Biological measurement beyond the quantum limit

Quantum noise places a fundamental limit on the per photon sensitivity attainable in optical measurements. This limit is of particular importance in biological measurements, where the optical power must be constrained to avoid damage to the specimen. By using non-classically correlated light, we demonstrated that the quantum limit can be surpassed in biological measurements. Quantum enhanced microrheology was performed within yeast cells by tracking naturally occurring lipid granules with sensitivity 2.4 dB beyond the quantum noise limit. The viscoelastic properties of the cytoplasm could thereby be determined with a 64% improved measurement rate. This demonstration paves the way to apply quantum resources broadly in a biological context

Shape-induced force fields in optical trapping

Advances in optical tweezers, coupled with the proliferation of two-photon polymerization systems, mean that it is now becoming routine to fabricate and trap non-spherical particles. The shaping of both light beams and particles allows fine control over the flow of momentum from the optical to mechanical regimes. However, understanding and predicting the behaviour of such systems is highly complex in comparison with the traditional optically trapped microsphere. In this Article, we present a conceptually new and simple approach based on the nature of the optical force density. We illustrate the method through the design and fabrication of a shaped particle capable of acting as a passive force clamp, and we demonstrate its use as an optically trapped probe for imaging surface topography. Further applications of the design rules highlighted here may lead to new sensors for probing biomolecule mechanics, as well as to the development of optically actuated micromachines

Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands

Phenomenological analysis of ATP dependence of motor protein

In this study, through phenomenological comparison of the velocity-force data of processive motor proteins, including conventional kinesin, cytoplasmic dynein and myosin V, we found that, the ratio between motor velocities of two different ATP concentrations is almost invariant for any substall, superstall or negative external loads. Therefore, the velocity of motor can be well approximated by a Michaelis-Menten like formula V=\atp k(F)L/(\atp +K_M), with $L$ the step size, and $k(F)$ the external load $F$ dependent rate of one mechanochemical cycle of motor motion in saturated ATP solution. The difference of Michaelis-Menten constant $K_M$ for substall, superstall and negative external load indicates, the ATP molecule affinity of motor head for these three cases are different, though the expression of $k(F)$ as a function of $F$ might be unchanged for any external load $F$. Verifications of this Michaelis-Menten like formula has also been done by fitting to the recent experimental data

A Rigidity-Enhanced Antimicrobial Activity: A Case for Linear Cationic α-Helical Peptide HP(2–20) and Its Four Analogues

Linear cationic α-helical antimicrobial peptides are referred to as one of the most likely substitutes for common antibiotics, due to their relatively simple structures (≤40 residues) and various antimicrobial activities against a wide range of pathogens. Of those, HP(2–20) was isolated from Helicobacter pylori ribosomal protein. To reveal a mechanical determinant that may mediate the antimicrobial activities, we examined the mechanical properties and structural stabilities of HP(2–20) and its four analogues of same chain length by steered molecular dynamics simulation. The results indicated the following: the resistance of H-bonds to the tensile extension mediated the early extensive stage; with the loss of H-bonds, the tensile force was dispensed to prompt the conformational phase transition; and Young's moduli (N/m2) of the peptides were about 4∼8×109. These mechanical features were sensitive to the variation of the residue compositions. Furthermore, we found that the antimicrobial activity is rigidity-enhanced, that is, a harder peptide has stronger antimicrobial activity. It suggests that the molecular spring constant may be used to seek a new structure-activity relationship for different α-helical peptide groups. This exciting result was reasonably explained by a possible mechanical mechanism that regulates both the membrane pore formation and the peptide insertion

Single Molecule Analysis Research Tool (SMART): An Integrated Approach for Analyzing Single Molecule Data

Single molecule studies have expanded rapidly over the past decade and have the ability to provide an unprecedented level of understanding of biological systems. A common challenge upon introduction of novel, data-rich approaches is the management, processing, and analysis of the complex data sets that are generated. We provide a standardized approach for analyzing these data in the freely available software package SMART: Single Molecule Analysis Research Tool. SMART provides a format for organizing and easily accessing single molecule data, a general hidden Markov modeling algorithm for fitting an array of possible models specified by the user, a standardized data structure and graphical user interfaces to streamline the analysis and visualization of data. This approach guides experimental design, facilitating acquisition of the maximal information from single molecule experiments. SMART also provides a standardized format to allow dissemination of single molecule data and transparency in the analysis of reported data

Ultrafast force-clamp spectroscopy of single molecules reveals load dependence of myosin working stroke

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Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way in which sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we compute energy landscapes, which describe the accessible secondary structures for a range of sequence lengths, to analyze the transcriptional process as a given sequence elongates to full length. In line with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct, dominant “downhill” pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states, each associated with a different biological function. Sharing concepts with the “new view” of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved in various riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design

Single-Molecule Force Spectroscopy: Experiments, Analysis, and Simulations

International audienceThe mechanical properties of cells and of subcellular components are important to obtain a mechanistic molecular understanding of biological processes. The quantification of mechanical resistance of cells and biomolecules using biophysical methods matured thanks to the development of nanotechnologies such as optical and magnetic tweezers, the biomembrane force probe and atomic force microscopy (AFM). The quantitative nature of force spectroscopy measurements has converted AFM into a valuable tool in biophysics. Force spectroscopy allows the determination of the forces required to unfold protein domains and to disrupt individual receptor/ligand bonds. Molecular simulation as a computational microscope allows investigation of similar biological processes with an atomistic detail. In this chapter, we first provide a step-by-step protocol of force spectroscopy including sample preparation, measurement and analysis of force spectroscopy using AFM and its interpretation in terms of available theories. Next, we present the background for molecular dynamics (MD) simulations focusing on steered molecular dynamics (SMD) and the importance of bridging of computational tools with experimental technique

Modulating RNA structure and catalysis: lessons from small cleaving ribozymes

RNA is a key molecule in life, and comprehending its structure/function relationships is a crucial step towards a more complete understanding of molecular biology. Even though most of the information required for their correct folding is contained in their primary sequences, we are as yet unable to accurately predict both the folding pathways and active tertiary structures of RNA species. Ribozymes are interesting molecules to study when addressing these questions because any modifications in their structures are often reflected in their catalytic properties. The recent progress in the study of the structures, the folding pathways and the modulation of the small ribozymes derived from natural, self-cleaving, RNA motifs have significantly contributed to today’s knowledge in the field