Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Macromolecular oligomeric assemblies are crucial for cellular processes.
• Determining the structure of large assemblies is challenging due to size and binding affinity variations.
• Existing methods struggle with ranking models for large complex structures.

Purpose of the Study:

• To develop a novel combinatorial greedy algorithm for assembling large oligomeric complexes.
• To infer complex symmetry during the assembly process, eliminating the need for prior information.
• To introduce an efficient geometric scoring function, the transformation match score, to improve model ranking.

Main Methods:

• A combinatorial greedy algorithm was developed to assemble complexes from monomer interaction interface data.
• An efficient geometric score, the transformation match score, was implemented for simultaneous scoring of dimers.
• A benchmark set of 308 homo- and heteromeric complexes was compiled for testing.

Main Results:

• The algorithm successfully reconstructed 252 out of 308 complexes (81.8%) using optimized parameters.
• Cross-validation coverage achieved a mean fraction of 78.1% correctly reconstructed complexes.
• The method demonstrated robust performance across a diverse range of biological complex sizes and types.

Conclusions:

• The proposed algorithm effectively reconstructs the topology of large and complex biological assemblies.
• This method offers a significant advancement in structural determination of macromolecular complexes.
• The approach is applicable to a wide variety of homo- and heteromeric complexes.