR) experiences for each other. Auggie is centered around crafting a 3D character with photos, animated movements, drawings, and audio for someone else. We conducted a two-week-long field study with 30 participants (15 pairs), who used Auggie with their partners remotely. Our qualitative findings show that Auggie participants engaged in meaningful effort through the handcrafting process, and felt closer to their partners, although the tool may not be appropriate in all situations. We discuss design implications and future directions for systems that encourage effortful communication.Comment: To appear at the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW '22). 25 page

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online RL agents trained in imperfect simulation environments can suffer from severe sim-to-real issues. Offline RL approaches although bypass the need for simulators, often pose demanding requirements on the size and quality of the offline datasets. The recently emerged hybrid offline-and-online RL provides an attractive framework that enables joint use of limited offline data and imperfect simulator for transferable policy learning. In this paper, we develop a new algorithm, called H2O+, which offers great flexibility to bridge various choices of offline and online learning methods, while also accounting for dynamics gaps between the real and simulation environment. Through extensive simulation and real-world robotics experiments, we demonstrate superior performance and flexibility over advanced cross-domain online and offline RL algorithms

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks. To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network. Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals. Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio. Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit. Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations

Highly branched poly(β-amino ester) delivery of minicircle DNA for transfection of neurodegenerative disease related cells

Current therapies for most neurodegenerative disorders are only symptomatic in nature and do not change the course of the disease. Gene therapy plays an important role in disease modifying therapeutic strategies. Herein, we have designed and optimized a series of highly branched poly(β-amino ester)s (HPAEs) containing biodegradable disulfide units in the HPAE backbone (HPAESS) and guanidine moieties (HPAESG) at the extremities. The optimized polymers are used to deliver minicircle DNA to multipotent adipose derived stem cells (ADSCs) and astrocytes, and high transfection efficiency is achieved (77% in human ADSCs and 52% in primary astrocytes) whilst preserving over 90% cell viability. Furthermore, the top-performing candidate mediates high levels of nerve growth factor (NGF) secretion from astrocytes, causing neurite outgrowth from a model neuron cell line. This synergistic gene delivery system provides a viable method for highly efficient non-viral transfection of ADSCs and astrocytes