Automated procedure for contact-map-based protein structure reconstruction.

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Three-dimensional protein structures are crucial for understanding ion channel function and conductivity.
• Protein contact map information significantly enhances the accuracy of structure prediction.
• Existing methods for contact-map-based structure prediction lack standardized validation, hindering comparison.

Purpose of the Study:

• To design and validate a robust Contact Map-to-Structure pipeline (C2S_pipeline) for protein structure reconstruction.
• To assess the pipeline's performance on monomeric and multimeric proteins using various contact information subsets.
• To investigate the correlation between structural quality and electrostatic potential in ion channel models.

Main Methods:

• Development of the Contact Map-to-Structure pipeline (C2S_pipeline).
• Validation using a representative set of protein structures, measuring accuracy with Root Mean Square Deviation (RMSD).
• Case study on the KcsA ion channel, analyzing models reconstructed with varying contact information and electrostatic potential.

Main Results:

• The C2S_pipeline achieved a median RMSD of 5.27 Å and a best-case RMSD of 1.59 Å on diverse protein structures.
• Electrostatic potential analysis proved effective in discriminating between high and low-quality KcsA ion channel models.
• Accurate KcsA structures required approximately 30% of random contact information, increasing to 70% for erroneous maps.

Conclusions:

• The C2S_pipeline provides a reliable method for protein structure reconstruction from contact maps.
• Electrostatic parameters serve as valuable indicators for assessing the quality of predicted protein structures.
• Reconstruction accuracy is influenced by the quantity and quality of contact information, with higher accuracy in protein cores.