Bioinformatic and computational biophysics tools for nanopore engineering: a review from standard approaches to machine learning advancements

Abstract

Nanopores are key components in several emerging technologies. Nanopore sensors allow the observation of single molecules from the signature they leave in the ionic current flowing through the pore, while nanoporous membranes provide a potentially customizable platform to achieve unprecedented ion selectivity performance. In parallel to the improvements in the fabrication techniques, computational methods have flourished in the last few years. However, despite these advancements, bioinformatic and computational biophysics tools are still not systematically employed in nanopore research when compared to other engineering fields that integrated computer-assisted design (CAD) in their development pipeline decades ago. This review aims to provide a wide-ranging overview of the main bioinformatic tools useful for the engineering of biological nanopores including: analysing the effect of mutations on pore properties, determining the protonation state at different pH and studying the electrostatic environment via adaptive Poisson-Boltzmann solver (APBS). A final section presents recent progress in de novo design using AI-based methods. To favour the widespread adoption of these approaches, the Supplementary Information contains some scripts and protocols that may aid the readers to integrate these tools in their design approaches.