Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen + 764 word(s) 764 2020-02-27 11:22:10 |
2 format correct Meta information modification 764 2020-03-05 09:07:32 | |
3 format correct + 9 word(s) 773 2020-11-01 09:43:03 | |
4 format correct + 14 word(s) 787 2020-12-23 07:40:09 | |
5 format correct Meta information modification 787 2021-09-26 10:05:50 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Sheik Amamuddy, O.; Veldman, W.; Manyumwa, C.; Khairallah, A.; Agajanian, S.; Oluyemi, O.; Verkhivker, G.M.; Tastan Bishop, �. Allosteric Drug Discovery. Encyclopedia. Available online: (accessed on 25 February 2024).
Sheik Amamuddy O, Veldman W, Manyumwa C, Khairallah A, Agajanian S, Oluyemi O, et al. Allosteric Drug Discovery. Encyclopedia. Available at: Accessed February 25, 2024.
Sheik Amamuddy, Olivier, Wayde Veldman, Colleen Manyumwa, Afrah Khairallah, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker, Özlem Tastan Bishop. "Allosteric Drug Discovery" Encyclopedia, (accessed February 25, 2024).
Sheik Amamuddy, O., Veldman, W., Manyumwa, C., Khairallah, A., Agajanian, S., Oluyemi, O., Verkhivker, G.M., & Tastan Bishop, �. (2020, March 05). Allosteric Drug Discovery. In Encyclopedia.
Sheik Amamuddy, Olivier, et al. "Allosteric Drug Discovery." Encyclopedia. Web. 05 March, 2020.
Allosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

Allostery allosteric modulators network analysis MD-TASK drug resistance precision medicine

1. Introduction

Allosteric regulation is often a mechanism of choice for proteins and biomolecular assemblies to operate in complex signalling cascades and to modulate their activity levels, adapting to binding partners in the cellular environment during signal transduction, catalysis, and gene regulation [[1][2][3][4][5]]. The advances in X-ray crystallography, Nuclear Magnetic Resonance (NMR), and biophysical techniques have enabled numerous detailed investigations of large protein systems and conformational dynamic processes at atomic resolution [[6][7][8][9][10][11][12][13][14][15][16][17][18][19]]. These developments have facilitated the integration of computational and experimental studies of allosteric regulation, eventually leading to new conceptual outlooks and attempts to develop a unified theory of this allosteric phenomenon. The thermodynamics-based conformational selection model of allosteric regulation has been particularly fruitful in explaining a wide range of experiments by assuming that a statistical ensemble of preexisting conformational states and communication pathways is inherent to any protein system and can be modulated through allosteric ligand perturbations [[20][21][22][23][24][25][26]]. While great leaps have been made in the field of molecular modelling, NMR spectroscopy, and X-ray crystallography, it should be noted that no single method can provide allostery information for all cases due to the complexity and incomplete understanding of allosteric phenomena.

2. Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Understanding molecular mechanisms of allosteric regulation in proteins has attracted considerable attention in both academia and industry owing to the importance of discovering allosteric modulators of therapeutically important targets [[27]]. These efforts are motivated by fundamental differences in structural and evolutionary diversity between active and allosteric sites even among structurally similar proteins of the same family. While active sites for structurally related proteins and protein families are often highly conserved and present a formidable challenge for design of selective modulators, allosteric binding is typically more dynamic and structurally and evolutionarily diverse, thereby often alleviating conceptual difficulties in the design of target-specific therapies and addressing lingering problems of toxicity and side effects [[28]]. Another important incentive for the development of allosteric drugs is that, while traditional orthosteric drugs usually inhibit protein activity, allosteric modulators may not only inhibit but also increase protein activity (allosteric activators) [[29]]. In the last decade, drug discovery has been shifting its focus toward targeting allosteric sites in order to improve compound selectivity [[28][29][30][31][32][33]]. Allosteric drugs also feature distinct physicochemical properties, adding further freedom for discovery of novel active compounds, and can often be combined with orthosteric drugs into synergistic drug cocktails to modulate and improve enzyme activities, specificity, and pharmacological profiles.

While orthostery-based therapies have enhanced the quality of life for patients, they have brought forth many daunting challenges for which allostery may provide new solutions. Drug discovery against more diverse protein targets can result in less toxic and more specific therapies. The incorporation of dynamic and network analysis tools has proven their effectiveness in drug discovery studies of several target proteins [[32][33][34][35]] and offer a promising direction for the analysis of large datasets [[36]]. With the maturation of open-source projects, the availability of cheaper computation, and large datasets, in silico simulations are a very attractive venture for early-stage drug discovery as they offer cost-effective drug development. The integration of such approaches into robust, reproducible, and customizable workflows should make in silico allosteric drug discovery more efficient and reliable. In this review article, we discuss how the integration of state-of-the-art structural, dynamic, and network-based approaches for simulation of ligand–protein binding can provide a comprehensive methodological framework for advancing computer-aided discovery of allosteric sites and allosteric modulators of protein functions and mechanisms.


  1. Jacque Monod; Jeffries Wyman; Jean-Pierre Changeux; On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology 1965, 12, 88-118, 10.1016/s0022-2836(65)80285-6.
  2. D. E. Koshland; G. Nemethy; D. Filmer; Comparison of Experimental Binding Data and Theoretical Models in Proteins Containing Subunits*. Biochemistry 1966, 5, 365-385, 10.1021/bi00865a047.
  3. Jean-Pierre Changeux; Allostery and the Monod-Wyman-Changeux Model After 50 Years. Annual Review of Biophysics 2012, 41, 103-133, 10.1146/annurev-biophys-050511-102222.
  4. Jean-Pierre Changeux; Stuart J. Edelstein; Allosteric receptors after 30 years. Rendiconti Lincei. Scienze Fisiche e Naturali 2006, 17, 59-96, 10.1007/bf02904502.
  5. Alexandr P. Kornev; Susan S. Taylor; Dynamics-Driven Allostery in Protein Kinases.. Trends in Biochemical Sciences 2015, 40, 628-647, 10.1016/j.tibs.2015.09.002.
  6. David D. Boehr; H. Jane Dyson; Peter E. Wright; An NMR Perspective on Enzyme Dynamics. Chemical Reviews 2006, 106, 3055-3079, 10.1021/cr050312q.
  7. Virginia A. Jarymowycz; Martin J. Stone; Fast Time Scale Dynamics of Protein Backbones: NMR Relaxation Methods, Applications, and Functional Consequences. Chemical Reviews 2006, 106, 1624-1671, 10.1021/cr040421p.
  8. Anthony K. Mittermaier; New Tools Provide New Insights in NMR Studies of Protein Dynamics. Science 2006, 312, 224-228, 10.1126/science.1124964.
  9. Remco Sprangers; Algirdas Velyvis; Lewis E Kay; Solution NMR of supramolecular complexes: providing new insights into function. Nature Methods 2007, 4, 697-703, 10.1038/nmeth1080.
  10. Anthony K. Mittermaier; Lewis E Kay; Observing biological dynamics at atomic resolution using NMR. Trends in Biochemical Sciences 2009, 34, 601-611, 10.1016/j.tibs.2009.07.004.
  11. Lewis E Kay; NMR studies of protein structure and dynamics - a look backwards and forwards.. Journal of Magnetic Resonance 2011, 213, 492-4, 10.1016/j.jmr.2011.08.010.
  12. Dmitry M. Korzhnev; Lewis E Kay; Probing Invisible, Low-Populated States of Protein Molecules by Relaxation Dispersion NMR Spectroscopy: An Application to Protein Folding. Accounts of Chemical Research 2008, 41, 442-451, 10.1021/ar700189y.
  13. Charalampos G. Kalodimos; NMR reveals novel mechanisms of protein activity regulation. Protein Science 2011, 20, 773-782, 10.1002/pro.614.
  14. Rina Rosenzweig; Lewis E Kay; Bringing Dynamic Molecular Machines into Focus by Methyl-TROSY NMR. Annual Review of Biochemistry 2014, 83, 291-315, 10.1146/annurev-biochem-060713-035829.
  15. Lewis E. Kay; New Views of Functionally Dynamic Proteins by Solution NMR Spectroscopy. Journal of Molecular Biology 2016, 428, 323-331, 10.1016/j.jmb.2015.11.028.
  16. George P. Lisi; J. Patrick Loria; Solution NMR Spectroscopy for the Study of Enzyme Allostery. Chemical Reviews 2016, 116, 6323-6369, 10.1021/acs.chemrev.5b00541.
  17. Chengdong Huang; Charalampos G. Kalodimos; Structures of Large Protein Complexes Determined by Nuclear Magnetic Resonance Spectroscopy. Annual Review of Biophysics 2017, 46, 317-336, 10.1146/annurev-biophys-070816-033701.
  18. Ronald A. Venters; Richele Thompson; John Cavanagh; Current approaches for the study of large proteins by NMR. Journal of Molecular Structure 2002, 602, 275-292, 10.1016/s0022-2860(01)00690-1.
  19. Celia J. Webby; Wanting Jiao; Richard D. Hutton; Nicola J. Blackmore; Heather M. Baker; Edward N. Baker; Geoffrey Jameson; Emily Parker; Synergistic Allostery, a Sophisticated Regulatory Network for the Control of Aromatic Amino Acid Biosynthesis in Mycobacterium tuberculosis*. Journal of Biological Chemistry 2010, 285, 30567-30576, 10.1074/jbc.M110.111856.
  20. Gunasekaran, K.; Ma, B.; Nussinov, R. Is allostery an intrinsic property of all dynamic proteins? Proteins: Struct. Funct. Genet. 2004, 57, 433–443. [Google Scholar] [CrossRef]
  21. Jin Liu; Mingzhen Zhang; Allosteric effects in the marginally stable von Hippel–Lindau tumor suppressor protein and allostery-based rescue mutant design. Proceedings of the National Academy of Sciences 2008, 105, 901-906, 10.1073/pnas.0707401105.
  22. Chung-Jung Tsai; Antonio Del Sol; Mingzhen Zhang; Allostery: absence of a change in shape does not imply that allostery is not at play.. Journal of Molecular Biology 2008, 378, 1-11, 10.1016/j.jmb.2008.02.034.
  23. Chung-Jung Tsai; Antonio Del Sol; Mingzhen Zhang; Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms. Molecular BioSystems 2009, 5, 207-216, 10.1039/b819720b.
  24. Antonio Del Sol; Chung-Jung Tsai; Buyong Ma; Ruth Nussinov; The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways. Structure 2009, 17, 1042-50, 10.1016/j.str.2009.06.008.
  25. Pavel Zhuravlev; Garegin A. Papoian; Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework. Quarterly Reviews of Biophysics 2010, 43, 295-332, 10.1017/s0033583510000119.
  26. Kristin Blacklock; Gennady M. Verkhivker; Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications. PLOS Computational Biology 2014, 10, e1003679, 10.1371/journal.pcbi.1003679.
  27. Mingzhen Zhang; Chung-Jung Tsai; Allostery in Disease and in Drug Discovery. Cell 2013, 153, 293-305, 10.1016/j.cell.2013.03.034.
  28. Nussinov, R.; Tsai, C.J.; Csermely, P.; Allo-network drugs: Harnessing allostery in cellular networks. Trends Pharmacol. Sci. , 2011., 0.
  29. David L. Penkler; Ozlem Tastan Bishop; Modulation of Human Hsp90α Conformational Dynamics by Allosteric Ligand Interaction at the C-Terminal Domain. Scientific Reports 2019, 9, 1600, 10.1038/s41598-018-35835-0.
  30. André Fischer; Martin Smieško; Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity. International Journal of Molecular Sciences 2020, 21, 534, 10.3390/ijms21020534.
  31. Andras Szilagyi; Ruth Nussinov; Péter Csermely; Allo-network drugs: extension of the allosteric drug concept to protein- protein interaction and signaling networks.. Current Topics in Medicinal Chemistry 2013, 13, 64-77, 10.2174/1568026611313010007.
  32. Arnold Amusengeri; Ozlem Tastan Bishop; Discorhabdin N, a South African Natural Compound, for Hsp72 and Hsc70 Allosteric Modulation: Combined Study of Molecular Modeling and Dynamic Residue Network Analysis. Molecules 2019, 24, 188, 10.3390/molecules24010188.
  33. Arnold Amusengeri; Lindy Astl; Kevin Lobb; Gennady M. Verkhivker; Ozlem Tastan Bishop; Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study of Plasmodium falciparum Hsp70s.. International Journal of Molecular Sciences 2019, 20, 5574, 10.3390/ijms20225574.
  34. Gennady M. Verkhivker; Dynamics-based community analysis and perturbation response scanning of allosteric interaction networks in the TRAP1 chaperone structures dissect molecular linkage between conformational asymmetry and sequential ATP hydrolysis. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 2018, 1866, 899-912, 10.1016/j.bbapap.2018.04.008.
  35. David L Penkler; Canan Atilgan; Ozlem Tastan Bishop; Allosteric Modulation of Conformational Dynamics in Human Hsp90α: A Computational Study. null 2017, null, 198341, 10.1101/198341.
  36. Zhongjie Liang; Gennady M Verkhivker; Guang Hu; Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications.. Briefings in Bioinformatics 2019, NO, NO, 10.1093/bib/bbz029.
Subjects: Biophysics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , , , , , ,
View Times: 781
Revisions: 5 times (View History)
Update Date: 26 Sep 2021