Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917050183581696 |
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| author | Aydin, Sena Andreichev, Valerii Maragkoudakis, Pantelis Meuwly, Markus |
| author_facet | Aydin, Sena Andreichev, Valerii Maragkoudakis, Pantelis Meuwly, Markus |
| contents | Molecular dynamics simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm-1 which compares with 20 cm-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both, the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_26145 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions Aydin, Sena Andreichev, Valerii Maragkoudakis, Pantelis Meuwly, Markus Chemical Physics Biological Physics Molecular dynamics simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm-1 which compares with 20 cm-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both, the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments. |
| title | Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions |
| topic | Chemical Physics Biological Physics |
| url | https://arxiv.org/abs/2510.26145 |