Emerging Methods and Applications to Decrypt Allostery in Proteins and Nucleic Acids

Area of Science:

• Biophysics
• Structural Biology
• Computational Biology

Background:

• Large protein-nucleic acid complexes often display allosteric regulation, a complex process involving conformational dynamics and catalytic function.
• Elucidating allosteric mechanisms in these systems is challenging for current analytical techniques.

Purpose of the Study:

• To review established and introduce innovative computational approaches for studying allosteric mechanisms in protein-nucleic acid complexes.
• To present novel network models integrating graph theory with advanced simulation methods.

Main Methods:

• Utilized molecular dynamics (MD) simulations, including accelerated MD and ab-initio MD.
• Developed enhanced network models for long-timescale allosteric responses and conformational changes.
• Created ab-initio network models combining graph theory with QM/MM for catalytic allostery analysis.

Main Results:

• Demonstrated how enhanced network models capture allosteric responses over extended timescales.
• Showcased ab-initio network models revealing step-by-step catalytic regulation and transition state modulation.
• Illustrated tense-to-relaxed allosteric regulation during biochemical reaction steps.

Conclusions:

• These integrated computational approaches provide innovative protocols for determining allosteric mechanisms.
• The methods offer significant promise for applications in medicine and bioengineering, particularly for understanding complexes like nucleosomes, CRISPR-Cas9, and spliceosomes.