Molecular structure matching by simulated annealing. I. A comparison between different cooling schedules

Area of Science:

• Computational chemistry
• Bioinformatics
• Structural biology

Background:

• Molecular matching is crucial for understanding protein interactions and drug design.
• Traditional methods struggle with the combinatorial complexity of assigning atom correspondences.
• Simulated annealing offers a probabilistic approach to optimization problems.

Purpose of the Study:

• To apply simulated annealing theory to address molecular matching challenges.
• To evaluate different cooling schedules within the simulated annealing framework.
• To demonstrate the scalability of the approach for larger molecular structures.

Main Methods:

• Utilized simulated annealing with linear, exponential, and dynamic cooling schedules.
• Defined the objective function as the sum of elements in the difference distance matrix.
• Employed continual reordering of one molecule to explore conformational space.
• Tested algorithms on random coordinate data and two related protein structures.

Main Results:

• Simulated annealing effectively resolved combinatorial problems in atom correspondence.
• The approach demonstrated successful optimization for molecular matching up to 150 atoms.
• Performance was validated using both synthetic and real-world protein data.

Conclusions:

• Simulated annealing is a robust method for tackling complex molecular matching problems.
• The developed algorithms provide an efficient solution for large-scale molecular comparisons.
• This approach has significant implications for structural bioinformatics and computational drug discovery.