[Show abstract][Hide abstract] ABSTRACT: Identification of residues responsible for functional specificity in enzymes is a challenging and important problem in protein chemistry. Active site residues are generally easy to identify, but residues outside the active site are also important to catalysis and their identities and roles are more difficult to determine. We report a method based on analysis of multiple sequence alignments, embodied in our program Janus, for predicting mutations required to interconvert structurally related but functionally distinct enzymes. Conversion of aspartate aminotransferase into tyrosine aminotransferase is demonstrated and compared to previous efforts. Incorporation of 35 predicted mutations resulted in an enzyme with the desired substrate specificity but low catalytic activity. A single round of DNA back-shuffling with wild type aspartate aminotransferase on this variant generated mutants with tyrosine aminotransferase activities better than those previously realized from rational design or directed evolution. Methods such as this, coupled with computational modeling, may prove invaluable in furthering our understanding of enzyme catalysis and engineering.
[Show abstract][Hide abstract] ABSTRACT: While information from homologous structures plays a central role in X-ray structure determination by molecular replacement, such information is rarely used in NMR structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein structures of up to 225 residues by combining (1)H(N), (13)C, and (15)N backbone and (13)Cβ chemical shift data, distance restraints derived from homologous structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known structures and blind targets show that the method can accurately model protein structures, even with very remote homology information, to a backbone rmsd of 1.2-1.9 Å relative to the conventional determined NMR ensembles and of 0.9-1.6 Å relative to X-ray structures for well-defined regions of the protein structures. This approach facilitates the accurate modeling of protein structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.
Proceedings of the National Academy of Sciences 06/2012; 109(25):9875-80. DOI:10.1073/pnas.1202485109 · 9.67 Impact Factor