John Karanicolas, PhD

John Karanicolas, PhD

This Fox Chase professor participates in the Undergraduate Summer Research Fellowship
Learn more about Research Volunteering.

Lab Overview

  • Designing Protein Switches and Sensors Important advances in cell biology have been enabled though selective activation of proteins involved in glycosylation, nuclear import/export, proteolysis, signaling across the cell membrane, and cell suicide.

  • Modulating Protein Function with Small Molecules, Using Computational and Experimental Design Techniques Our primary goal is to develop structure-based approaches for modulating protein function using small-molecules. We are exploring two parallel paths towards this overarching goal: the first is re-engineering proteins so that a small-molecule can be used to “turn on” function, and the second is identifying small-molecules that naturally complement and occlude a protein surface to “turn off” function. We apply these tools to understand how specific protein interactions are responsible for normal and aberrant signal transduction in cells.

    Educational Background

    • PhD, Macromolecular and Cellular Structure and Chemistry, The Scripps Research Institute, La Jolla, CA, 2003
    • BSc, McGill University, Montreal, Quebec, 1998


    • American Association for Cancer Research (AACR)
    • American Chemical Society (ACS)

    Honors & Awards

    • Leading Light Award for research productivity, University of Kansas, 2013
    • Alfred P. Sloan Research Fellowship, 2010-2012
    • Damon Runyon Postdoctoral Fellowship, 2004-2007
    • Jairo H. Arevelo Fellowship, 2002-2003
    • La Jolla Interfaces in Science (Burroughs Wellcome Fund) Postgraduate Scholarship, 2001-2003
    • Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship, 2000-2002


    • Grigorii Andrianov
      PhD student
    • Lei Ke
      PhD student
    • Chris Parry
    • Jake Khowsathit
      Postdoctoral Fellow
    • Palani Kirubakaran
      Postdoctoral Fellow
    • Sven Miller
      Postdoctoral Fellow
    • Daniel Yeggoni
      Postdoctoral Fellow

    Research Interests

    Modulating protein activity using small molecules

    My lab builds novel chemical tools for modulating biological systems. We “design in” new ligand binding sites that allow us to selectively activate proteins, and we identify novel chemical tools to disrupt – or enhance – protein activity. We are especially focused on:

    • Designing selective kinase inhibitors via deep learning
    • Building new therapeutic antibodies that can be selectively activated using small molecules
    • Designing compounds that restore activity to mutant p53 / mutant VHL

    Lab Overview

    Our goal is to develop structure-based approaches for modulating protein function using small-molecules. We apply these new approaches in projects seeking to re-activate disabled tumor suppressors, inhibit cancer-driving RNA-binding proteins, disable key oncogenic kinases, and tune the activity of antibodies used in cancer immunotherapy.


    Lab Description

    My lab’s research program is intensely focused on building new computational design approaches, to be immediately applied in pressing challenges in cancer biology.

    In our first key project, we recognize that modern cancer biology leans heavily on kinase inhibitors as a means to probe the consequences of deactivating a particular kinase, but that the majority of commonly-used chemical probes are not sufficiently target-selective for robust interpretation of the observed phenotypes. We therefore developed a new computational approach for rapidly and accurately building 3D structural models of individual inhibitor/kinase complexes. We are using this as the basis for building a wholly new class of deep learning approaches that make use of 3D structural features. Broadly speaking, these models fall into two classes: the first entail models for predicting the binding affinity of individual inhibitor/kinase pairs, while the second are “generative” models that build very large computational libraries of novel chemical matter. Together, these two deep learning tools will allow us to compile huge in silico compound collections, and assign each compound’s selectivity. We envision that this project will deliver new chemical probes for several hitherto unaddressed (“orphan”) kinases, to serve as chemical tools and as starting point for drug development.

    In our second key project, we recognize that immune checkpoint inhibitors (ICIs) have completely transformed the landscape of cancer therapeutics over the past 10 years. These antibody-based drugs unleash the human immune system to aggressively attack tumors, and can be amazingly effective: even curative in some cases. However, they also induce a variety of immune-related adverse events, which can be so severe that they preclude many patients from even receiving a full course of therapy. Importantly, these adverse events occur because of the ICIs’ effects on the immune system away from the tumor; in contrast, the therapeutic benefits arise from ICIs’ activities at (or near) the site of the tumor. In light of this, we envision a class of antibodies that are selectively activated only in the vicinity of the tumor: these would be expected to confer the same therapeutic benefits as existing ICIs, but without their associated toxicities. We recently developed a method by which antibodies can be deactivated by mutation, and then their activity rescued by addition of a specific drug-like small molecule. In essence, this provides a means for controlling antibody activity through external stimuli. We have recently demonstrated that we can control activity of multiple clinically-relevant ICIs in this manner; moving forward, we are testing this strategy using in vivo models, and at the same time we seek to install enhanced chemical biology control into these designs. We expect that this project will deliver an entirely new means for targeting ICIs in a tumor-selective manner; this will represent a key tool for ongoing immuno-oncology research, and also a valuable starting point for drug development.

    In our third and final key project, we were intrigued to learn that certain tumor suppressor proteins – including p53 and pVHL – are deactivated by missense mutations at residues not directly responsible for function. Instead, some of the most frequent cancer-driving mutations to these two tumor suppressors reduce the protein’s thermodynamic stability, such that cellular activity is diminished because the tumor suppressor protein is no longer correctly folded. Driven by unique computational approaches, we have identified a cryptic pocket on the surface of each of these two proteins, and identified several compounds intended to bind at this pocket. Because this pocket is remote from all of their common cancer-associated destabilizing mutations, we expect that compounds binding here will restore activity to many different destabilized p53/pVHL mutants. We have separately tested compounds for each target in a variety of preliminary biophysical and cell-based assays, and in both cases we find results consistent with re-activation of the mutant tumor suppressor. Moving forward, in both cases we are carrying out further cellular studies to fully define the effect of re-awaking a dormant tumor suppressor, and optimizing the compounds’ potency in preparation for in vivo studies. Through the combination of more potent compounds and mechanistic insights, we anticipate that these studies may serve as a starting point for developing new cancer therapeutics. We further expect that the learnings from addressing these two tumor suppressor proteins will prove valuable in adapting this overarching strategy for other tumor suppressors that are deactivated via thermodynamically-destabilizing mutations.

    Selected Publications

    Andrianov G.V., Gabriel Ong W.J., Serebriiskii I.,Karanicolas J., Efficient hit-to-lead searching of kinase inhibitor chemical space via computational fragment merging. J Chem Inf Model. 61(12): 5967-5987, 2021. PMC8865965.

    Bai N, Yates M, Andrianov G, Miller S, Kirubakaran P, Karanicolas J. Rationalizing PROTAC-mediated ternary complex formation using Rosetta. bioRxiv,

    Kirubakaran P*, Morton G*, Zhang P*, Zhang H, Gordon J, Abou-Gharbia M, Issa JPJ, Wu J, Childers W†, Karanicolas J†. Comparative modeling of CDK9 inhibitors to explore selectivity and structure-activity relationships. bioRxiv,

    Adeshina YO, Deeds EJ, Karanicolas J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc. Natl. Acad. USA, 117, p. 18477-18488 (2020).

    Khowsathit J, Bazzoli A, Cheng H, Karanicolas J. Computational design of an allosteric antibody switch by deletion and rescue of a complex structural constellation. ACS Cent. Sci. 6, p. 390-403 (2020).

    Zhang H, Pandey S, Travers M, Khowsathit J, Morton G, Sun H, Perez-Leal O, Barrero CA, Merali C, Okamoto Y, Sato T, Pan J, Garriga J, Bhanu NV, Simithy J, Patel B, Madzo J, Chung W, Raynal NJ-M, Garcia BA, Jacobson MA, Kadoch C, Merali S, Zhang Y, Childers W, Abou-Gharbia M, Karanicolas J, Baylin SB, Zahnow CA, Jelinek J, Graña X, Issa J-PJ. Targeting CDK9 reactivates epigenetically silenced genes in cancer. Cell 175, p. 1244-1258 (2018).

    Malhotra S, Karanicolas J. When does chemical elaboration induce a ligand to change its binding mode? J. Med. Chem. 60, p. 128-45 (2017).

    Gowthaman R*, Miller SA*, Rogers S, Khowsathit J, Lan L, Bai N, Johnson DK, Liu C, Xu L, Anbanandam A, Aubé J, Roy A, Karanicolas J. DARC: mapping surface topography by ray-casting for effective virtual screening at protein interaction sites. J. Med. Chem. 59, p. 4152‑70 (2016).

    Johnson DK, Karanicolas J. Ultra-high-throughput structure-based virtual screening for small-molecule inhibitors of protein-protein interactions. J. Chem. Inf. Model., 56, p. 399-411 (2016).

    Additional Publications

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    This Fox Chase professor participates in the Undergraduate Summer Research Fellowship
    Learn more about Research Volunteering.