Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.
Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina29208, United States.
J Chem Inf Model. 2022 Nov 28;62(22):5830-5840. doi: 10.1021/acs.jcim.2c01008. Epub 2022 Oct 16.
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.
产生β-内酰胺酶的病原体对治疗抗生素耐药性感染构成了巨大挑战；因此，迫切需要发现新型β-内酰胺酶抑制剂用于药物开发。传统的高通量筛选非常昂贵，而基于结构的虚拟筛选则受到机制的限制。在这项研究中，我们构建了一种新型的多通道深度神经网络（DeepBLI）用于β-内酰胺酶抑制剂筛选，使用标签反转 KIBA 数据集进行预训练，并在 BindingDB 中的β-内酰胺酶-抑制剂对上进行微调。首先，编码器对（Conv 和 Att）融合了酶和抑制剂的空间和顺序信息。然后，共同注意模块创建抑制剂和酶嵌入之间的连接。最后，多通道输出通过元素乘积融合，然后输入 3 层全连接网络进行预测交互。与最先进的方法相比，DeepBLI 的 AUROC 为 0.9240，AUPRC 为 0.9715，这表明它可以识别新的β-内酰胺酶-抑制剂相互作用。为了证明其预测能力，描述了 DeepBLI 的一个应用，用于筛选金属β-内酰胺酶 AIM-1 的潜在抑制剂化合物，并将rottlerin 重新用于四类β-内酰胺酶靶标，表明它有可能成为一种广谱抑制剂。DeepBLI 为抗菌药物开发提供了一种有效的方法，有助于治疗抗生素耐药性。
