Michael Cianfrocco


Research Interests

We are a research team that is trying to understand the molecular details that determine how, where, and when motor proteins transport intracellular cargo. The past thirty years of cell biology research have set the stage for us to determine the general principles that underlie the complex process of intracellular transport.

Specifically, we are interested in the mechanisms that dictate how cytoplasmic dynein and kinesin are recruited to- and activated for- cargo transport. For each project, we will be trying to answer the following questions:

How is specificity determined between motor proteins and cargo?
How are the activities of multi-motor complexes regulated? 
What are the molecular consequences of neurodegenerative disease-causing mutations?

We will be approaching these questions using state of the art technologies that range from mammalian cell protein expression to cryo-EM to single molecule fluorescence assays. This integrated approach will allow us to relate how changes at the molecular level alter the structure and function of transporting motor-cargo complexes.

Tool development for cryo-electron microscopy
As a fast-growing part of structural biology, cryo-electron microscopy (cryo-EM) is determining new and exciting macromolecular structures on a seemingly daily basis. Despite its power, cryo-EM is a field that needs to undergo rapid maturation to allow for new users to come into the fold to solve structures. Unlike other structural biology tools, cryo-EM necessarily requires access to high-performance computing capabilities. The large computational workload will limit the throughput and spread of cryo-EM due to users 1) waiting for cluster time or 2) being unable to find a cluster amenable for cryo-EM.

To address these problems, we are building cloud computing resources at Amazon Web Services and the San Diego Supercomputer Center to help give users access to cryo-EM so they can focus on understanding biology instead of deal with Linux. In addition to these new software tools, we are also considering new methods that will give un-supervised assessment of single particle electron microscopy data quality, given the large computing resources of the cloud.