Statistical theory for protein combinatorial libraries. Packing interactions, backbone flexibility, and the sequence...

Area of Science:

• Computational biology
• Protein structure and folding
• Bioinformatics

Background:

• Protein folding is complex, influenced by sequence and structure.
• Designing novel folding sequences experimentally is challenging due to vast sequence space.
• Computational approaches are needed to guide protein engineering and folding studies.

Purpose of the Study:

• To develop a quantitative computational theory for designing and interpreting protein folding experiments.
• To identify sequence properties compatible with a specific protein backbone structure.
• To predict amino acid likelihoods at specific positions within a protein structure.

Main Methods:

• A statistically based, computational approach was developed.
• Atom-based protein side-chain conformations were incorporated.
• Calculations were performed on similar backbone structures to ensure robustness.
• An environmental energy term was introduced to model hydrophobic effects.

Main Results:

• The method predicts the likelihood of each amino acid at preselected positions, rather than specific sequences.
• It quantifies sequence space characteristics for a given structure.
• Hydrophobic effects were effectively modeled using an environmental energy term.
• Calculations for the immunoglobulin light chain-binding domain of protein L showed favorable agreement with experimental data.

Conclusions:

• The developed computational theory aids in designing and interpreting combinatorial protein folding experiments.
• The method provides a way to understand sequence-structure relationships and predict compatible amino acid sequences.
• This approach can accelerate the discovery of novel protein folding sequences and the engineering of proteins with desired structures.