Deep-LASI, single-molecule data analysis software.

By avoiding ensemble averaging, single-molecule methods provide novel means of extracting mechanistic insights into function of material and molecules at the nanoscale. However, one of the big limitations is the vast amount of data required for analyzing and extracting the desired information, which is time-consuming and user dependent. Here, we introduce Deep-LASI, a software suite for the manual and automatic analysis of single-molecule traces, interactions, and the underlying kinetics. The software can handle data from one-, two- and three-color fluorescence data, and was particularly designed for the analysis of two- and three-color single-molecule fluorescence resonance energy transfer experiments. The functionalities of the software include: the registration of multiple-channels, trace sorting and categorization, determination of the photobleaching steps, calculation of fluorescence resonance energy transfer correction factors, and kinetic analyses based on hidden Markov modeling or deep neural networks. After a kinetic analysis, the ensuing transition density plots are generated, which can be used for further quantification of the kinetic parameters of the system. Each step in the workflow can be performed manually or with the support of machine learning algorithms. Upon reading in the initial data set, it is also possible to perform the remaining analysis steps automatically without additional supervision. Hence, the time dedicated to the analysis of single-molecule experiments can be reduced from days/weeks to minutes. After a thorough description of the functionalities of the software, we also demonstrate the capabilities of the software via the analysis of a previously published dynamic three-color DNA origami structure fluctuating between three states. With the drastic time reduction in data analysis, new types of experiments become realistically possible that complement our currently available palette of methodologies for investigating the nanoworld.

Folding pathway of a discontinuous two-domain protein.

It is estimated that two-thirds of all proteins in higher organisms are composed of multiple domains, many of them containing discontinuous folds. However, to date, most in vitro protein folding studies have focused on small, single-domain proteins. As a model system for a two-domain discontinuous protein, we study the unfolding/refolding of a slow-folding double mutant of the maltose binding protein (DM-MBP) using single-molecule two- and three-color Förster Resonance Energy Transfer experiments. We observe a dynamic folding intermediate population in the N-terminal domain (NTD), C-terminal domain (CTD), and at the domain interface. The dynamic intermediate fluctuates rapidly between unfolded states and compact states, which have a similar FRET efficiency to the folded conformation. Our data reveals that the delayed folding of the NTD in DM-MBP is imposed by an entropic barrier with subsequent folding of the highly dynamic CTD. Notably, accelerated DM-MBP folding is routed through the same dynamic intermediate within the cavity of the GroEL/ES chaperone system, suggesting that the chaperonin limits the conformational space to overcome the entropic folding barrier. Our study highlights the subtle tuning and co-dependency in the folding of a discontinuous multi-domain protein.

Mapping Extracellular Space Features of Viral Encephalitis to Evaluate the Proficiency of Anti-Viral Drugs.

The extracellular space (ECS) is an important barrier against viral attack on brain cells, and dynamic changes in ECS microstructure characteristics are closely related to the progression of viral encephalitis in the brain and the efficacy of antiviral drugs. However, mapping the precise morphological and rheological features of the ECS in viral encephalitis is still challenging so far. Here, a robust approach is developed using single-particle diffusional fingerprinting of quantum dots combined with machine learning to map ECS features in the brain and predict the efficacy of antiviral encephalitis drugs. These results demonstrated that this approach can characterize the microrheology and geometry of the brain ECS at different stages of viral infection and identify subtle changes induced by different drug treatments. This approach provides a potential platform for drug proficiency assessment and is expected to offer a reliable basis for the clinical translation of drugs.