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Protein function does not solely depend on structure but often relies on dynamical transitions between distinct conformations. Despite this fact, our ability to characterize or predict protein dynamics is substantially less developed compared to state-of-the-art protein structure prediction. Molecular simulations provide unique opportunities to study protein dynamics, but the timescales associated with conformational changes generate substantial challenges. Enhanced sampling algorithms with collective variables can greatly reduce the computational cost of sampling slow processes. However, defining collective variables suitable to enhance sampling of protein conformational transitions is non-trivial. Low-frequency vibrations have long been considered as promising candidates for collective variable but their identification so far relied on assumptions inherently invalid at low frequencies. We recently introduced an analysis of molecular vibrations that does not rely on such approximations and remains accurate at low frequencies. Here, we modified this approach to efficiently isolate low-frequency vibrations in proteins and applied it to a set of five proteins of varying complexity. We demonstrate that our approach is not only highly reproducible but results in collective variables that consistently enhance sampling of protein conformational transitions and associated free energy surfaces on timescales compatible with high throughput applications. This enables the efficient generation of protein conformational ensembles, which will be key for future prediction algorithms aiming beyond static protein structures.

We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on timescales faster than conformational transitions but describe a path of least resistance towards structural change. A key challenge is that harmonic approximations are ill-suited to characterize these vibrations, which are observed at far-infrared frequencies and are easily excited by thermal collisions at room temperature. Here, we approached this problem with a frequency-selective anharmonic (FRESEAN) mode analysis that does not rely on harmonic approximations and successfully isolates anharmonic low-frequency vibrations from short molecular dynamics simulation trajectories. We applied FRESEAN mode analysis to simulations of alanine dipeptide, a common test system for enhanced sampling simulation protocols, and compare the performance of isolated low-frequency vibrations to conventional user-defined collective variables (here backbone dihedral angles) in enhanced sampling simulations. The comparison shows that enhanced sampling along anharmonic low-frequency vibrations not only reproduces known conformational dynamics but can even further improve sampling of slow transitions compared to user-defined collective variables. Notably, free energy surfaces spanned by low-frequency anharmonic vibrational modes exhibit lower barriers associated with conformational transitions relative to representations in backbone dihedral space. We thus conclude that anharmonic low-frequency vibrations provide a promising path for highly effective and fully automated enhanced sampling simulations of conformational dynamics in biomolecules.

Oral squamous cell carcinomas (OSCC) are the 6th most common cancer and the diagnosis is often belated for a curative treatment. The reliable and early differentiation between healthy and diseased cells is the main aim of this study in order to improve the quality of the treatment and to understand tumour pathogenesis. Here, the optical stretcher is used to analyse mechanical properties of cells and their potential to serve as a marker for malignancy. Stretching experiments revealed for the first time that cells of primary OSCCs were deformed by 2.9 % rendering them softer than cells of healthy mucosa which were deformed only by 1.9 %. Furthermore, the relaxation behaviour of the cells revealed that these malignant cells exhibit a faster contraction than their benign counterparts. This suggests that deformability as well as relaxation behaviour can be used as distinct parameters to evaluate emerging differences between these benign and malignant cells. Since many studies in cancer research are performed with cancer cell lines rather than primary cells, we have compared the deformability and relaxation of both types, showing that long time culturing leads to softening of cells. The higher degree of deformability and relaxation behaviour can enable cancer cells to traverse tissue emphasizing that changes in cell architecture may be a potential precondition for malignant transformation. Respecting the fact that even short culture times have an essential effect on the significance of the results, the use of primary cells for further research is recommended. The distinction between malignant and benign cells would enable an early confirmation of cancer diagnoses by testing cell samples of suspect oral lesions., Comment: 17 pages, 3 figures