Maintaining solvent accessible surface area under rotamer substitution for protein design

Area of Science:

• Computational Biology
• Structural Biology
• Protein Design

Background:

• Solvent Accessible Surface Area (SASA) calculations are crucial for protein design and structural biology.
• Accurate SASA computation is computationally expensive, limiting its integration into standard protein design workflows.

Purpose of the Study:

• To develop an efficient method for maintaining accurate SASA during Monte Carlo simulations of protein design.
• To accelerate SASA calculations within protein sequence and rotamer optimization for a fixed protein backbone.

Main Methods:

• Extended the Le Grand and Merz algorithm by replacing semigroup operations with group operations for efficient SASA updates.
• Developed an algorithm where computational cost scales with the number of affected atoms, not the total protein size.
• Implemented a method to maintain accurate SASA during Monte Carlo searches in sequence and rotamer space.

Main Results:

• The new algorithm achieves SASA updates in time proportional to affected atoms, significantly faster than recalculating from scratch.
• For a 100-residue protein, the method is approximately 145 times faster than the original Le Grand and Merz approach.
• Demonstrated practical effectiveness by optimizing a SASA-based protein packing measure in large-scale protein redesign.

Conclusions:

• The developed method significantly reduces the computational cost of SASA calculations in protein design.
• This advancement enables the effective integration of SASA-based scoring into complex protein design simulations.
• The approach facilitates the optimization of protein packing and the redesign of proteins and protein interfaces.