Free energy of binding of coiled-coil complexes with different electrostatic environments: the influence of force field polarisation and capping

Virtual screening and biological activity evaluation of novel efflux pump inhibitors targeting AdeB

The AdeABC efflux pump is an important mechanism causing multidrug resistance in Acinetobacter baumannii, and its main component AdeB can recognize carbapenems, aminoglycosides, and other multi-class antibiotics and efflux them intracellularly, which is an ideal target for the development of anti-multidrug resistant bacteria drugs. Here, we combined multiple computer-aided drug design methods to target AdeB to identify promising novel structural inhibitors. Virtual screening was performed by molecular docking and molecular dynamics simulation (MD) and 12 potential compounds were identified from the databases. Meanwhile, their biological activities were validated by in vitro activity assays, and ChemDiv L676-2179 (γ-IFN), ChemDiv L676-1461, and Chembridge 53717615 were confirmed to suppress efflux effects and restore antibiotic susceptibility of resistant bacteria, which are expected to be developed as adjuvant drugs for the treatment of multi-drug resistant Acinetobacter baumannii clinical infections.

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning

MOTIVATION

In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available.

RESULTS

Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instead of those expressing the entire molecule, as well as the circularity of the cyclic peptides should be considered. Therefore, we developed a prediction method named CycPeptPPB using deep learning that considers both factors. First, the macrocycle ring of cyclic peptides was decomposed residue by residue. The residue-based descriptors were arranged according to the sequence information of the cyclic peptide. Furthermore, the circular data augmentation method was used, and the circular convolution method CyclicConv was devised to express the cyclic structure. CycPeptPPB exhibited excellent performance, with mean absolute error (MAE) of 4.79% and correlation coefficient (R) of 0.92 for the public drug dataset, compared to the prediction performance of the existing PPB rate prediction software (MAE=15.08%, R=0.63).

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online supplementary material. The source code of CycPeptPPB is available at https://github.com/akiyamalab/cycpeptppb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.