Analyzing site heterogeneity during protein evolution.

New computational models of the kinetics of natural site substitutions in proteins are described based on the underlying physical chemical properties of the amino acids. The corresponding reduction in the number of adjustable parameters allows us to analyze site-heterogeneity. Applying this evolutionary model to various data sets allows us to identify the important factors constraining molecular evolution, providing insight into the relationship between amino acid properties and protein structure.

ZF5, which is a Kruppel-type transcriptional repressor, requires the zinc finger domain for self-association.

ZF5, which we have cloned as a transcriptional repressor on the mouse c-myc promoter, has the POZ domain at the amino-terminus and the Kruppel-type zinc finger domain at the carboxy-terminus. In this report, we showed that ZF5 has two contradictory functions in transcription: activation of human immunodeficiency virus (HIV) promoter and repression of the HSV thymidine kinase (TK) promoter. The POZ domain contributed to the repressor activity, whereas the active function resulted from the DNA-binding ability of the zinc finger domain.

Using physical-chemistry-based substitution models in phylogenetic analyses of HIV-1 subtypes.

HIV-1 subtype phylogeny is investigated using a previously developed computational model of natural amino acid site substitutions. This model, based on Boltzmann statistics and Metropolis kinetics, involves an order of magnitude fewer adjustable parameters than traditional substitution matrices and deals more effectively with the issue of protein site heterogeneity. When optimized for sequences of HIV-1 envelope (env) proteins from a few specific subtypes, our model is more likely to describe the evolutionary record for other subtypes than are methods using a single substitution matrix, even a matrix optimized over the same data.

Major structural determinants of transmembrane proteins identified by principal component analysis.

We identify amino acid characteristics important in determining the secondary structures of transmembrane proteins, and compare them with characteristics important for cytoplasmic proteins. Using information derived from multiple sequence alignments, we perform a principal component analysis (PCA) to identify the directions in the 20-dimensional amino acid frequency space that comprise the most variance within each protein secondary structure. These vectors represent the important position-specific properties of the amino acids for coils, turns, beta sheets, and alpha helices.

Models of natural mutations including site heterogeneity.

New computational models of natural site mutations are developed that account for the different selective pressures acting on different locations in the protein. The number of adjustable parameters is greatly reduced by basing the models on the underlying physical-chemical properties of the amino acids. This allows us to use our method on small data sets built of specific protein types.