DeepBLI: A Transferable Multichannel Model for Detecting β-Lactamase-Inhibitor Interaction

Abstract

Pathogens producing β-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel β-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for β-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on β-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new β-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-β-lactamase AIM-1 and repurpose rottlerin for four classes of β-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

Recent Approaches of Intranasal to Brain Drug Delivery System

“…Potential adverse effects include nasal irritation, itching, epistaxis, alterations in smell and taste, rhinosinusitis, and damage to the nasal and olfactory epithelium [54]. Maintaining the pH of IN-administered formulations around human nasal pH (5.5-6.5) is also critical for minimizing the risk of nasal irritation and tissue damage [55]; however, many antipsychotics including aripiprazole [56], clozapine [57], olanzapine [58], risperidone [59], and quetiapine [60] are moderately basic in solution, such that prolonged IN use could cause irritation and irreversible damage.…”

PMF-CPI: assessing drug selectivity with a pretrained multi-functional model for compound–protein interactions

“…Pretrained protein language models such as TAPE [ 21 ] and ESM [ 22 ] provide a more direct way to transfer associate knowledge for more protein targets and they show capability in CPI prediction [ 23 , 24 ]. On the other hand, pretraining on large set helps transfer the associated knowledge to specific tasks with the fine-tuning step [ 25 ]. Thus, we can exploit these characteristics of pretrained CPI model to detect drug selectivity.…”