Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Protein-protein interactions are crucial for biological processes.
• Accurate prediction of these interactions is essential for understanding cellular mechanisms.
• The Critical Assessment of Prediction of Interactions (CAPRI) provides a benchmark for protein-protein docking methods.

Purpose of the Study:

• To assess the performance of the LZerD protein-protein docking program in recent CAPRI rounds.
• To investigate the effectiveness of various scoring functions for ranking docking models.
• To explore methods for predicting the quality of generated docking model pools.

Main Methods:

• Utilized the LZerD program, employing 3D Zernike descriptors (3DZD) for protein surface representation.
• Incorporated interface residue predictions (BindML, cons-PPISP) and literature data to guide docking.
• Employed a combination of scoring functions, including PRESCO, for model ranking.

Main Results:

• LZerD demonstrated performance in CAPRI rounds, with analysis identifying reasons for unsuccessful predictions.
• The correlation of multiple scoring functions effectively predicted the quality of the docking model pool, indicating the presence of near-native models.
• PRESCO scoring function evaluated the native-likeness of residue spatial environments.

Conclusions:

• The study validates the utility of LZerD and 3D Zernike descriptors for protein-protein docking, particularly for unbound cases.
• Combining scoring functions offers a reliable strategy for assessing the quality of docking predictions.
• The findings on scoring functions are broadly applicable to various protein docking methods.