Dataset Information

Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging.

ABSTRACT: There is considerable interest in generating proteins with both high stability and high activity for biomedical and industrial purposes. One approach that has been used successfully to increase the stability of linear repeat proteins is consensus design. It is unclear the extent over which the consensus design approach can be used to produce folded and hyperstable proteins, and importantly, whether such stabilized proteins would retain function. Here we extend the consensus strategy to design a globular protein. We show that a consensus-designed homeodomain (HD) sequence adopts a cooperatively folded homeodomain structure. The unfolding free energy of the consensus-HD is 5 kcal·mol-1 higher than that of the naturally occurring engrailed-HD from Drosophila melanogaster. Remarkably, the consensus-HD binds the engrailed-HD cognate DNA in a similar mode as the engrailed-HD with approximately 100-fold higher affinity. 15N relaxation studies show a decrease in ps-ns backbone dynamics in the free state of consensus-HD, suggesting that increased affinity is not a result of increased plasticity. In addition to demonstrating the potential for consensus design of globular proteins with increased stability, these results demonstrate that greatly stabilized proteins can bind cognate substrates with increased affinities, showing that high stability is compatible with function.

SUBMITTER: Tripp KW

PROVIDER: S-EPMC5617789 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging.

Tripp Katherine W KW Sternke Matt M Majumdar Ananya A Barrick Doug D

Journal of the American Chemical Society 20170328 14

There is considerable interest in generating proteins with both high stability and high activity for biomedical and industrial purposes. One approach that has been used successfully to increase the stability of linear repeat proteins is consensus design. It is unclear the extent over which the consensus design approach can be used to produce folded and hyperstable proteins, and importantly, whether such stabilized proteins would retain function. Here we extend the consensus strategy to design a ...[more]

PMID: 28326770

Similar Datasets

Project description:Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity, and showing for the first time that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success. Keywords: Mouse homeodomain protein binding microarrays 178 Protein binding microarray (PBM) experiments of mouse homeodomains were performed, with 10 proteins done in replicate. Briefly, the PBMs involved binding GST-tagged mouse homeodomains to custom-designed, double-stranded 44K Agilent microarrays in order to determine their sequence preferences. The method is described in Berger et al., Nature Biotechnology 2006. A key feature is that the microarrays are composed of de Bruijn sequences that contain each 10-base sequence once and only once, providing an evenly balanced sequence distribution. Individual de Bruijn sequences have different properties, including representation of gapped patterns. The array sequences as well as the primary array data are available via a EULA at http://the_brain.bwh.harvard.edu/pbms/webworks2/. Here we provide the data transformed into median intensities (after normalization and detrending of the original array data) for all 32,896 8-base sequences, Z-scores for these intensities, and E-scores. E-scores are a modified version of AUC, and describe how well each 8-mer ranks the intensities of the spots. In general the E-scores are slightly more reproducible than Z-scores, but contain less information about relative binding affinity. Additional experimental details are found in Berger et al., Nature Biotechnology 2006, Berger et al., Cell 2008, and the accompanying Supplementary information.

Project description:BackgroundThere is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information.Methodology/principal findingsBased on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-? binding peptide with K(d)?= 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive.Conclusions/significanceBy using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-? binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner.

Dataset Information

Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging.

Publications

Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets