# Method Development

The Lerner lab probe biomolecular structural dynamics using spectroscopic nano-rulers with single-molecule sensitivity. We combine these experimental results together with other high information-content experiments with computational modeling of macromolecular structure & dynamics.

FRET imaging of weak protein-protein interactions

One of the most common uses of the FRET <10 nm sensitive ruler is to identify protein-protein interactions in fluorescence imaging.

Assume we suspect proteins A & B interact in the cell. Imaging them as they interact can be classically achieved by genetically fused fluorescent proteins (FP) to protein A & to protein B. The two FPs fused to proteins A & B serve as donor & acceptor of FRET. This assay works well in the case of strong interactions between proteins A & B. Many such A & B proteins will be found together (in within the FRET distance range) for long periods of time. However, if the results of the fluorescence imaging do not indicate proteins A & B are interacting, does it necessarily mean there are no pairs of interacting proteins A & B?

The case of weak interactions is a challenge in biology - we cannot necessarily infer from negative results of FRET imaging that there are necessarily no interactions. The best we can do is to claim that if there are interactions, there are just a few of them, and they are below the background of non-interacting proteins A & B. Indeed, if proteins A & B weakly interact, there will be short periods of time in which a few of them will be found together (in within the FRET distance range). This means each pixel of the microscopic image will include many proteins A & B that are not interacting, and maybe just a few that are interacting, so most of the signal will be of pure fluorescence with no signatures of FRET.

The Lerner lab is developing a new fluorescence imaging approach that will assist in elucidating these weakly-interacting proteins in FRET imaging.

Weak interactions exposed in new type of FRET imaging

smPIFE - a single-molecule spatial sensor shorter than FRET for identifying sub-populations of local biomolecular structures

Single-molecule FRET (smFRET), is a very useful tool for identifying and distinguishing between different sub-populations of biomolecular conformations, that is based on the distance dependence of the excitation energy transfer, which is sensitive to inter-dye distances in the 3-10 nm range. However, this range clearly does not cover sensitivity towards changes within <3 nm distances that are prevalent in local biomolecular structures. This is especially important when studying intrinsically-disordered proteins, which do not seem to exhibit stable global structure, but rather extremely rapid overall structural dynamics (withion nanoseconds-to-microseconds). In such cases, it is worthwhile testing whether while using smFRET the overall structure rapidly changes, certain local structures might actually stay stable for much longer times (milliseconds or slower).

For that, we utilize the time-resolved version of single-molecule protein-induced fluorescence enhancement (smPIFE; see Zaer & Lerner 2021, JoVE). In smPIFE, different molecules, one by one, are classified to different sub-populations according to the fluorescence lifetime of a Cy3 labeling a certain amino acid, where the higher the fluorescence lifetime is, the more steric hindrance is experienced nearby that amino acid side chain at that structural sub-population. Using this approach, we have recently shown that the free-form $$\alpha$$-Syn exhibits local structural sub-populations that are stable for 10-100 ms, which is quite stable relative to its lack of global structural stability (overall structural dynamics in hundreds of nanoseconds!).

We now continue to develop this tool, transforming it from a semi-quantitative tool, to a fully quantitative one, which will also allow using its results as experimentally-derived restraints of integrative structural modeling.

Bio-detection of specific intact viruses in minutes, at high sensitivities and with minimal false negatives

The outburst of the COVID-19 pandemic served as a global game changers in so many aspects. Many aspects of pandemic preparedness re-opened for scientific evaluation. One aspect that was under scrutiny was the current technologies with which we identify and quantify viral infections with high specificity and high sensitivity, as rapid as we possibly can. Rapid testing is viable to assist in bringing society back to normal life routine, with minimal viral exposure risks.

The most accurate testing technique is based on RT-PCR. The main drawback is the time it takes to get these accurate results (hours). There are alternative rapid testing approaches, including point-of-care tests. However, these tend to produce minutes-time results leaving many positive undetected, in what is better known as false negatives. This is due to the trade off between accuracy and sensitivity. Since viral loads of the COVID-19 can be quite low, rapid tests oftentimes leave high amount of false negatives that carry viral loads below the rapid detection schemes, and can only be accurately quantified with the RT-PCR based approach.

Like many research groups around the world, the Lerner lab was also intrigued by these questions. Using our unique know-how & experience, we have recently developed an approach to specifically detect intact viruses and to quantify low viral loads in less than 10 minutes, minimizing the rate of false negatives within symptomatics. The new technique is easy-to-use and affordable, and further developments will enable testing its potential use as a platform for accurate rapid testing. The technique is easily adaptable to viruses other than SARS-CoV-2.

More to come soon...

This research is a collaboration with Prof. Dr. Thorben Cordes (LMU, Munich), Prof. Eran Zahavy (IIBR, Israel) and in consultation with Prof. Moshe Kotler and Dr. Alex Rouvinski (Haddassah Ein Kerem medical Campus, Hebrew U). This research is supported by Milner Foundation and by the ISRAEL SCIENCE FOUNDATION (grant No. 3565/20) within the KillCorona – Curbing Coronavirus Research Program.