KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions.

Area of Science:

• Computational chemistry
• Structural bioinformatics
• Drug discovery

Background:

• Protein-ligand interactions are crucial in biology and drug discovery.
• Current docking and affinity prediction tools face challenges in precision and efficiency.
• Physics-based methods are accurate but computationally expensive.

Purpose of the Study:

• To develop a novel, fast, and efficient computational scoring function for protein-ligand docking and screening.
• To assess the performance of a minimalist, coarse-grained, sidechain-free potential.

Main Methods:

• A novel coarse-grained potential (KORP-PL) was developed based on a 3D joint probability distribution function.
• The potential considers only pairwise orientation and position between protein backbone and ligand atoms.
• Performance was evaluated on benchmarks like DUD-E, comparing against established tools such as Autodock Vina.

Main Results:

• KORP-PL demonstrated highly competitive results compared to state-of-the-art scoring functions.
• A twofold improvement in the median 5% enrichment factor was observed on the DUD-E benchmark versus Autodock Vina.
• The study confirmed that a coarse, sidechain-free potential is sufficient for accurate docking pose prediction.

Conclusions:

• KORP-PL offers a significant advancement in computational efficiency and accuracy for protein-ligand interaction studies.
• The developed potential provides a powerful tool for large-scale virtual screening in drug discovery.
• The simplicity and effectiveness of KORP-PL make it a valuable alternative to more complex methods.