Performance of predictive models using different sets of residue interactions.

<p>The predictive models were optimized using the combined set of interactions and evaluated with nested cross-validation as described in the Methods. The residue interactions used to describe the coiled-coil interaction were varied as descried in the text. We also applied recursive feature elimination to find a smaller subset of key features that retained good prediction performance. The reduced number of features, and the model performance with this number of features, are given in parentheses.</p><p>Performance of predictive models using different sets of residue interactions.</p

Benchmark of existing methods for predicting coiled-coil specificity.

<p>Experimentally determined K_d values were compared to predicted scores using two tests: the Pearson correlation coefficient (R) and the area under the curve (AUC). The Pearson correlation coefficient is reported only for interactions with 1 nM < K_d < 5,000 nM. In the AUC test, the interactions were divided into two classes: strong interactions (K_d < 250 nM) and weak/non-interactions (K_d ≥ 5,000 nM). The number of interactions used in each test is given in parentheses at the top of each column.</p><p>^a Vinson and colleagues measured coupling energies for pairs of residues in <b>a_i</b>-<b>a<i>'</i>_i</b> and <b>g_i</b>-<b>e<i>'</i>_i+1</b> positions. An additional empirical coupling energy of -2 kcal/mol for Leu-Leu interaction at <b>d_i</b>-<b>d<i>'</i>_i</b> positions was added to account for these strongly stabilizing interactions, as in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref016" target="_blank">16</a>].</p><p>Benchmark of existing methods for predicting coiled-coil specificity.</p

Designed bZIP-binding peptides inhibit interactions of native bZIP dimers.

<p>(A) JUN-d1 inhibits the interaction of 10 nM JUN with 50 nM FOS with an IC₅₀ of 245 nM at 37°C. (B) XBP1-d1 inhibits the interaction of 10 nM XBP1 with 50 nM CREBZF with an IC₅₀ of 136 nM at 23°C. (C) ATF4-d1 inhibits the interaction of 10 nM ATF4 with 200 nM FOS with an IC₅₀ of 279 nM at 37°C. The dissociation constants at the indicated temperatures are K_d ≤ 1 nM for FOS-JUN, K_d ≤1 nM for XBP1-CREBZF, and K_d = 60 nM for ATF4-FOS, according to [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref018" target="_blank">18</a>]. Fluorescence intensities were measured at both 37°C and 23°C, and the IC₅₀ value was fit and reported for the highest temperature that gave a well-defined lower baseline. The target bZIP in each complex was labeled with the FRET donor (fluorescein), the partner was labeled with the TAMRA FRET acceptor, and the design was unlabeled. Fluorescence emission was monitored at 525 nm and is reported in relative fluorescence units.</p

Specificity profiles of four designs tested at 37°C.

<p>The designed binders interact strongly with their targets as rhodamine-labeled peptides (bold red circles). Thin red circles show interactions with other bZIPs in the same family as the target. The ATF5-d1 design bound more tightly to ATF4 (thin red circle) than to ATF5. The designed proteins do not form strong homodimers (black bars), and there are large specificity gaps between the design/target interactions and design/off-target interactions (white and grey bars, colored according to target-off-target sequence identity at <b>a</b>, <b>d</b>, <b>e</b> and <b>g</b> positions). All K_d values are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.s011" target="_blank">S5</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.s018" target="_blank">S12</a> Tables.</p

Molecular weights of designed bZIP complexes determined by analytical ultracentrifugation.

<p>Molecular weights of designed bZIP complexes determined by analytical ultracentrifugation.</p

Interpretation of the model weights.

<p>Optimized weights correlate with experimentally measured coupling energies reported in the literature: (A) <b>g</b>-<b>e<i>'</i></b><i>R</i>_<i>ge</i> = 0.94 (<i>p</i> = 5x10^–5) and (B) <b>a</b>-<b>a<i>'</i></b><i>R</i>_<i>aa</i> = 0.89 (<i>p</i> = 6x10^–4) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref005" target="_blank">5</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref022" target="_blank">22</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref023" target="_blank">23</a>]. (C, D) Examples illustrating triplets of residues that are predicted to be stabilizing (C) or destabilizing (D) according to the derived model. PDB ID 4DMD was used to illustrate the triplets; the structure in panel (D) was obtained by modeling glutamic acid at position 20 using the SCWRL4 side-chain prediction program [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.ref057" target="_blank">57</a>].</p

Schematic representation of the model-building and design protocol used to generate selective bZIP-interacting peptides.

<p>(A) Experimentally determined dissociation constants for 4,549 bZIP interaction pairs were used as input to train a scoring model. (B) Weights corresponding to contributions from pairs and triplets of residues were fit to experimental binding data using a regression technique. The appropriate optimized weights can be summed to provide a predicted binding energy for two aligned bZIP coiled-coil sequences. (C) Binders were designed by using coiled-coil heptads as building blocks. Optimal combinations of heptads to construct tight-binding and selective designs were identified using integer linear programming in conjunction with the developed scoring function. (D) Designed sequences exhibit tight and selective binding to target bZIP coiled coils. Each square in the cartoon corresponds to a native human bZIP coiled coil, and cells are colored by the strength of interaction of each bZIP with the indicated designed peptide; darker shades correspond to stronger binding. Names of the bZIP proteins corresponding to each cell are given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004046#pcbi.1004046.s011" target="_blank">S5 Table</a>.</p

Binding of designed peptides to their bZIP targets.

<p>¹ These four designs were chosen for further characterization.</p><p>² A double mutant of XBP1-d2, in which serine at position <b>4e</b> and tyrosine at position <b>5a</b> were mutated to lysine and leucine, respectively.</p><p>³ Although ATF5 was used in the computational design, binding was tighter to ATF4, which shares 69.2% sequence identity with ATF5 in <b><i>a</i></b>, <b><i>d</i></b>, <b><i>e</i></b> and <b><i>g</i></b> positions.</p><p>Binding of designed peptides to their bZIP targets.</p

Detection of Alkynes via Click Chemistry with a Brominated Coumarin Azide by Simultaneous Fluorescence and Isotopic Signatures in Mass Spectrometry

Alkynes are a key component of click chemistry and used for a wide variety of applications including bioconjugation, selective tagging of protein modifications, and labeling of metabolites and drug targets. However, challenges still exist for detecting alkynes because most 1,2,3-triazole products from alkynes and azides do not possess distinct intrinsic properties that can be used for their facile detection by either fluorescence or mass spectrometry. To address this critical need, a novel brominated coumarin azide was used to tag alkynes and detect alkyne-conjugated biomolecules. This tag has several useful properties: first, it is fluorogenic and the click-chemistry products are highly fluorescent and quantifiable; second, its distinct isotopic pattern facilitates identification by mass spectrometry; and third, its click-chemistry products form a unique pair of reporter ions upon fragmentation that can be used for the quick screening of data. Using a monoclonal antibody conjugated with alkynes, a general workflow has been developed and examined comprehensively