Abstract
X-Entropy is a Python package used to calculate the entropy of a given distribution, in this case, based on the distribution of dihedral angles. The dihedral entropy facilitates an alignment-independent measure of local protein flexibility. The key feature of our approach is a Gaussian kernel density estimation (KDE) using a plug-in bandwidth selection, which is fully implemented in a C++ backend and parallelized with OpenMP. We further provide a Python frontend, with predefined wrapper functions for classical coordinate-based dihedral entropy calculations, using a 1D approximation. This makes the package very straightforward to include in any Python-based analysis workflow. Furthermore, the frontend allows full access to the C++ backend, so that the KDE can be used on any binnable one-dimensional input data. In this application note, we discuss implementation and usage details and illustrate potential applications. In particular, we benchmark the performance of our module in calculating the entropy of samples drawn from a Gaussian distribution and the analytical solution thereof. Further, we analyze the computational performance of this module compared to well-established python libraries that perform KDE analyses. X-Entropy is available free of charge on GitHub (https://github.com/liedllab/X-Entropy).
Highlights
All physicochemical properties correspond to an ensemble of structures with varying probabilities and not to a single structure alone.[3−5] It is well established that countless physiological processes, such as biomolecular recognition,[6−8] catalytic activity,[2] or drug binding,[9] are directly linked to a biomolecule’s conformational ensemble
Molecular dynamics (MD) simulations are a vital tool to study the conformational flexibility of biomolecules, as they capture conformational ensembles in atomistic detail with reliable state probabilities.[3,9]
We report that the processing of the data of this 2 μs accelerated MD simulation (aMD) simulation took ∼2.6 s altogether (200 000 data points for each dihedral)
Summary
Biomolecules constantly fluctuate between various conformations.[1,2] all physicochemical properties correspond to an ensemble of structures with varying probabilities and not to a single structure alone.[3−5] It is well established that countless physiological processes, such as biomolecular recognition,[6−8] catalytic activity,[2] or drug binding,[9] are directly linked to a biomolecule’s conformational ensemble. Classical MD simulations are generally hardware limited to the lower microsecond time scale, while most biologically relevant processes occur at much slower time scales.[2] Numerous enhanced sampling techniques have emerged over the last decades aiming to circumvent this limitation.[27] these techniques vary significantly in their fundamental assumptions, they share the common feature of a bias energy, which is incorporated to accelerate phase space exploration Since this bias substantially distorts the free energy surface, reweighting schemes need to be applied to retrieve the system’s unbiased thermodynamics.[23] Here we use aMD as an example enhanced sampling method to showcase X-Entropy’s ability to reweigh biased data on the fly. Similar to previous studies[8,11] the calculated entropy for the backbone dihedrals of each amino acid was mapped onto the crystal structure for an intuitive representation of the protein’s dynamics For the calculation of the entropy, we report a wall clock time of 75.6 s, for all 103 105 frames comprising the BPTI simulation on a standard PC
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
