分类: 药物科学 >> 药物设计 提交时间: 2024-04-10
摘要: Halogen bonds (XBs) are essential non-covalent interactions in molecular recognition and drug design. Current studies on XBs in drug design mainly focus on the interactions between halogenated ligands and target proteins, lacking a systematic study on naturally existing and artificially prepared halogenated residue XBs (hr_XBs) and their characteristics. Here, we conducted a computational study on the potential hr_XBs in proteins/peptides using database searching, quantum mechanics calculations, and molecular dynamics simulations. XBs at protein-peptide interaction interfaces are found to enhance their binding affinity. Additionally, the formation of intramolecular XBs (intra_XBs) within proteins may significantly contribute to the structural stability of structurally flexible proteins, while having a minor impact on proteins with inherently high structural rigidity. Impressively, introducing halogens without the formation of intra_XBs may lead to a decrease in protein structural stability. This study enriches our comprehension of the roles and effects of halogenated residue XBs in biological systems.
分类: 药物科学 >> 药物设计 提交时间: 2024-05-13
摘要: As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1,339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.