arXiv subjects

Sena Aydin

Publications and source records attributed to Sena Aydin.

5 recordsLinked to original sources

arXiv2026

Site-Specific Vibrational Dynamics to Probe Local and Global Protein Motions

Vibrational spectroscopic probes can provide site-specific information on protein structure and dynamics. In this work, the possibility to relate protein motion with the vibrational response for --SCN, --N$_3$, and --SNO labels covalently linked to all alanine-residues in lysozyme is investigated. Depending on the position of the probe along the polypeptide chain, its local hydration, and the dynamics of its environment, the vibrational response can encode not only local fluctuations but also low-frequency, collective motions of the protein. The spectroscopic probes are described using machine-learning-based models for both bonded interactions, represented by reproducing-kernel models, and electrostatic interactions, represented by fluctuating minimally distributed charges. Spectroscopic dynamics are characterized through frequency fluctuation correlation functions (FFCFs). For many probe locations, the FFCFs contain a non-decaying component on the simulation time scale, indicating residual slow dynamics and incomplete sampling of the underlying conformational fluctuations. The magnitude of these static contributions is consistent with previous experimental observations, providing qualitative validation of the simulations. Overall, the results demonstrate that site-specific vibrational probes can report on dynamics extending beyond their immediate local environment and can, at suitable locations, provide information on collective protein motions.

physics.chem-ph↗

arXiv2025

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces

Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in Machine Learning (ML) methodologies have led to the development of Machine Learned Potential Energy Surfaces (ML-PES) which are now widely used to simulate such systems. The present work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on practical and recurrent issues to conceive and use such model. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs.

physics.chem-ph↗

arXiv2025

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

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.

physics.chem-ph↗

arXiv2025

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations

Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems.

physics.chem-ph↗

arXiv2024

SCN as a Local Probe of Protein Structural Dynamics

The dynamics of lysozyme is probed by attaching -SCN to all alanine-residues. The 1-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption by 4 cm$^{-1}$ which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution $^{12}$C $\rightarrow ^{13}$C leads to a red-shift by $-47$ cm$^{-1}$ which is consistent with experiments with results on CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contain label-specific information in the difference spectra when compared with the wild type protein. Depending on the positioning of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the $α-$helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labelled Alanine residues as a function of time can be related to angular reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency (CN-stretch) and far-infrared part of the spectrum.

physics.chem-ph↗