From Quantum Mechanics, Classical Mechanics, and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases

Coskuner‐Weber, Orkid; Habiboglu, M. Gokhan; Teplow, David B.; Uversky, Vladimir N.

doi:10.1007/978-1-0716-1546-1_8

Methods in Molecular Biology

2022

DOI: 10.1007/978-1-0716-1546-1_8

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From Quantum Mechanics, Classical Mechanics, and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases

Orkid Coskuner‐Weber

M. Gokhan Habiboglu

David B. Teplow

et al.

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2024

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Cited by 2 publications

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Intrinsically Disordered Proteins by Homology Modeling and Replica Exchange Molecular Dynamics Simulations: A Case Study of Amyloid-β42

Coskuner Weber

2024

Journal of the Turkish Chemical Society Section A: Chemistry

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Homology modeling emerges as a potent tool unveiling the structural enigma of intrinsically disordered proteins (IDPs), with recent advancements such as AlphaFold2 enhancing the precision of these analyses. The process usually involves identifying homologous proteins with known structures and utilizing their templates to predict the three-dimensional architecture of the target IDP. However, IDPs lack a well-defined three-dimensional structure, and their flexibility makes it difficult to accurately predict their conformations. On the other hand, special sampling molecular dynamics simulations have been shown to be useful in defining the distinct structural properties of IDPs. Here, the structural properties of the disordered amyloid-β42 peptide were predicted using various homology modeling tools including C-I-TASSER, I-TASSER, Phyre2, SwissModel and AlphaFold2. In parallel, extensive replica exchange molecular dynamics simulations of Aβ42 were conducted. Results from homology modeling were compared to our replica exchange molecular dynamics simulations and experiments for gaining insights into the accuracy of homology modeling tools for IDPs used in this work. Based on our findings, none of the homology modeling tools used in this work can capture fully the structural properties of Aβ42. However, C-I-TASSER yields a radius of gyration and tertiary structure properties that are more in accord with the simulations and experimental data rather than I-TASSER, Phyre2, SwissModel and AlphaFold2.

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Intrinsically Disordered Proteins by Homology Modeling and Replica Exchange Molecular Dynamics Simulations: A Case Study of Amyloid-β42

Coskuner Weber

2024

Journal of the Turkish Chemical Society Section A: Chemistry

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Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

Coskuner-Weber,

Uversky

2024

CPPS

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Abstract:: The structural ensembles of intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) cannot be easily characterized using conventional experimental techniques. Computational techniques complement experiments and provide useful insights into the structural ensembles of IDPs and proteins with IDRs. Herein, we discuss computational techniques such as homology modeling, molecular dynamics simulations, machine learning with molecular dynamics, and quantum computing that can be applied to the studies of IDPs and hybrid proteins with IDRs. We also provide useful future perspectives for computational techniques that can be applied to IDPs and hybrid proteins containing ordered domains and IDRs.

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From Quantum Mechanics, Classical Mechanics, and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases

Cited by 2 publications

References 48 publications

Intrinsically Disordered Proteins by Homology Modeling and Replica Exchange Molecular Dynamics Simulations: A Case Study of Amyloid-β42

Intrinsically Disordered Proteins by Homology Modeling and Replica Exchange Molecular Dynamics Simulations: A Case Study of Amyloid-β42

Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

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