Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Protein Dynamics

Background:

• Understanding protein side chain conformations is crucial for predicting protein structure and function.
• Exploring the vast conformational space of protein side chains is computationally challenging.
• Accurate calculation of conformational entropy requires comprehensive sampling of accessible states.

Purpose of the Study:

• To develop and present an algorithm for efficiently searching protein side chain conformational space.
• To identify global minimum energy conformations and states within an energy cutoff.
• To calculate the side chain contribution to protein conformational entropy.

Main Methods:

• An algorithm was developed to systematically search the conformational space of protein side chains.
• The algorithm identifies the global minimum energy conformation and all conformations within a specified energy threshold.
• It explores the impact of different energy models and rotamer libraries on the conformational energy surface.

Main Results:

• The algorithm successfully identified global minimum energy conformations and near-minimum energy states for various proteins.
• The study revealed that most low-energy conformations result from independent side chain adjustments rather than concerted changes.
• The partition function was evaluated, enabling direct calculation of side chain conformational entropy.

Conclusions:

• The developed algorithm provides an efficient method for exploring protein side chain conformational landscapes.
• The findings suggest that protein side chain flexibility is largely governed by independent residue dynamics.
• This approach facilitates accurate calculation of conformational entropy, aiding in protein folding and function studies.