X-Entropy

X-Entropy calculates entropies from dihedral-angle and other one-dimensional distributions derived from molecular dynamics (MD) simulation data to quantify local protein flexibility.

Key Features:

• Kernel Density Estimation (KDE): Employs Gaussian kernel density estimation to estimate probability density functions using the plug-in bandwidth selection method proposed by Z. Botev et al.
• Parallelization: Backend implemented in C++ with OpenMP parallelization for high-performance processing of large datasets.
• Python frontend: Provides a Python frontend exposing wrapper functions for dihedral entropy calculations.
• Generalized application: Computes entropy for any one-dimensional data distribution in addition to dihedral-angle distributions.

Scientific Applications:

• Protein Flexibility Analysis: Provides an alignment-independent measure of local protein flexibility and conformational variability from MD-derived dihedral entropies.
• Benchmarking and Performance Evaluation: Validated on Gaussian-distributed samples and compared with established Python KDE libraries for accuracy and computational performance.

Methodology:

Computational methods include Gaussian KDE with the plug-in bandwidth selector of Z. Botev et al., entropy computation from KDE-estimated probability density functions of dihedral-angle and other one-dimensional data, a C++ implementation with OpenMP parallelization, a Python frontend, and benchmarking on Gaussian samples with comparisons to established Python KDE libraries.