Department of Biomedical and Chemical Engineering, Syracuse University, 343 Link Hall, Syracuse, NY 13244, USA.
Soft Matter. 2021 Mar 18;17(10):2725-2736. doi: 10.1039/d0sm02035d.
Bacterial colonization of biotic and abiotic surfaces and antibiotic resistance are grand challenges with paramount societal impacts. However, in the face of increasing bacterial resistance to all known antibiotics, efforts to discover new classes of antibiotics have languished, creating an urgent need to accelerate the antibiotic discovery pipeline. A major deterrent in the discovering of new antibiotics is the limited permeability of molecules across the bacterial envelope. Notably, the Gram-negative bacteria have nutrient specific protein channels (or porins) that restrict the permeability of non-essential molecules, including antibiotics. Here, we have developed the Computational Antibiotic Screening Platform (CLASP) for screening of potential drug molecules through the porins. The CLASP takes advantage of coarse grain (CG) resolution, advanced sampling techniques, and a parallel computing environment to maximize its performance. The CLASP yields comprehensive thermodynamic and kinetic output data of a potential drug molecule within a few hours of wall-clock time. Its output includes the potential of mean force profile, energy barrier, the rate constant, and contact analysis of the molecule with the pore-lining residues, and the orientational analysis of the molecule in the porin channel. In our first CLASP application, we report the transport properties of six carbapenem antibiotics-biapenem, doripenem, ertapenem, imipenem, meropenem, and panipenem-through OccD3, a major channel for carbapenem uptake in Pseudomonas aeruginosa. The CLASP is designed to screen small molecule libraries with a fast turnaround time to yield structure-property relationships to discover antibiotics with high permeability. The CLASP will be freely distributed to enable accelerated antibiotic drug discovery.
生物和非生物表面的细菌定植和抗生素耐药性是具有重大社会影响的重大挑战。然而，面对所有已知抗生素的细菌耐药性不断增加，发现新抗生素的努力进展缓慢，因此迫切需要加速抗生素发现的管道。在发现新抗生素方面的一个主要障碍是分子穿过细菌包膜的通透性有限。值得注意的是，革兰氏阴性菌具有营养特异性的蛋白质通道（或孔蛋白），限制了非必需分子（包括抗生素）的通透性。在这里，我们开发了计算抗生素筛选平台（CLASP），用于通过孔蛋白筛选潜在的药物分子。CLASP 利用粗粒度（CG）分辨率、先进的采样技术和并行计算环境来最大限度地提高其性能。CLASP 在几个小时的运行时间内即可生成潜在药物分子的全面热力学和动力学输出数据。它的输出包括潜在平均力曲线、能量势垒、速率常数以及分子与孔衬里残基的接触分析，以及分子在孔道中的取向分析。在我们的第一个 CLASP 应用中，我们报告了六种碳青霉烯类抗生素-比阿培南、多尼培南、厄他培南、亚胺培南、美罗培南和帕尼培南通过铜绿假单胞菌中碳青霉烯类摄取的主要通道 OccD3 的转运特性。CLASP 的设计目的是筛选小分子文库，以快速周转时间产生结构-性质关系，从而发现具有高通透性的抗生素。CLASP 将免费分发，以加速抗生素药物的发现。
