Precision in proteins: predicting pKa with PypKa, a Poisson–Boltzmann-based tool

The aim is to establish an easy-to-use cloud service that allows for fast pKa and isoelectric point calculations using user-provided protein structures or those obtained from the Protein Data Bank. The primary goal is to make PypKa the go-to solution for these calculations, building upon its high accuracy and computational speed. Additionally, we aim to create a large dataset of pKa values and isoelectric points, which will be pivotal in training machine-learning algorithms. PypKa is a Python module to predict Poisson-Boltzmann-based pKa values of biomolecules. This is a free and open source project that provides a simple, reusable and extensible Python API and CLI for pKa calculations with a valuable trade-off between fast and accurate predictions. With PypKa, one can enable pKa calculations, including optional proton tautomerism, within existing protocols by adding a few extra lines of code. PypKa supports CPU parallel computing on anisotropic (membrane) and isotropic (protein) systems and allows the user to find a balance between accuracy and speed.

Considering that the performance of the PypKa cloud service is scaling almost linearly concerning the number of CPU cores, to support this challenge, the PypKa cloud server was deployed on the cloud resources of the EGI cloud infrastructure. Specifically, a dedicated virtual cluster, whose resources scale dynamically, taking into account the number of users’ requests to be served, was configured using the Infrastructure Manager (IM) solution developed by the Grid and High-Performance Computing Group (GRyCAP) at the Instituto de Instrumentación para Imagen Molecular (I3M) from the Universitat Politècnica de València (UPV).