Predicting the structure of protein cavities created by mutation

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Protein engineering relies on understanding how mutations affect protein structure and stability.
• Predicting the structural consequences of mutations, particularly cavity formation, is crucial for designing novel proteins.

Purpose of the Study:

• To develop and validate an efficient computational strategy for predicting the structural outcome of mutations that create cavities in proteins.
• To assess the general applicability of the developed minimization strategy across different proteins and mutation types.

Main Methods:

• Modeled mutant protein structures using energy minimization techniques on virtual structures derived from wild-type coordinates.
• Employed an unconstrained pathway, all-atom representation, and steepest descent minimization for structure modeling.
• Validated the method on T4 lysozyme, barnase, and cytochrome c peroxidase mutants with known crystal structures.

Main Results:

• The optimized minimization strategy accurately predicted the structures of various mutants, with low root mean square deviation (r.m.s.d.) from experimental crystal structures (average 0.33 +/- 0.25 Å).
• The method showed high accuracy for both cavity-expanding and cavity-collapsing mutations, with only one outlier (L121A).
• Protein cavity structures generated by mutation can be confidently simulated by energy minimization.

Conclusions:

• The developed energy minimization strategy is a reliable tool for predicting the fate of protein cavities created by mutation.
• This computational approach assists in the efficient design of proteins by accurately simulating structural changes.
• The method's robustness across different mutation types and proteins highlights its utility in protein engineering and design.