New drugs are urgently needed for Gram-negative species for which the presence of the additional outer membrane hinders the access of molecules to internal targets. Our limiting understanding of the molecular basis for compound entry and efflux out from these pathogens is widely recognized as a key bottleneck for rational design of novel antibacterial compounds with good cellular activity. General porins are a well-known route of entry into Gram-negative bacteria for polar compounds, but calculating the number of molecules per second that translocate through these nanochannels (molecular flux) is a challenging computational and experimental task, and a long-standing goal of antibacterial drug discovery. Starting from new high-resolution X-ray structures of the major prototypical porins from Enterobacteriaceae we performed a thorough computational investigation, which revealed the common filtering mechanism of general porins. From the systematic analysis of permeability data for nine clinically relevant antibiotics through eight Enterobacteriaceae porins from different species, we condensed the molecular mechanism behind molecular translocation through porins in a scoring function (or supervised machine learning algorithm) that is able to predict molecular permeability. The scoring function is based on the energetics of the process at atomic level and it does not depend on specific chemical groups. This bottom up computational approach has been validated using a wide range of microbiological assays including cellular activity assays. We also present here the permeability prediction of a larger dataset of nearly 200 compounds not used in the training phase, with a correlation of R=0.84 to recent accumulation studies in <i>E. coli</i>. The scoring function enables the virtual screening of large virtual databases (thousand of molecules per day) to identify scaffolds with good permeation and/or improve molecules with poor permeation through porins and guide antibacterial drug design.<br/>

From structure to molecular flux: Predicting compound permeability in Gram-negative bacteria

2019 Fall National Meeting

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