Khan Muhammad Naseem, Farooq Umar, Khushal Aneela, Wani Tanveer A, Zargar Seema, Khan Sara
Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Molecular Reaction dynamics, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2025 May 9;20(5):e0321500. doi: 10.1371/journal.pone.0321500. eCollection 2025.
EGFR is critical for tumor angiogenesis and cancer progression, but existing treatments like erlotinib face limitations such as acquired resistance and side effects. To address these issues, this study employs structure-based drug design techniques including virtual screening, molecular docking, and molecular dynamics simulations to identify new small molecule inhibitors targeting the EGFR kinase domain. From an initial selection of 633,000 compounds from diverse databases, top candidates were identified based on their binding affinity and stability. The virtual screening and docking analyses revealed compounds with higher binding scores than erlotinib. Molecular dynamics simulations and Anisotropic Network Model (ANM) analysis uniquely report that EGFR undergoes significant conformational shifts: inward flap movements in the bound state stabilize a closed conformation, while outward movements in the free state result in a more open conformation. Among the identified inhibitors, compounds such as JFD00243, NPA015124, and others exhibited strong binding affinities and stable interactions with both active and inactive forms of EGFR. Notably, JFD00243 was effective in targeting EGFR in both active and inactive conformations. These findings suggest that the identified inhibitors could potentially overcome current treatment limitations and improve targeted cancer therapies by effectively inhibiting EGFR-mediated tumor angiogenesis.
表皮生长因子受体(EGFR)对肿瘤血管生成和癌症进展至关重要,但像厄洛替尼这样的现有治疗方法面临诸如获得性耐药和副作用等局限性。为了解决这些问题,本研究采用基于结构的药物设计技术,包括虚拟筛选、分子对接和分子动力学模拟,以识别靶向EGFR激酶结构域的新型小分子抑制剂。从不同数据库最初筛选的633,000种化合物中,根据其结合亲和力和稳定性确定了顶级候选物。虚拟筛选和对接分析揭示了结合分数高于厄洛替尼的化合物。分子动力学模拟和各向异性网络模型(ANM)分析独特地报告称,EGFR经历了显著的构象变化:结合状态下的向内襟翼运动稳定了封闭构象,而自由状态下的向外运动导致更开放的构象。在鉴定出的抑制剂中,如JFD00243、NPA015124等化合物与EGFR的活性和非活性形式均表现出强结合亲和力和稳定的相互作用。值得注意的是,JFD00243在靶向活性和非活性构象的EGFR方面均有效。这些发现表明,鉴定出的抑制剂可能潜在地克服当前治疗局限性,并通过有效抑制EGFR介导的肿瘤血管生成来改善靶向癌症治疗。