Michael Cianfrocco
Life Sciences Institute
210 Washtenaw Ave.
Ann Arbor, MI 48109
[email protected]

Available to mentor

Michael Cianfrocco
Research Assistant Professor
  • About
  • Links
  • Qualifications
  • Research Overview
  • Recent Publications
  • About

    Our research team is trying to understand the molecular details determining 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.

    Overarching questions we are trying to answer: How is motor protein activity turned ‘on’ and ‘off’? How do viruses hijack motor protein activity?
    How do microtubule structure and post-translational modifications affect motor protein activity?

    We are approaching these questions from several angles, using cryo-electron microscopy, single molecule TIRF, and biochemistry to relate protein structure to its activity in a cell.

    Links
    • Cianfrocco Lab
    • Google Scholar
    Qualifications
    • Damon Runyon HHMI Post-doctoral fellow
      University of California - San Diego, Cellular & Molecular Medicine, 2017
    • Damon Runyon HHMI Post-doctoral fellow
      Harvard University, Molecular and Cellular Biology, 2015
    • Damon Runyon HHMI Post-doctoral Fellow
      Harvard Medical School, Cell Biology, 2015
    • PhD
      University of California - Berkeley (Advisor: Eva Nogales), Berkeley, 2012
    • BS
      Providence College, Providence, 2007
    Research Overview

    Kinesin regulation: Kinesins are a ubiquitous motor protein that has been intensively studied over the past 30 years, yet a key question remains: How do you turn off kinesin activity? In the lab, we study two regulatory strategies for turning off kinesin in cis (via autoinhibition) and trans (via kinesin-binding protein). As we develop models of kinesin inhibition, we are extending our work into kinesin activation by cargos and cargo adaptors.

    Viral hijacking of motor proteins: Many viruses exploit the microtubule cytoskeletal network to access the host cell nucleus. These viruses include HIV-1, rabies virus, herpes virus, SV40 and adenovirus. In the laboratory, we are using reconstitution biochemistry, single-molecule imaging and cryo-EM to understand how motor protein activity is hijacked by viral pathogens.

    Microtubule post-translational modifications: Microtubules are dynamic cytoskeletal filaments that undergo rapid growth and shrinkage. On top of these dynamics, specific modifications alter microtubule structure and affect motor protein activity. In the lab, we work on several microtubule modifications and ‘readers’ of the microtubule post-translational modification ‘code.’

    Tool Development for Cryo-Electron Microscopy: As a fast-growing part of structural biology, 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 new users to come into the fold to determine structures. Our laboratory designs new algorithms and builds computational infrastructure to implement streamlined, intelligent cryo-EM workflows.

    Algorithm development: Cryo-EM data collection remains bespoke, cumbersome, and inefficient. We are leveraging databases of 350,000+ micrographs in the laboratory to determine optimal path planning across cryo-EM grids. Navigating on a cryo-EM grid is akin to exploring an unknown landscape without prior knowledge of ‘good’ and ‘bad’ areas. We believe incorporating artificial intelligence will enable high-quality, automated cryo-EM data collection to remove human users from microscope operations. Beyond data collection, we are constructing data processing pipelines that capture human expertise into trained neural networks. We believe that early steps in cryo-EM must become automated and robust so that automation in data collection will be coupled with higher throughput processing.

    Building cyberinfrastructure for structural biology: Cryo-EM requires access to high-performance computing capabilities, unlike other structural biology tools. The large computational workload will limit the throughput and spread of cryo-EM due to users 1) waiting for cluster time or 2) finding a cluster amenable to cryo-EM. To address these problems, we have built cloud computing resources on Amazon Web Service and the San Diego Supercomputer Center to help give users access to cryo-EM, so they can focus on understanding biology instead of dealing with Linux.

    COSMIC² Science Gateway: The COSMIC² science gateway(cosmic2.sdsc.edu) is a public resource for determining cryo-EM structures and predicting protein structure using AlphaFold. COSMIC² provides a simple web interface to access National Science Foundation ACCESS supercomputing resources. As of July 2023, 3800+ worldwide users had submitted 15,000+ jobs to the Expanse Supercomputer.

    Recent Publications See All Publications
    • Preprint
      HIV-1 binds dynein directly to hijack microtubule transport machinery.
      Badieyan S, Lichon D, Andreas MP, Gillies JP, Peng W, Shi J, DeSantis ME, Aiken CR, Böcking T, Giessen TW, Campbell EM, Cianfrocco MA. 2023 Dec 2; DOI:10.1101/2023.08.29.555335
      PMID: 37693451
    • Journal Article
      Autoinhibited kinesin-1 adopts a hierarchical folding pattern
      Tan Z, Yue Y, Leprevost F, Haynes S, Basrur V, Nesvizhskii AI, Verhey KJ, Cianfrocco MA. eLife, 12: DOI:10.7554/elife.86776.3
    • Journal Article
      Cloud computing platforms to support cryo-EM structure determination.
      Li Y, Cianfrocco MA. Trends Biochem Sci, 2022 Feb; 47 (2): 103 - 105. DOI:10.1016/j.tibs.2021.11.005
      PMID: 34895958
    • Journal Article
      Kinesin-binding protein remodels the kinesin motor to prevent microtubule binding.
      Solon AL, Tan Z, Schutt KL, Jepsen L, Haynes SE, Nesvizhskii AI, Sept D, Stumpff J, Ohi R, Cianfrocco MA. Sci Adv, 2021 Nov 19; 7 (47): eabj9812 DOI:10.1126/sciadv.abj9812
      PMID: 34797717
    • Journal Article
      High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines.
      Li Y, Cash JN, Tesmer JJG, Cianfrocco MA. Structure, 2020 Jul 7; 28 (7): 858 - 869.e3. DOI:10.1016/j.str.2020.03.008
      PMID: 32294468
    • Journal Article
      What Could Go Wrong? A Practical Guide to Single-Particle Cryo-EM: From Biochemistry to Atomic Models.
      Cianfrocco MA, Kellogg EH. J Chem Inf Model, 2020 May 26; 60 (5): 2458 - 2469. DOI:10.1021/acs.jcim.9b01178
      PMID: 32078321
    • Presentation
      Artificial intelligence algorithms to automate cryo-EM data collection
      Cianfrocco M. 2024 Jan 16;
    • Presentation
      Autoinhibited kinesin-1 adopts a hierarchical folding pattern
      Cianfrocco M. 2023 Dec 5;