Efficient First-Principles Exploration on the Physical and Chemical Space of Peptides and Saccharides Enabled by Neural Network Potential
Jer-Lai Kuo
M3 Lab, Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, Taiwan
Mercredi 27 Novembre 2024, 11H00
Bibiothèque du LCT, Couloir 12-13, 4ème étage, Campus Pierre et Marie Curie
Sampling of the conformational space of peptides and saccharides with first-principles accuracy is critical as such a database provide a solid base to interpret experimental measurements such as Infrared photo-dissociation (IRPD) spectroscopy, ion mobility spectrometry (IMS), and/or collision-induced dissociation (CID). The conformational space of both peptides and saccharides are highly flexible, in which the distinct conformers of mono- and di-saccharide is estimated to be in the order of 103 and 106, respectively.1-3 To efficiently explore the diverse conformational space of saccharide without losing accuracy, we developed a multi-level sampling scheme integrating semi-empirical models, density function theory (DFT) and neural network potential (NNP). Preliminary studies on small-size peptides with different protonated sites have also been demonstrated.4 Solvation of these molecules can also be simulated with a computational approach that integrate fragment-based methods with NNP.5
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References :
[1] H. T. Phan, P.-K. Tsou, P.-J. Hsu and J.-L. Kuo, Phys. Chem. Chem. Phys. 2023, 25, 5817.
[2] P.-K. Tsou, H. T. Huynh, H. T. Phan and J.-L. Kuo, Phys. Chem. Chem. Phys. 2023, 25, 3332.
[3] H. T. Phan, P.-K. Tsou, P.-J. Hsu and J.-L. Kuo, Phys. Chem. Chem. Phys. 2024, 26, 9556.
[4] H.-C. Dong, P.-J. Hsu and J.-L. Kuo, Phys. Chem. Chem. Phys. 2024, 26,11125.
[5] S. Jindal, P.-J. Hsu, H. T. Phan, P.-K. Tsou and J.-L. Kuo, Phys. Chem. Chem. Phys. 2022, 24,27263.