A Threshold-Limited Fluorescence Probe for Viscosity

Viscosity of body fluid is an established biomarker of pathological conditions. Abnormality of cellular viscosity occurs when cells are challenged with external stresses. Small molecule probes to assess the viscosity are sought after for both disease diagnostics and basic studies. Fluorescence based probes are particular attractive due to their potentials for convenient and high spatiotemporal resolution microscopic monitoring of biological samples. The dyes with a floppy push-pull backbone or dyes with a rotatable substituent exhibits a viscosity responsive fluorescence enhancement and therefore viable viscosity probes. The scaffold of the existing viscosity probes contains typically one such floppy site. Therefore, they typically linearly respond to log(viscosity). We argue that minor viscosity fluctuation could potentially be physiological as the biological system is dynamic. We wish to develop a type of conceptually-new, threshold-limited viscosity probes, to complement the existing probes. Such probes do not exhibit a fluorescence enhancement when challenged with minor and presumably physiological enhancement of viscosity. When the viscosity is higher than a certain threshold, their fluorescence turns on. We hypothesize that a dye with two far-apart floppy sites could potentially yield such a threshold-limited signal and designed VPZ2 and VPZ3. Through spectral titration, VPZ3 was found to yield the desired threshold-limited signal. VPZ3 was suitable for in vitro bioimaging of viscosity under one-photon or two-photon excitation. VPZ3 is potentially useful in many downstream applications. Future work includes fine-tune of the threshold to allow tailored limit for fluorescence turn-on to better meet the need of different applications. Besides the implications in the real-world applications, the design concept could also be translated to design of alternative substrates

A rule-based algorithm for automatic bond type perception

Abstract Assigning bond orders is a necessary and essential step for characterizing a chemical structure correctly in force field based simulations. Several methods have been developed to do this. They all have advantages but with limitations too. Here, an automatic algorithm for assigning chemical connectivity and bond order regardless of hydrogen for organic molecules is provided, and only three dimensional coordinates and element identities are needed for our algorithm. The algorithm uses hard rules, length rules and conjugation rules to fix the structures. The hard rules determine bond orders based on the basic chemical rules; the length rules determine bond order by the length between two atoms based on a set of predefined values for different bond types; the conjugation rules determine bond orders by using the length information derived from the previous rule, the bond angles and some small structural patterns. The algorithm is extensively evaluated in three datasets, and achieves good accuracy of predictions for all the datasets. Finally, the limitation and future improvement of the algorithm are discussed.</p

The Selective Interaction between Silica Nanoparticles and Enzymes from Molecular Dynamics Simulations

<div><p>Nanoscale particles have become promising materials in many fields, such as cancer therapeutics, diagnosis, imaging, drug delivery, catalysis, as well as biosensors. In order to stimulate and facilitate these applications, there is an urgent need for the understanding of the interaction mode between the nano-particles and proteins. In this study, we investigate the orientation and adsorption between several enzymes (cytochrome c, RNase A, lysozyme) and 4 nm/11 nm silica nanoparticles (SNPs) by using molecular dynamics (MD) simulation. Our results show that three enzymes are adsorbed onto the surfaces of both 4 nm and 11 nm SNPs during our MD simulations and the small SNPs induce greater structural stabilization. The active site of cytochrome c is far away from the surface of 4 nm SNPs, while it is adsorbed onto the surface of 11 nm SNPs. We also explore the influences of different groups (-OH, -COOH, -NH₂ and CH₃) coated onto silica nanoparticles, which show significantly different impacts. Our molecular dynamics results indicate the selective interaction between silicon nanoparticles and enzymes, which is consistent with experimental results. Our study provides useful guides for designing/modifying nanomaterials to interact with proteins for their bio-applications.</p></div

Comparison of the structure of lysozyme adsorbed onto different diameter of SNPs.

<p>(a) align the crystal structure (gray) with the structure adsorbed onto 4 nm SNP (blue), (b) align the crystal structure (gray) with the structure adsorbed onto 11 nm SNP (red). The structures are the conformations after 100 ns MD simulation.</p

Calculated RMSF of Cα atoms vs protein residue number for (a) cytochrome c (103 residues), (b) RNase A (124 residues), and (c) lysozyme (130 residues) during the MD simulation.

<p>A comparison between the RMSF plot for natural structures of proteins, proteins adsorbed onto the surface of 4 nm SNPs, and the proteins adsorbed onto the surface of 11 ns SNPs.</p

The binding mode of TBU (tertiary-butyl alcohol, highlighted in the stick model) in (a) the crystal structure of RNase A, (b) the structure of RNase A adsorbed onto 4 nm SNP (after 100 ns MD simulation), (c) the structure of RNase A adsorbed onto 11 nm SNP (after 100 ns MD simulation).

<p>The binding mode of TBU (tertiary-butyl alcohol, highlighted in the stick model) in (a) the crystal structure of RNase A, (b) the structure of RNase A adsorbed onto 4 nm SNP (after 100 ns MD simulation), (c) the structure of RNase A adsorbed onto 11 nm SNP (after 100 ns MD simulation).</p

Structure of lysozyme adsorbed onto the surface of SNPs.

<p>(a) with 4 nm SNP, (b) with 11 nm SNP. The structures are the conformations after 100 ns MD simulation.</p

The active site of cytochrome c after it absorbs onto (a) 4 nm SNP, (b) 11 nm SNP.

<p>The structures are the conformations after 100 ns MD simulation.</p