Computational approaches to investigating allostery
- Emanuele Paci (University of Leeds, United Kingdom)
- Shoshana Wodak (VIB, Brussels and University of Toronto, Belgium)
- Nikolay Dokholyan (University of North Carolina, Chapel Hill, USA)
Allosteric regulation plays a key role in many biological processes such as signal transduction, transcriptional regulation, and many more. It is rooted in fundamental thermodynamic and dynamic properties of macromolecular systems that are still poorly understood. These properties are moreover modulated by the cellular context in both health and disease.
Computational approaches have all along played a very important role in the investigation of allosteric mechanisms. They have provided insights into some of the underpinnings of allostery, and have recently shown great promise in various practical applications, such as engineering regulatory modules in proteins and identifying allosteric binding sites that can be targeted by specific drugs. Notable examples of the latter application include re sensitizing resistant hepatitis C variants by a combination therapy that involves binding to the allosteric site of NS5A, allosteric inhibitors of HIV integrase, or the discovery of allosteric drugs that inhibit PARP-1 without hampering its action on cancer related DNA repair deficiencies.
On a more fundamental molecular level however, computational approaches have as yet not been able to yield information on the important kinetic and thermodynamic drivers of allostery, an area where they could ideally complemented experimental methods. But this may be changing with increased access to massively parallel computer architectures and the availability of more powerful methods for exploring the energy landscapes of complex systems.
One should also mention various recent bioinformatics approaches, which analyze sequence information (patterns of sequence conservation or correlated mutations) with the goal of uncovering signals of evolutionary pressure that may either inform or validate mechanistic aspects of allosteric processes. Here too the vast increase in available data on protein sequences from different organisms and massive data on human polymorphism derived from next generation sequencing efforts is providing unprecedented (and still largely untapped) opportunities in investigation the role of evolution in shaping allosteric regulation.
The planned CECAM workshop will bring together about 30 computational biophysicists, protein modelers and, bioinformaticians, as well as experimentalists. The main aim of the workshop is to critically discuss state of the art computational and experimental methods, and how they can be exploited to move the field forward on both the fundamental and applied levels. The workshop will be open to participants from Industry and the scientific community at large.
1. Monod J, Jacob F: Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harb Symp Quant Biol 1961, 26:389-401.
2. Monod J, Wyman J, Changeux JP: On the Nature of Allosteric Transitions: A Plausible Model. J Mol Biol 1965, 12:88-118.
3. Koshland DE, Jr., Nemethy G, Filmer D: Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochemistry 1966, 5:365-385.
4. Dokholyan NV: Controlling Allosteric Networks in Proteins. Chem Rev 2016.
5. Guo J, Zhou HX: Protein Allostery and Conformational Dynamics. Chem Rev 2016.
6. Sun JH, O'Boyle DR, 2nd, Fridell RA, Langley DR, Wang C, Roberts SB, Nower P, Johnson BM, Moulin F, Nophsker MJ, et al.: Resensitizing daclatasvir-resistant hepatitis C variants by allosteric modulation of NS5A. Nature 2015, 527:245-248.
7. Motlagh HN, Wrabl JO, Li J, Hilser VJ: The ensemble nature of allostery. Nature 2014, 508:331-339.
8. Hilser VJ, Wrabl JO, Motlagh HN: Structural and energetic basis of allostery. Annu Rev Biophys 2012, 41:585-609.
9. Pande VS, Beauchamp K, Bowman GR: Everything you wanted to know about Markov State Models but were afraid to ask. Methods 2010, 52:99-105.
10. Chodera JD, Noe F: Markov state models of biomolecular conformational dynamics. Curr Opin Struct Biol 2014, 25:135-144.
11. Pontiggia F, Pachov DV, Clarkson MW, Villali J, Hagan MF, Pande VS, Kern D: Free energy landscape of activation in a signalling protein at atomic resolution. Nat Commun 2015, 6:7284.
12. Malmstrom RD, Kornev AP, Taylor SS, Amaro RE: Allostery through the computational microscope: cAMP activation of a canonical signalling domain. Nat Commun 2015, 6:7588.
13. Schueler-Furman O, Wodak SJ. Computational approaches to investigating allostery. Curr Opin Struct Biol. 2016 Dec;41:159-171.