Integrated computational approaches and tools for allosteric drug discovery:
- Amamuddy, Olivier S, Veldman, Wade, Manyumwa, Colleen, Khairallah, Afrah, Agajanian, Steve, Oluyemi, Odeyemi, Verkhivker, Gennady M, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Veldman, Wade , Manyumwa, Colleen , Khairallah, Afrah , Agajanian, Steve , Oluyemi, Odeyemi , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163012 , vital:41004 , https://doi.org/10.3390/ijms21030847
- Description: 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.
- Full Text:
- Date Issued: 2020
- Authors: Amamuddy, Olivier S , Veldman, Wade , Manyumwa, Colleen , Khairallah, Afrah , Agajanian, Steve , Oluyemi, Odeyemi , Verkhivker, Gennady M , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163012 , vital:41004 , https://doi.org/10.3390/ijms21030847
- Description: 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.
- Full Text:
- Date Issued: 2020
Understanding the Pyrimethamine drug resistance mechanism via combined molecular dynamics and dynamic residue network analysis:
- Amusengeri, Arnold, Tata, Rolland B, Tastan Bishop, Özlem
- Authors: Amusengeri, Arnold , Tata, Rolland B , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163022 , vital:41005 , https://doi.org/10.3390/molecules25040904
- Description: In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine’s stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity.
- Full Text:
- Date Issued: 2020
- Authors: Amusengeri, Arnold , Tata, Rolland B , Tastan Bishop, Özlem
- Date: 2020
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163022 , vital:41005 , https://doi.org/10.3390/molecules25040904
- Description: In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine’s stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity.
- Full Text:
- Date Issued: 2020
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