Multiscale simulations of allosteric regulatory mechanisms in cancer-associated proteins and signaling protein networks

October 15, 2018 to October 17, 2018
Location : CECAM-Lugano, Lugano, Switzerland
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  • Francesco Luigi Gervasio (University College London, United Kingdom)
  • Gennady Verkhivker (Chapman University, USA)




   Frontiers in Molecular Biosciences


The phenomenon of allosteric communication is fundamental to many biological processes including the regulation of signal transduction networks [1,2]. Theoretical and computational studies of allostery in biomolecular systems have witnessed a recent renaissance. Sequence-based approaches have unveiled that protein allostery may be mediated by coupled motions of evolutionary networks of functional residues [3]. Network-based structural studies have also demonstrated that allosteric pathways may be formed through interactions of evolutionary conserved residues that are energetically coupled to mediate long-range communication. Elastic network models (ENM) and normal mode analysis (NMA) approaches, have provided a generalized formalism of allosteric communication in proteins[4,5] and have identified that conserved low-frequency modes of collective motions are robust to sequence variations and capable of transmitting molecular signals over long distances[6]. More recently, fully atomistic MD simulations, complemented by enhanced-sampling algorithms have provided further insight into the details of allosteric regulation and dysregulation of signaling proteins [7,8]. Allosteric communication mechanisms can range from a sequential model, where binding of a molecule at one site causes a sequential propagation of conformational changes across the protein, to a fully cooperative model, where structural changes are tightly coupled. An intermediate, “block-based” model was proposed, where sparse clusters of closely interacting residues can maintain a weak association to other blocks of residues and thus pass information between more distance regions of a protein [9].
The allosteric regulation of protein kinases is of particular interest, due to their fundamental role in signal transduction networks and in cancer.[10] Dysregulation of protein kinase activation by pathogenic mutations is involved in the initiation and propagation of most cancers.
The cancer-causing mutations can directly affect their allosteric activation, affect protein-protein interactions or their folding. The molecular mechanisms of cancer-causing mutations have been studied by atomistic MD with Metadynamics-based algorithms[10] The role of mutations in mis-folding and activation is particularly difficult to study by experiments and computational models due to the involvement of chaperones [11]. Hsp90-Cdc37 chaperones are recruited to misfolded or partially unfolded proteins and helps their folding. It is now clear that allosteric interactions of the Hsp90 with co-chaperones and protein kinase clients can determine regulatory mechanisms and cellular functions of many signaling proteins and cascades.[11]
Still, despite recent progress in computational and experimental studies of protein kinase structure and function, the molecular mechanism and dynamics of allosteric kinase activation and its regulation by protein-protein interactions and chaperones and deregulation by mutations remain mostly qualitative.

An important goal and central focus of the Symposium is to bring together computational and experimental experts in the field of allosteric regulation of signaling proteins and have an open and productive exchange and discussion about new computational developments and current status in the field. A significant novelty of the proposed workshop is investigating the role of misfolding and chaperones in regulating signaling protein activation.

Scientific Objectives
- Report on the state-of-the-art of research in the field of signaling protein allosteric regulation, focusing on major recent advances both in theoretical and computational techniques.
- Show successful cases in which the combination of experiments and theory provided an added insight to either of the two approaches.
- Discuss the importance of allosteric regulation of kinases and other cancer-related proteins in drug-discovery and report successful case studies.
- Promote discussion on open issues both in simulations and experiments of allosteric regulation and chaperone-mediated folding.
- Promote transfer of knowledge from computer simulation experts to experimentalists and vice-versa.
- Promote collaborations between academia and industry across different fields and among top groups in Europe and in the rest of the world.

We are planning to cover a number of topics including:
a) Theoretical models and multiscale simulations of allosteric regulation and interactions in protein kinases and molecular chaperones;
b) Combining biophysical studies and multiscale simulations of allosteric regulation in signaling networks;
c) Therapeutic applications of allosteric mechanisms of protein kinases and chaperones.
d) Integration of multiscale simulations with big data analysis and machine learning.

The proposed workshop will foster progress beyond the current state of the art in understanding of the molecular mechanisms underlying allosteric regulation of cellular signaling networks and molecular chaperones.
A deeper understanding of these molecular mechanisms is pivotal to the discovery of novel and more effective anti-cancer agents and in targeting cancer-causing genes in a personalized manner. In the long term the novel collaborations, simulation platforms and theoretical knowledge will lead to more effective and less toxic therapies for cancer and other complex diseases.



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2. Tsai et al. JMB 378:1, 2008.
3. Lockless & Ranganathan Science  286:295,1999.
4. Yang, et al. PNAS 106:12347, 2009.
5. Chennubhotla & Bahar Mol Syst Biol 2:36, 2006.
6. Blacklock & Verkhivker PLoS Comp Biol. 10: e1003679,2014.
7. J. O. Schulze et al. Cell Cheml Biol 23,1193,2016.
8. E. Papaleo, et al. Chemical Reviews, 116:6391,2016.
9. Kidd, Baker, Thomas  PLoS Comput  Biol 5:e1000484,2009.
10. Sutto & Gervasio PNAS, 110:10616, 2013.
11. Blacklock & Verkhivker PLoS One. 8: e71936, 2013.