A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR

Conformational changes essential for protein function involve transitions through multiple short-lived, high-energy states within the complex free energy landscape. While existing methods, such as Markov State Models and non-Markovian approaches built from molecular dynamics (MD) simulations, can effectively capture metastable states, they struggle to identify transition states. Transition state identification via dispersion and variational principle regularized neural networks (TS-DAR) is a computational framework that utilizes out-of-distribution detection to systematically identify all transition states involved in specific biomolecular conformational changes. TS-DAR leverages a deep learning model to map protein conformations from MD simulations onto a hyperspherical latent space. This low-dimensional representation retains the critical kinetic information of biomolecular conformational changes. To distinguish metastable states from transition states, TS-DAR utilizes a VAMP-2 and dispersion loss function, enabling the automated identification of transition state conformations. This framework provides a comprehensive view of protein conformational landscapes and facilitates studies on drug binding, enzyme activity, and mutation effects. This tutorial aims to guide researchers through the implementation and application of TS-DAR, highlighting its utility in computational biophysics. The code for this tutorial is available at: https://github.com/xuhuihuang/ts-dar-tutorials.