The Designability Hypothesis and Protein Evolution

Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.
Protein and Peptide Letters (Impact Factor: 1.07). 03/2005; 12(2):111-6. DOI: 10.2174/0929866053005881
Source: PubMed


The usage of protein folds in nature is known to be non-uniform: a few folds are used often, while most others are used relatively rarely. What makes one fold more successful than another? The designability explanation, which posits that successful folds have an exponentially larger number of compatible sequences, is critically reviewed, and compared with other structural and functional explanations. It is argued that designability is one component of fold fitness, but most likely not a dominant one.

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    • "A protein's designability is defined as the total number of amino acid sequences that fold into the given structure (Li et al. 1996; Kussell 2005). Designability varies widely among structures. "
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    ABSTRACT: The density of contacts or the fraction of buried sites in a protein structure is thought to be related to a protein's designability, and genes encoding more designable proteins should evolve faster than other genes. Several recent studies have tested this hypothesis but have found conflicting results. Here, we investigate how a gene's evolutionary rate is affected by its protein's contact density, considering the four species Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We find for all four species that contact density correlates positively with evolutionary rate, and that these correlations do not seem to be confounded by gene expression level. The strength of this signal, however, varies widely among species. We also study the effect of contact density on domain evolution in multidomain proteins and find that a domain's contact density influences the domain's evolutionary rate. Within the same protein, a domain with higher contact density tends to evolve faster than a domain with lower contact density. Our study provides evidence that contact density can increase evolutionary rates, and that it acts similarly on the level of entire proteins and of individual protein domains.
    Journal of Molecular Evolution 05/2008; 66(4):395-404. DOI:10.1007/s00239-008-9094-4 · 1.68 Impact Factor
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    • "Natural proteins perform a startling diversity of biological functions, but comprise a miniscule fraction of the theoretical sequence–structure space that polypeptides might occupy.1–4 The goal of protein design is to identify new free-energy minima in this sequence–structure landscape so as to expand the functional repertoire of polypeptides beyond that observed in nature.5–9 "
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    ABSTRACT: Recent efforts to design de novo or redesign the sequence and structure of proteins using computational techniques have met with significant success. Most, if not all, of these computational methodologies attempt to model atomic-level interactions, and hence high-resolution structural characterization of the designed proteins is critical for evaluating the atomic-level accuracy of the underlying design force-fields. We previously used our computational protein design protocol RosettaDesign to completely redesign the sequence of the activation domain of human procarboxypeptidase A2. With 68% of the wild-type sequence changed, the designed protein, AYEdesign, is over 10 kcal/mol more stable than the wild-type protein. Here, we describe the high-resolution crystal structure and solution NMR structure of AYEdesign, which show that the experimentally determined backbone and side-chains conformations are effectively superimposable with the computational model at atomic resolution. To isolate the origins of the remarkable stabilization, we have designed and characterized a new series of procarboxypeptidase mutants that gain significant thermodynamic stability with a minimal number of mutations; one mutant gains more than 5 kcal/mol of stability over the wild-type protein with only four amino acid changes. We explore the relationship between force-field smoothing and conformational sampling by comparing the experimentally determined free energies of the overall design and these focused subsets of mutations to those predicted using modified force-fields, and both fixed and flexible backbone sampling protocols.
    Journal of Molecular Biology 04/2007; 366(4):1209-21. DOI:10.1016/j.jmb.2006.11.080 · 4.33 Impact Factor
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