Learning to Model G-Quadruplexes: Current Methods and Perspectives.

Area of Science:

• Biochemistry and Molecular Biology
• Computational Chemistry
• Medicinal Chemistry

Background:

• G-quadruplexes, noncanonical DNA structures, are implicated in cancer via telomeres and oncogene promoters.
• Stabilization of G-quadruplexes can disrupt tumor cell growth, making them therapeutic targets.
• Mechanisms of G-quadruplex folding and stabilization by various molecules (cations, organic compounds, metal complexes) are not fully understood.

Purpose of the Study:

• To review and compile computational studies on G-quadruplex formation, stabilization, and interactions with small molecules.
• To highlight the synergy between experimental and computational approaches in understanding G-quadruplexes.
• To categorize and discuss different in silico methods used in G-quadruplex research.

Main Methods:

• Focus on in silico approaches, categorizing them into classical and quantum methods.
• Classical methods: Long-timescale molecular dynamics simulations for analyzing dynamical information.
• Quantum methods: Semiempirical, QM/MM, and DFT for explicit electronic structure simulation (providing static insights).

Main Results:

• Computational methods are vital for rationalizing the interaction of small molecules with G-quadruplexes.
• Classical simulations offer insights into dynamics, while quantum methods provide detailed electronic structure information.
• A comprehensive overview of leading computational studies in the field is presented.

Conclusions:

• Synergistic use of experimental and computational methods is crucial for advancing G-quadruplex research.
• In silico approaches provide essential tools for understanding G-quadruplex stabilization and interactions.
• This review consolidates current computational strategies for G-quadruplex-targeted drug development.