DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression

Area of Science:

• Computational biology
• Biophysics
• Bioinformatics

Background:

• Designing biosequences for biosensing and therapeutics is complex.
• Computational modeling can accelerate design via virtual screening.
• Current models lack flexibility or are computationally expensive.

Purpose of the Study:

• To introduce DeltaGzip, a novel computational approach.
• To evaluate binding free energy in biopolymer-ligand complexes.
• To overcome limitations of existing prediction methods.

Main Methods:

• Utilizing ultrashort equilibrium molecular dynamics simulations.
• Applying Kolmogorov complexity for entropy evaluation.
• Approximating entropy using the Gzip lossless compression algorithm.

Main Results:

• DeltaGzip accurately predicts binding free energy.
• Method validated on protein-ligand complexes.
• Predictions align with Jarzynski equality and experimental data.

Conclusions:

• DeltaGzip offers an efficient computational tool for biosequence design.
• The method enhances prediction accuracy under various conditions.
• Accelerates development of novel therapeutics and biosensors.