目前,变构抑制剂已成为开发针对抗药性灰霉病的防腐剂的有效策略(B.cinerea)。然而,事实证明,他们被动驱动的开发效率对满足实际需求具有挑战性。这里,利用深度学习神经关系推理(NRI)框架,我们积极鉴定了一种针对灰霉病菌几丁质酶的变构抑制剂,即,2-丙酮酮。2-乙酰萘酮与几丁质酶的关键结构域结合,与变构位点形成强烈的相互作用。在整个互动过程中,2-丙酮酮降低了蛋白质的整体连通性,诱导构象变化。这些发现与从几丁质酶活性实验获得的结果一致。显示IC50值为67.6μg/mL。此外,2-丙酮酮表现出出色的抗B。通过抑制几丁质酶的灰霉病活性。在灰霉病感染模型中,2-丙酮酮显著延长了樱桃番茄的保存时间,将其定位为水果储存的有前途的防腐剂。
Currently, allosteric inhibitors have emerged as an effective strategy in the development of preservatives against the drug-resistant Botrytis cinerea (B. cinerea). However, their passively driven development efficiency has proven challenging to meet the practical demands. Here, leveraging the deep learning Neural Relational Inference (NRI) framework, we actively identified an allosteric inhibitor targeting B. cinerea Chitinase, namely, 2-acetonaphthone. 2-Acetonaphthone binds to the crucial domain of Chitinase, forming the strong interaction with the allosteric sites. Throughout the interaction process, 2-acetonaphthone diminished the overall connectivity of the protein, inducing conformational changes. These findings align with the results obtained from Chitinase activity experiments, revealing an IC50 value of 67.6 μg/mL. Moreover, 2-acetonaphthone exhibited outstanding anti-B. cinerea activity by inhibiting Chitinase. In the gray mold infection model, 2-acetonaphthone significantly extended the preservation time of cherry tomatoes, positioning it as a promising preservative for fruit storage.