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Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa
Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA
Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(3), 847;
Received: 25 December 2019 / Revised: 20 January 2020 / Accepted: 21 January 2020 / Published: 28 January 2020
(This article belongs to the Collection Feature Papers in Molecular Biophysics)
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. View Full-Text
Keywords: Allostery; allosteric modulators; network analysis; MD-TASK; drug resistance; precision medicine Allostery; allosteric modulators; network analysis; MD-TASK; drug resistance; precision medicine
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MDPI and ACS Style

Sheik Amamuddy, O.; Veldman, W.; Manyumwa, C.; Khairallah, A.; Agajanian, S.; Oluyemi, O.; Verkhivker, G.M.; Tastan Bishop, Ö. Integrated Computational Approaches and Tools for Allosteric Drug Discovery. Int. J. Mol. Sci. 2020, 21, 847.

AMA Style

Sheik Amamuddy O, Veldman W, Manyumwa C, Khairallah A, Agajanian S, Oluyemi O, Verkhivker GM, Tastan Bishop Ö. Integrated Computational Approaches and Tools for Allosteric Drug Discovery. International Journal of Molecular Sciences. 2020; 21(3):847.

Chicago/Turabian Style

Sheik Amamuddy, Olivier, Wayde Veldman, Colleen Manyumwa, Afrah Khairallah, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker, and Özlem Tastan Bishop. 2020. "Integrated Computational Approaches and Tools for Allosteric Drug Discovery" International Journal of Molecular Sciences 21, no. 3: 847.

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