Can multiscale simulations unravel the function of metallo-enzymes to improve knowledge-based drug discovery?. (2nd April 2019)

• Record Type: Journal Article
• Authors: Sgrignani, Jacopo; Casalino, Lorenzo; Doro, Fabio; Spinello, Angelo; Magistrato, Alessandra
• Is Part Of: Future medicinal chemistry. Volume 11:Number 7(2019)
• Page Start: 771
• Page End: 791
• Publication Date: 2019-04-02
• Subjects: CYP19A1 -- DNA processing enzymes -- drug discovery -- Metallo-proteins -- QM/MM molecular dynamics -- ribozyme -- spliceosome -- steroid synthesis; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.4155/fmc-2018-0495
• Languages: English
• ISSNs: 1756-8919

Abstract

Metallo-enzymes are a large class of biomolecules promoting specialized chemical reactions. Quantum-classical quantum mechanics/molecular mechanics molecular dynamics, describing the metal site at quantum mechanics level, while accounting for the rest of system at molecular mechanics level, has an accessible time-scale limited by its computational cost. Hence, it must be integrated with classical molecular dynamics and enhanced sampling simulations to disentangle the functions of metallo-enzymes. In this review, we provide an overview of these computational methods and their capabilities. In particular, we will focus on some systems such as CYP19A1 a Fe-dependent enzyme involved in estrogen biosynthesis, and on Mg 2+ -dependent DNA/RNA processing enzymes/ribozymes and the spliceosome, a protein-directed ribozyme. This information may guide the discovery of drug-like molecules and genetic manipulation tools.