Application of Anomaly Detection to Identify Important Features of Protein Dynamics.

ABSTRACT: Molecular dynamics (MD) simulations are a popular tool for the study of protein dynamics. Recent machine-learning-based structure prediction methods, such as AlphaFold, can provide a broad variety of initial protein structures for MD simulation. Hence, the development of methods to enhance the practicality of MD simulation (such as efficient sampling or detection of collective variables) is increasingly important. Identifying a small number of elements or features that can describe biological phenomena from MD trajectories serves as a basis for these methods. In this study, we applied the anomaly detection method based on sparse structure learning of the element correlation within MD trajectories to identify important features associated with state transitions. This approach was tested on

SUBMITTER: Yamamori Y 

PROVIDER: S-EPMC12163642 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature