# InfleCS: Clustering Free Energy Landscapes with Gaussian Mixtures

## TL;DR

InfleCS is a novel clustering method that accurately identifies core states in free energy landscapes, aiding the analysis of biomolecular conformational changes with superior performance over existing methods.

## Contribution

The paper introduces InfleCS, a new Gaussian mixture-based clustering approach that effectively extracts metastable states from free energy landscapes, outperforming existing techniques.

## Key findings

- InfleCS outperforms other clustering methods on toy models.
- It successfully identifies core states in biomolecular free energy landscapes.
- Application to Calmodulin reveals a conformational pathway driven by electrostatics.

## Abstract

Free energy landscapes provide insights into conformational ensembles of biomolecules. In order to analyze these landscapes and elucidate mechanisms underlying conformational changes, there is a need to extract metastable states with limited noise. This has remained a formidable task, despite a plethora of existing clustering methods. We present InfleCS, a novel method for extracting well-defined core states from free energy landscapes. The method is based on a Gaussian mixture free energy estimator and exploits the shape of the estimated density landscape. The core states that naturally arise from the clustering allow for detailed characterization of the conformational ensemble. The clustering quality is evaluated on three toy models with different properties, where the method is shown to consistently outperform other conventional and state-of-the-art clustering methods. Finally, the method is applied to a temperature enhanced molecular dynamics simulation of Ca2+ bound Calmodulin. Through the free energy landscape, we discover a pathway between a canonical and a compact state, revealing conformational changes driven by electrostatic interactions.