In small volumes, the kinetics of filamentous protein self-assembly is expected to show significant variability, arising from intrinsic molecular noise. This is not accounted for in existing deterministic models. We introduce a simple stochastic model including nucleation and autocatalytic growth via elongation and fragmentation, which allows us to predict the effects of molecular noise on the kinetics of autocatalytic self-assembly. We derive an analytic expression for the lag-time distribution, which agrees well with experimental results for the fibrillation of bovine insulin. Our expression decomposes the lag-time variability into contributions from primary nucleation and autocatalytic growth and reveals how each of these scales with the key kinetic parameters. Our analysis shows that significant lag-time variability can arise from both primary nucleation and from autocatalytic growth and should provide a way to extract mechanistic information on early-stage aggregation from small-volume experiments.

Amyloid fibrils are self-assembled protein aggregates implicated in a number of human diseases. Fragmentation-dominated models for the self-assembly of amyloid fibrils have had important successes in explaining the kinetics of amyloid fibril formation but predict fibril length distributions that do not match experiments. Here we resolve this inconsistency using a combination of experimental kinetic measurements and computer simulations. We provide evidence for a structural transition that occurs at a critical fibril mass concentration, or ‘CFC’, above which fragmentation of fibrils is suppressed. Our simulations predict the formation of distinct fibril length distributions above and below the CFC, which we confirm by electron microscopy. These results point to a new picture of amyloid fibril growth in which structural transitions that occur during self-assembly have strong effects on the final population of aggregate species with small, and potentially cytotoxic, oligomers dominating for long periods of time at protein concentrations below the CFC.

Kinetic measurements of the self-assembly of proteins into amyloid fibrils are often used to make inferences about molecular mechanisms. In particular, the lag time--the quiescent period before aggregates are detected--is often found to scale with the protein concentration as a power law, whose exponent has been used to infer the presence or absence of autocatalytic growth processes such as fibril fragmentation. Here we show that experimental data for lag time versus protein concentration can show signs of kinks: clear changes in scaling exponent, indicating changes in the dominant molecular mechanism determining the lag time. Classical models for the kinetics of fibril assembly suggest that at least two mechanisms are at play during the lag time: primary nucleation and autocatalytic growth. Using computer simulations and theoretical calculations, we investigate whether the competition between these two processes can account for the kinks which we observe in our and others' experimental data. We derive theoretical conditions for the crossover between nucleation-dominated and growth-dominated regimes, and analyze their dependence on system volume and autocatalysis mechanism. Comparing these predictions to the data, we find that the experimentally observed kinks cannot be explained by a simple crossover between nucleation-dominated and autocatalytic growth regimes. Our results show that existing kinetic models fail to explain detailed features of lag time versus concentration curves, suggesting that new mechanistic understanding is needed. More broadly, our work demonstrates that care is needed in interpreting lag-time scaling exponents from protein assembly data.

The ability to control the morphologies of biomolecular aggregates is a central objective in the study of self-assembly processes. The development of predictive models offers the surest route for gaining such control. Under the right conditions, proteins will self-assemble into fibers that may rearrange themselves even further to form diverse structures, including the formation of closed loops. In this study, chicken egg white ovalbumin is used as a model for the study of fibril loops. By monitoring the kinetics of self-assembly, we demonstrate that loop formation is a consequence of end-to-end association between protein fibrils. A model of fibril formation kinetics, including end-joining, is developed and solved, showing that end-joining has a distinct effect on the growth of fibrillar mass density (which can be measured experimentally), establishing a link between self-assembly kinetics and the underlying growth mechanism. These results will enable experimentalists to infer fibrillar morphologies from an appropriate analysis of self-assembly kinetic data.