X-Entropy: A Parallelized Kernel Density Estimator with Automated Bandwidth Selection to Calculate Entropy.

Area of Science:

• Computational Biology
• Biophysics
• Bioinformatics

Background:

• Local protein flexibility is crucial for understanding protein function and dynamics.
• Accurate and efficient calculation of protein flexibility measures is essential for molecular modeling and drug discovery.
• Existing methods for calculating protein flexibility may lack computational efficiency or ease of integration into analysis pipelines.

Purpose of the Study:

• To introduce X-Entropy, a novel Python package for calculating dihedral entropy.
• To provide an alignment-independent measure of local protein flexibility.
• To offer a computationally efficient and user-friendly tool for bioinformatics workflows.

Main Methods:

• Gaussian kernel density estimation (KDE) with plug-in bandwidth selection.
• A C++ backend for high-performance computation, parallelized with OpenMP.
• A Python frontend with predefined wrapper functions for 1D approximations and full access to the C++ backend.

Main Results:

• X-Entropy provides a straightforward method for calculating dihedral entropy.
• The package demonstrates efficient computational performance compared to established KDE libraries.
• Benchmarking against Gaussian distributions and analytical solutions validates the accuracy of the entropy calculations.

Conclusions:

• X-Entropy offers a robust, efficient, and easily integrable tool for assessing local protein flexibility.
• The package facilitates advanced analysis of protein dynamics within Python-based research.
• X-Entropy is freely available, promoting wider adoption in the scientific community.