A swift all-atom energy-based computational protocol to predict DNA-ligand binding affinity and DeltaTm

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Accurate prediction of DNA-ligand binding affinity is crucial for drug design.
• Existing computational methods often require extensive database training.
• Developing a versatile and accurate prediction protocol is essential.

Purpose of the Study:

• To develop a novel computational protocol for calculating DNA-ligand binding affinity.
• To validate the protocol's accuracy against experimental data.
• To create a user-friendly, web-accessible tool for binding affinity prediction.

Main Methods:

• A hybrid approach combining molecular mechanics, statistical mechanics, and solvent accessibility was employed.
• The protocol was validated using a dataset of 50 diverse DNA-ligand complexes.
• Binding energies were correlated with experimental binding free energies (ΔG°) and melting temperatures (ΔTm).

Main Results:

• The computational protocol demonstrated high accuracy, with correlation coefficients of 0.95 (R² = 0.90) for ΔG° and 0.96 (R² = 0.93) for ΔTm.
• The method achieved high correlation without relying on any database training.
• The protocol was successfully implemented as a web tool (http://www.scfbio-iitd.res.in/preddicta).

Conclusions:

• The developed hybrid computational protocol provides a reliable and accurate method for predicting DNA-ligand binding affinity.
• The freely accessible web tool facilitates rapid prediction, supporting rational drug design efforts.
• This approach offers a valuable resource for researchers in drug discovery and molecular modeling.