Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, California, USA.
Graduate Program in Biophysics, University of California-San Francisco, San Francisco, California, USA.
Protein Sci. 2023 Aug;32(8):e4712. doi: 10.1002/pro.4712.
Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (M ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of M inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like M versus other targets where it has been more successful, and versus other structure-based techniques against M itself.
需要抗病毒疗法来治疗 SARS-CoV-2,以降低当前 COVID-19 大流行的发病率。一个有充分先例的药物靶点是主要病毒蛋白酶(M),它是一种已批准药物和几种研究性药物的靶点。新出现的病毒耐药性使得新的抑制剂类更加迫切。我们采用基于结构的方法,将 12 亿个非共价键类似物和一个新的 650 万个亲电试剂库对接在酶结构上。从这些化合物中,我们在 37 个系列中鉴定出 29 个非共价和 11 个共价抑制剂,其中最有效的抑制剂的 IC 分别为 29 和 20 μM。对几个系列进行了优化,得到了低微摩尔抑制剂。随后的晶体学证实了对接预测的结合模式,并可能为进一步优化提供模板。虽然新的化学类型可能有助于进一步优化针对 SARS-CoV-2 的 M 抑制剂,但这种中等的成功率也揭示了我们的方法在应对 M 等具有挑战性的目标方面的弱点,与其他目标相比,它在其他针对 M 本身的基于结构的技术方面也更为成功。