title: Protein engineering and stabilization from sequence statistics: variation and covariation analysis.
abstract: The concepts of consensus and correlation in multiple sequence alignments (MSAs) have been used in the past to understand and engineer proteins. However, there are multiple ways of acquiring MSA databases and also numerous mathematical metrics that can be applied to calculate each of the parameters. This chapter describes an overall methodology that we have chosen to employ for acquiring and statistically analyzing MSAs. We have provided a step-by-step protocol for calculating relative entropy and mutual information metrics and describe how they can be used to predict mutations that have a high probability of stabilizing a protein. This protocol allows for flexibility for modification of formulae and parameters without using anything more complicated than Microsoft Excel. We have also demonstrated various aspects of data analysis by carrying out a sample analysis on the BPTI-Kunitz family of proteins and identified mutations that would be predicted to stabilize this protein based on consensus and correlation values.
authors: Department of Chemistry, The Ohio State University, Columbus, Ohio, USA.
authors: Venuka
authors: V
authors: Durani
authors: Thomas J
authors: TJ
authors: Magliery
mesh_terms: D000595:Amino Acid Sequence; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment
chemical_list: D011506:Proteins