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
Computational analysis of missense mutations from the human Macrophage Migration Inhibitory Factor (MIF) protein by Molecular Dynamics Simulations and Dynamic Residue Network Analysis:
- Kimuda, Phillip M, Brown, David K, Amamuddy, Olivier S, Ross, Caroline J, Matovu, Enock, Tastan Bishop, Özlem
- Authors: Kimuda, Phillip M , Brown, David K , Amamuddy, Olivier S , Ross, Caroline J , Matovu, Enock , Tastan Bishop, Özlem
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163238 , vital:41021 , https://doi.org/10.21955/aasopenres.1115054.1
- Description: Missense mutations are changes in the DNA that result in a change in the amino acid sequence. Depending on their location within the protein they can have a negative impact on how the protein functions. This is especially important for proteins involved in the body’s response to infection and diseases. Macrophage migration inhibitory factor (MIF) is one such protein that functions to recruit white blood cells to sites of inflammation.
- Full Text:
- Date Issued: 2019
- Authors: Kimuda, Phillip M , Brown, David K , Amamuddy, Olivier S , Ross, Caroline J , Matovu, Enock , Tastan Bishop, Özlem
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/163238 , vital:41021 , https://doi.org/10.21955/aasopenres.1115054.1
- Description: Missense mutations are changes in the DNA that result in a change in the amino acid sequence. Depending on their location within the protein they can have a negative impact on how the protein functions. This is especially important for proteins involved in the body’s response to infection and diseases. Macrophage migration inhibitory factor (MIF) is one such protein that functions to recruit white blood cells to sites of inflammation.
- Full Text:
- Date Issued: 2019
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