Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.

Area of Science:

• Computational Biology
• Bioinformatics
• Evolutionary Biology

Background:

• Protein sequence diversity reflects multiple evolutionary solutions for function.
• Statistical models can capture protein sequence patterns from large alignments.
• Covariance between amino acid positions suggests coevolution driven by constraints.

Purpose of the Study:

• To review and discuss coevolution-based modeling approaches for protein sequences.
• To compare Direct Coupling Analysis (DCA)-like Potts models and Statistical Coupling Analysis (SCA).
• To highlight the utility of these models for understanding protein structure and function.

Main Methods:

• Analysis of amino acid frequencies in large multiple sequence alignments.
• Implementation and comparison of Potts models (DCA-like) and Statistical Coupling Analysis (SCA).
• Review of recent advancements in gene synthesis and functional selection.

Main Results:

• Potts models identify local physical contacts within protein structures.
• SCA identifies larger networks of evolutionarily coupled residues.
• Both methods reveal important features of protein sequence and structure.

Conclusions:

• Coevolution-based models offer powerful insights into protein sequence-function relationships.
• Advances in experimental techniques necessitate benchmarking these models for prediction and design.
• Future work should focus on quantitative function prediction and de novo protein design.