Photochemical control of bacterial gene expression based ontransencoded genetic switches

Controlling gene expression by light with fine spatiotemporal resolution not only allows understanding and manipulating fundamental biological processes but also fuels the development of novel therapeutic strategies. In complement to exploiting optogenetic tools, photochemical strategies mostly rely on the incorporation of photo-responsive small molecules into the corresponding biomacromolecular scaffolds. Therefore, generally large synthetic effort is required and the switching of gene expression in both directions within a single system remains a challenge. Here, we report a trans encoded ribo-switch, which consists of an engineered tRNA mimicking structure (TMS), under control of small photo-switchable signalling molecules. The signalling molecules consist of two amino glycoside molecules that are connected via an azobenzene unit. The light responsiveness of our system originates from the photo-switchable noncovalent interactions between the signalling molecule and the TMS switch, leading to the demonstration of photochemically controlled expression of two different genes. We believe that this modular design will provide a powerful platform for controlling the expression of other functional proteins with high spatiotemporal resolution employing light as a stimulus

Whole Brain Mapping of Long-Range Direct Input to Glutamatergic and GABAergic Neurons in Motor Cortex

Long-range neuronal circuits play an important role in motor and sensory information processing. Determining direct synaptic inputs of excited and inhibited neurons is important for understanding the circuit mechanisms involved in regulating movement. Here, we used the monosynaptic rabies tracing technique, combined with fluorescent micro-optical sectional tomography, to characterize the brain-wide input to the motor cortex (MC). The whole brain dataset showed that the main excited and inhibited neurons in the MC received inputs from similar brain regions with a quantitative difference. With 3D reconstruction we found that the distribution of input neurons, that target the primary and secondary MC, had different patterns. In the cortex, the neurons projecting to the primary MC mainly distributed in the lateral and anterior portion, while those to the secondary MC distributed in the medial and posterior portion. The input neurons in the subcortical areas also showed the topographic shift model, as in the thalamus, the neurons distributed as outer and inner shells while the neurons in the claustrum and amygdala were in the ventral and dorsal part, respectively. These results lay the anatomical foundation to understanding the organized pattern of motor circuits and the functional differences between the primary and secondary MC

Molecular Dynamics Simulation of Bioactive Complexes, Nanoparticles and Polymers

In this thesis, molecular dynamics (MD) simulations were performed to design inhibitors against viral pathogens, examine drug delivery systems and model dynamical nano-systems. Peptide based inhibitors against SARS-CoV-2 were designed by mimicking ACE2 (cellular receptor of SARS-CoV-2). MD simulations revealed that inhibitors with double α-helix bundle could provide specific geometry matching to the spike protein of SARS-CoV-2. The sequence of inhibitors can be evolved by a newly designed computational algorism to provide specificity to different strains. Protein based boosters were also designed aiming to trigger the immune reaction to SARS-CoV-2. The binding efficiencies of those boosters were examined by MD simulations. In collaboration with different experimental groups, various heparan sulfate (HS) mimics designed in their labs were examined through MD simulations to study the atomistic details of the coupling between HS mimics and viral proteins, where multivalent binding modes were found to dominate the enhanced affinity. The corresponding virucidal mechanisms were proposed based on calculations of free energy of binding. Polymers forming aggregates or micelles, synthesized in our collaborator’s lab, were also studied in atomistic details about their membrane permeability and serum stability, where the interactions between polymers and their targets were quantified. In addition to bio-related systems, nanomaterial systems were also designed and studied. Stretch healable nanofibers composed of coronene (or) perfluorocoronene molecules were designed and tested by MD simulations. Intermolecular interactions interpreted by ab initio calculations were found to be the driving factor in the self-assembly of nanofibers. The intermolecular interactions were also the key factors in determining the reaction rates of the slider-track systems and ligand effects of functionalized gold nanoparticle systems in other collaborative projects

Computational design of ACE2-based short peptide inhibitors of SARS-CoV-2

Peptide inhibitors against the SARS-CoV-2 coronavirus, currently causing a worldwide pandemic, are designed and simulated. The inhibitors are formed by two sequential self-supporting alpha-helices (bundle) extracted from the protease domain (PD) of angiotensin-converting enzyme 2 (ACE2), which binds to the SARS-CoV-2 receptor binding domains. Molecular dynamics simulations revealed that the peptides maintain their secondary structure and provide a highly specific and stable binding (blocking) to SARS-CoV-2, determined by their sequences and conformations. The proposed peptide inhibitors could provide simple therapeutics against the COVID-19 disease.</div

Retrained Generic Antibodies Can Recognize SARS-CoV-2

The dramatic impact which novel viruses can have on the human society could be mitigated without the need of vaccination if antibodies present within the population are retrained to recognize these viruses. With this idea in mind, a double-faced peptide-based boosters are computationally designed to allow recognition of SARS-CoV-2 by Hepatitis B antibodies. One booster face is made of ACE2-mimic peptides that can bind to the receptor binding domain (RBD) of SARS-CoV-2. The other booster face is composed of a Hepatitis B core-antigen, targeting the Hepatitis B antibody fragment. Molecular dynamics simulations revealed that the designed boosters have a highly specific and stable bindingboth to RBD and the antibody fragment (AF). This approach can provide a cheap and efficient neutralization of emerging pathogens.</div

Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the last decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development ofadaptive (smart) therapeutics. Here, we develop a computational strategy to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2). Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), we gradually modify the templates by random mutations, while retaining those mutations that maximize their RBD-binding free energies. In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions. The computational search will provide librariesof optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale. <br /

Changing the Threshold in a Bivariate Polynomial Based Secret Image Sharing Scheme

Secret image sharing (SIS) is an important application of the traditional secret sharing scheme, which has become popular in recent years. In an SIS scheme, a confidential image is encrypted into a group of shadows. Any set of shadows that reaches the threshold can reconstruct the image; otherwise, nothing can be recovered at all. In most existing SIS schemes, the threshold on shadows for image reconstruction is fixed. However, in this work, we consider more complicated cases of SIS, such that the threshold is changeable according to the security environment. In this paper, we construct a (k&harr;h,n) threshold-changeable SIS (TCSIS) scheme using a bivariate polynomial, which provides h&minus;k+1 possible thresholds, k,k+1,&hellip;,h. During image reconstruction, each participant can update their shadow according to the current threshold T based only on their initial shadow. Unlike previous TCSIS schemes, the proposed scheme achieves unconditional security and can overcome the information disclosure problem caused by homomorphism