您选择的条件: Leyun Wu
  • Impact of Halogen Bonds on Protein-Peptide Binding and Protein Structural Stability Revealed by Computational Approaches

    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.

  • D3Rings: A fast and accurate method for ring system identification and deep generation of drug-like cyclic compounds

    分类: 药物科学 >> 药物设计 提交时间: 2024-02-06

    摘要: Continuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in 3 different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, fused and bridged rings. Based on this, three deep generative models, namely VAE, AAE, and CharRNN, were trained and used to construct two datasets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged datasets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like dataset against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures were exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.