In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.
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http://dx.doi.org/10.1016/j.ijbiomac.2024.133813 | DOI Listing |
Nucleic Acids Res
January 2025
Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, United States.
The mammalian high mobility group protein AT-hook 2 (HMGA2) is a small DNA-binding protein that specifically targets AT-rich DNA sequences. Structurally, HMGA2 is an intrinsically disordered protein (IDP), comprising three positively charged 'AT-hooks' and a negatively charged C-terminus. HMGA2 can form homodimers through electrostatic interactions between its 'AT-hooks' and C-terminus.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, Faculty of Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
Tau is a microtubule (MT)-associated protein that binds to and stabilizes the MTs of neurons. Due to its intrinsically disordered nature, it undergoes several post-translational modifications (PTMs) that are intricately linked to both the physiological and pathophysiological roles of Tau. Prior research has shown phosphorylation and O-GlcNAcylation to have contrasting effects on Tau aggregation; however, the precise molecular mechanisms and potential synergistic effects of these modifications remain elusive.
View Article and Find Full Text PDFSoft Matter
January 2025
Computation-based Science and Technology Research Center, The Cyprus Institute, 2121 Nicosia, Cyprus.
This work presents an investigation of the influence of poly(-isopropylacrylamide) (PNIPAM) polymer on the structural dynamics of intrinsically disordered alpha-synuclein (α-syn) protein, exploring the formation and intricate features of the resulting α-syn/PNIPAM complexes. Using atomistic molecular dynamics (MD) simulations, our study analyzes the impact of initial configuration, polymer molecular weight, and protein mutations on the α-syn and the α-syn/PNIPAM complex. Atomistic simulations, of a few μs, of the protein/polymer complex reveal crucial insights into molecular interactions within the complex, emphasizing a delicate balance of forces governing its stability and structural evolution.
View Article and Find Full Text PDFACS Phys Chem Au
January 2025
Department of Chemistry, West Virginia University, Morgantown, West Virginia 26506, United States.
In-droplet hydrogen/deuterium exchange (HDX)-mass spectrometry (MS) experiments have been conducted for peptides of highly varied conformational type. A new model is presented that combines the use of protection factors (PF) from molecular dynamics (MD) simulations with intrinsic HDX rates ( ) to obtain a structure-to-reactivity calibration curve. Using the model, the relationship of peptide structural flexibility and HDX reactivity for different peptides is elucidated.
View Article and Find Full Text PDFCell Rep Phys Sci
November 2024
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Graph neural networks (GNNs) have emerged as powerful tools for representation learning. Their efficacy depends on their having an optimal underlying graph. In many cases, the most relevant information comes from specific subgraphs.
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