Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring...

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• The Critical Assessment of PRediction of Interactions (CAPRI) is a community-wide experiment for assessing protein-protein complex structure prediction methods.
• Accurate prediction of protein-protein complex structures is crucial for understanding biological processes and drug discovery.

Purpose of the Study:

• To report the performance of our computational group in the CAPRI Round 37 competition for protein-protein complex structure prediction and scoring.
• To evaluate the effectiveness of combining template-based modeling with a novel docking program, LZerD, for predicting complex structures.

Main Methods:

• Utilized multi-chain template-based modeling (TBM) and the LZerD protein-protein docking program for structure prediction.
• LZerD employs 3D Zernike descriptors (3DZD) for soft surface representation, enabling tolerance to conformational changes.
• Implemented a hybrid strategy: TBM for targets with available templates and LZerD docking otherwise.

Main Results:

• Achieved near top-ranking performance for the human predictor group.
• Our server scorer group secured the top rank, a first for a server in CAPRI history.
• Demonstrated significant improvement in both prediction accuracy and scoring quality compared to previous CAPRI rounds.

Conclusions:

• The combination of multi-chain TBM and LZerD docking represents a powerful strategy for protein-protein complex structure prediction.
• The success highlights the potential of advanced docking algorithms and ensemble scoring functions in computational structural biology.
• Our findings underscore the value of objective assessments like CAPRI in driving methodological advancements.