Experimental and computational models to understand protein-ligand, metal-ligand and metal-DNA interactions pertinent to targeted cancer and other therapies

• Published in: European journal of medicinal chemistry reports 2024-04, Vol.10, p.100133, Article 100133
• Main Authors: Patil, Vaishali M., Gupta, Satya P., Masand, Neeraj, Balasubramanian, Krishnan
• Format: Article
• Language: English
• Subjects: Computational methods; Experimental methods; Mathematical and artificial intelligence methods; Metal-ligand interactions; Protein-ligand interactions
• ISSN: 2772-4174; 2772-4174
• DOI: 10.1016/j.ejmcr.2024.100133

The protein-ligand interactions play critical roles in targeted therapies including viral and cancer therapies. Hence in recent years several experimental and computational methods have been developed to address the mechanism of selectivity, swift binding, and enzyme functions. Furthermore big data, neural networks and artificial intelligence techniques are becoming increasingly important while combinatorial and graph theoretical methods are important arms of such techniques. Experimental methods such as isothermal titration calorimetry and surface plasmon resonance are used frequently to measure the binding affinity. Some of the computational methods used to examine the protein-ligand interactions are electrostatic calculations, molecular dynamics simulation, hybrid dynamics methods, etc., which shed light on structural stability, binding, protein functions etc. In this review we describe applications of a few selected experimental, computational and artificial intelligence tools with focus on graph theory to predict protein-ligand, metal-ligand interactions with implications in various types of malignancies and computational toxicology with focus on protein-heavy metal ion interactions and complexes of heavy metal ions with various ligands of biomedical relevance. [Display omitted]