Recent experimental studies have measured a 30−80% increase of the diffusion coefficient when various enzymes, including aldolase, are catalytically active. This observation has been supported by several theoretical explanations; however, other theoretical studies argue against the possibility of enhanced diffusion, and two of them ascribe the experimental observations to potential artifacts arising in fluorescence correlation spectroscopy (FCS) measurements. Here, we utilized dynamic light scattering (DLS) to measure the diffusion coefficient of aldolase in the absence and presence of its substrate. The DLS measurements have an experimental error of 3% and do not find a statistically significant change of the aldolase diffusion coefficient even at a saturating substrate concentration. This finding lends support to the contention that photophysical artifacts may have affected the FCS measurements and challenges the idea that enzymes can be self-propelled by their catalytic activity.

Motor proteins such as myosin and kinesin play a major role in cellular cargo transport, muscle contraction, cell division, and engineered nanodevices. Quantifying the collective behavior of coupled motors is critical to our understanding of these systems. An excellent model system is the gliding motility assay, where hundreds of surface-adhered motors propel one cytoskeletal filament such as an actin filament or a microtubule. The filament motion can be observed using fluorescence microscopy, revealing fluctuations in gliding velocity. These velocity fluctuations have been previously quantified by a motional diffusion coefficient, which Sekimoto and Tawada explained as arising from the addition and removal of motors from the linear array of motors propelling the filament as it advances, assuming that different motors are not equally efficient in their force generation. A computational model of kinesin head diffusion and binding to the microtubule allowed us to quantify the heterogeneity of motor efficiency arising from the combination of anharmonic tail stiffness and varying attachment geometries assuming random motor locations on the surface and an absence of coordination between motors. Knowledge of the heterogeneity allows the calculation of the proportionality constant between the motional diffusion coefficient and the motor density. The calculated value (0.3) is within a standard error of our measurements of the motional diffusion coefficient on surfaces with varying motor densities calibrated by landing rate experiments. This allowed us to quantify the loss in efficiency of coupled molecular motors arising from heterogeneity in the attachment geometry.

The study of protein adsorption at the single molecule level has recently revealed that the adsorption is reversible, but with a long-tailed residence time distribution which can be approximated with a sum of exponential functions putatively related to distinct adsorption sites. Here it is proposed that the shape of the residence time distribution results from an adsorption process with sequential and reversible steps that contribute to overall binding strength resembling “zippering”. In this model, the survival function of the residence time distribution of single proteins varies from an exponential distribution for a single adsorption step to a power law distribution with exponent −1/2 for a large number of adsorption steps. The adsorption of fluorescently labeled fibrinogen to glass surfaces is experimentally studied with single molecule imaging. The experimental residence time distribution can be readily fit by the proposed model. This demonstrates that the observed long residence times can arise from stepwise adsorption rather than rare but strong binding sites and provides guidance for the control of protein adsorption to biomaterials.

Ecosystems are characterized by particular scaling laws describing, for example, the relationship between animal abundance and species body weight. It is hypothesized that technological systems follow similar scaling laws, where the abundance of a type of machine correlates with its size. Human progress continuously expands the range of accessible machine sizes, creating a technology trend toward vast numbers of microscopic machines. Current research related to nanomotors, such as the report by Kumar et al. in this issue of ACS Nano describing advances in controlling biomolecular motors, lays the scientific foundation for this trend.

No abstract