arXiv subjects

Michael A. Sauer

Publications and source records attributed to Michael A. Sauer.

4 recordsLinked to original sources

arXiv2026

From Enhanced Sampling to Human-Readable Representations of Protein Dynamics

Understanding protein conformational dynamics is essential for elucidating biological function but remains challenging due to the wide range of timescales and the complexity of collective motions. Enhanced sampling methods overcome timescale limitations of conventional molecular dynamics, yet their effectiveness depends on the choice of collective variables (CVs), which are often difficult to define and may lack physical interpretability. In particular, collective variables derived from machine learning or collective vibrational modes can efficiently capture slow dynamics but are not easily mapped onto intuitive structural descriptors. Here, we present a fully automated framework that transforms enhanced sampling trajectories into human-readable representations of protein dynamics. Our approach combines enhanced sampling along CVs derived from frequency-selective anharmonic mode analysis with a post hoc analysis of biased trajectories using weighted dynamic cross-correlation matrices. From these, we identify residue pairs and domains exhibiting correlated and anti-correlated motions, yielding simple domain-domain distances that serve as physically interpretable CVs. We apply this method to five proteins, including KRAS and HIV-1 protease, and show that it consistently identifies biologically relevant domains and motions without prior system-specific knowledge. Projection onto these distances produces free energy surfaces that reproduce known conformational states with low statistical uncertainty while maximizing independent dynamical information. This workflow enables systematic recasting of complex CVs into simple geometric descriptors without loss of essential dynamics. Its generality and automation make it broadly applicable for interpreting enhanced sampling simulations and generating interpretable conformational ensembles for integration with emerging machine learning approaches.

cond-mat.stat-mech↗

arXiv2025

Fast Sampling of Protein Conformational Dynamics

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.

cond-mat.stat-mech↗

arXiv2025

High-Throughput Computation of Anharmonic Low-Frequency Protein Vibrations

At room temperature, low frequency vibrations at far-infrared frequencies are thermally excited ($k_B T > h ν$) and not restricted to harmonic fluctuations around a single potential energy minimum. For folded proteins, these intrinsically anharmonic vibrations can contain information on slow conformational transitions. Recently, we have developed FREquency-SElective ANharmonic (FRESEAN) mode analysis, a method based on time correlation functions that isolates low-frequency vibrational motions from molecular dynamics simulation trajectories without relying on harmonic approximations. We recently showed that low-frequency vibrations obtained from FRESEAN mode analysis are effective collective variables in enhanced sampling simulations of conformational ensembles. However, FRESEAN mode analysis is based on velocity time correlations between all degrees of freedom, which creates computational challenges for large biomolecules. To facilitate future applications, we demonstrate here how coarse-graining of all-atom simulation trajectories can be combined with FRESEAN mode analysis to extract information on low-frequency vibrations at minimal computational cost.

cond-mat.stat-mech↗

arXiv2024

Exploring Conformational Landscapes Along Anharmonic Low-Frequency Vibrations

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.

cond-mat.stat-mech↗