Abstract：
Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.
摘要：
类风湿性关节炎(RA)是一种慢性,以疼痛为特征的对称性炎症性自身免疫性疾病，肿胀的滑膜炎和关节糜烂，这可能会导致骨骼和软骨的损伤，并与进行性残疾有关。尽管扩大了治疗选择，一些患者仍然出现反应不足或无法忍受的不良反应。因此,RA的治疗选择仍然相当有限.AKT1酶对于设计各种人类疾病的药物至关重要，支持细胞功能，如增殖，生存,新陈代谢，正常和恶性细胞的血管生成。因此,AKT丝氨酸/苏氨酸激酶1被认为对于靶向旨在缓解RA机制的治疗策略至关重要。在这种情况下，针对AKT1的努力代表了开发新的抗关节炎药物的创新方法.这项研究的主要目的是使用诸如分子建模和动力学模拟（MDS）和基于形状的虚拟筛选（SBVS）等计算技术对AKT1抑制剂进行优先级排序。通过筛选源自ChemDiv和IMPPAT数据库的化合物库,采用组合的SBVS方法来预测针对AKT1的有效抑制剂。从SBVS结果来看，只有前三个化合物，ChemDiv_7266、ChemDiv_2796和ChemDiv_9468基于它们的高结合亲和力和有利的ADME/Tox性质进行稳定性分析。SBVS的研究结果表明，关键残基，包括Glu17，Gly37，Glu85和Arg273，显着有助于在AKT1的活性位点成功结合最高等级的前导化合物。这种见解有助于理解这些导线在抑制RA中的特定结合机制，促进更有效治疗剂的合理设计。
