A Search for Energy Minimized Sequences of Proteins

In this paper, we present numerical evidence that supports the notion of minimization in the sequence space of proteins for a target conformation. We use the conformations of the real proteins in the Protein Data Bank (PDB) and present computationally efficient methods to identify the sequences with minimum energy. We use edge-weighted connectivity graph for ranking the residue sites with reduced amino acid alphabet and then use continuous optimization to obtain the energy-minimizing sequences. Our methods enable the computation of a lower bound as well as a tight upper bound for the energy of a given conformation. We validate our results by using three different inter-residue energy matrices for five proteins from protein data bank (PDB), and by comparing our energy-minimizing sequences with 80 million diverse sequences that are generated based on different considerations in each case. When we submitted some of our chosen energy-minimizing sequences to Basic Local Alignment Search Tool (BLAST), we obtained some sequences from non-redundant protein sequence database that are similar to ours with an E-value of the order of 10-7. In summary, we conclude that proteins show a trend towards minimizing energy in the sequence space but do not seem to adopt the global energy-minimizing sequence. The reason for this could be either that the existing energy matrices are not able to accurately represent the inter-residue interactions in the context of the protein environment or that Nature does not push the optimization in the sequence space, once it is able to perform the function

How Protein Stability and New Functions Trade Off

Numerous studies have noted that the evolution of new enzymatic specificities is accompanied by loss of the protein's thermodynamic stability (ΔΔG), thus suggesting a tradeoff between the acquisition of new enzymatic functions and stability. However, since most mutations are destabilizing (ΔΔG>0), one should ask how destabilizing mutations that confer new or altered enzymatic functions relative to all other mutations are. We applied ΔΔG computations by FoldX to analyze the effects of 548 mutations that arose from the directed evolution of 22 different enzymes. The stability effects, location, and type of function-altering mutations were compared to ΔΔG changes arising from all possible point mutations in the same enzymes. We found that mutations that modulate enzymatic functions are mostly destabilizing (average ΔΔG = +0.9 kcal/mol), and are almost as destabilizing as the “average” mutation in these enzymes (+1.3 kcal/mol). Although their stability effects are not as dramatic as in key catalytic residues, mutations that modify the substrate binding pockets, and thus mediate new enzymatic specificities, place a larger stability burden than surface mutations that underline neutral, non-adaptive evolutionary changes. How are the destabilizing effects of functional mutations balanced to enable adaptation? Our analysis also indicated that many mutations that appear in directed evolution variants with no obvious role in the new function exert stabilizing effects that may compensate for the destabilizing effects of the crucial function-altering mutations. Thus, the evolution of new enzymatic activities, both in nature and in the laboratory, is dependent on the compensatory, stabilizing effect of apparently “silent” mutations in regions of the protein that are irrelevant to its function

Tryptophanyl-tRNA synthetase urzyme: A model to recapitulate molecular evolution and investigate intramolecular complementation

We substantiate our preliminary description of the class I tryptophanyl-tRNA synthetase minimal catalytic domain with details of its construction, structure, and steady-state kinetic parameters. Generating that active fragment involved deleting 65% of the contemporary enzyme, including the anticodon-binding domain and connecting peptide 1, CP1, a 74-residue internal segment from within the Rossmann fold. We used protein design (Rosetta), rather than phylogenetic sequence alignments, to identify mutations to compensate for the severe loss of modularity, thus restoring stability, as evidenced by renaturation described previously and by 70-ns molecular dynamics simulations. Sufficient solubility to enable biochemical studies was achieved by expressing the redesigned Urzyme as a maltose-binding protein fusion. Michaelis-Menten kinetic parameters from amino acid activation assays showed that, compared with the native full-length enzyme, TrpRS Urzyme binds ATP with similar affinity. This suggests that neither of the two deleted structural modules has a strong influence on ground-state ATP binding. However, tryptophan has 103 lower affinity, and the Urzyme has comparably reduced specificity relative to the related amino acid, tyrosine. Molecular dynamics simulations revealed how CP1 may contribute significantly to cognate amino acid specificity. As class Ia editing domains are nested within the CP1, this finding suggests that this module enhanced amino acid specificity continuously, throughout their evolution. We call this type of reconstructed protein catalyst an Urzyme (Ur prefix indicates original, primitive, or earliest). It establishes a model for recapitulating very early steps in molecular evolution in which fitness may have been enhanced by accumulating entire modules, rather than by discrete amino acid sequence changes. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.link_to_OA_fulltex

Incorporation of Noncanonical Amino Acids into Rosetta and Use in Computational Protein-Peptide Interface Design

<div><p>Noncanonical amino acids (NCAAs) can be used in a variety of protein design contexts. For example, they can be used in place of the canonical amino acids (CAAs) to improve the biophysical properties of peptides that target protein interfaces. We describe the incorporation of 114 NCAAs into the protein-modeling suite Rosetta. We describe our methods for building backbone dependent rotamer libraries and the parameterization and construction of a scoring function that can be used to score NCAA containing peptides and proteins. We validate these additions to Rosetta and our NCAA-rotamer libraries by showing that we can improve the binding of a calpastatin derived peptides to calpain-1 by substituting NCAAs for native amino acids using Rosetta. Rosetta (executables and source), auxiliary scripts and code, and documentation can be found at (<a href="http://www.rosettacommons.org/">http://www.rosettacommons.org/</a>).</p> </div