MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

<div><p>Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.</p></div

Comparison of backbone ensemble generation methods.

<p><b>(a)</b> Experimental specificity profiles. <b>(b)</b> MFPred on FastRelax backbone ensemble. The <i>p</i>-value of the JSD for a given position is represented by the color of the square under that position; white denotes a <i>p</i>-value > 0.5 and dark blue denotes a <i>p</i>-value of 0. A given circle to the right of a profile represents the cosine similarity (white) and AUC (black) of that profile. The ROC plots beneath each profile depict the SSAL calculation <i>via</i> the experimental ROC (blue) and predicted ROC (red) with their respective AUC values. <b>(c)</b> MFPred on FlexPepDock backbone ensemble. <b>(d)</b> MFPred on Backrub backbone ensemble.</p

Generalize MFPred to PRD benchmark.

<p><b>(a)</b> Experimental specificity profiles. <b>(b)</b> MFPred prediction. The <i>p</i>-value of the JSD for a given position is represented by the color of the square under that position; white denotes a <i>p</i>-value > 0.5 and dark blue denotes a <i>p</i>-value of 0. A given circle to the right of a profile represents the cosine similarity (white) and AUC (black) of that profile. For the PDZ domain, prediction was performed at a kT of 0.6, which was found to be optimal for PDZ domains.</p

MFPred workflow.

<p>MFPred input is a backbone ensemble of a protein/peptide complex, which is generated from a protein structure from the PDB (1CKA here) as described in Methods. For each backbone, Rosetta pre-calculates the interaction graph, which stores intrinsic rotamer one-body energies on the vertices (blue circles) and matrices of rotamer-rotamer two-body energies on the edges (black lines). A probabilities matrix (P) is initialized. Mean-field energies (E) are calculated using the interaction graph and P, and a new matrix, P’ is generated from E. If P’ is equal to P, convergence has been reached. If not, the process is repeated by updating P with a combination of P and P’. Once convergence is reached, the final energies matrix and probabilities matrix is used to generate the Boltzmann weights of each backbone position, which is then used to average all the backbone specificity profiles together. This specificity profile is divided by the background specificity profile to reach the final predicted specificity profile.</p

Results of all methods—MFPred (MF), sequence_tolerance (ST), and pepspec (PS)—on variously-sized backbone ensembles.

<p>Results of all methods—MFPred (MF), sequence_tolerance (ST), and pepspec (PS)—on variously-sized backbone ensembles.</p

Number of sequences vs. accuracy and information for methods of profile prediction.

<p><b>(a)-(d)</b> Number of sequences vs. accuracy for TEV, HCV, GrB, and HIV, respectively. Number of sequences is varied over 1-5-10-All experimentally derived sequences, which is different for each protease. <b>(e)-(h)</b> Number of sequences vs. information content (i.e. shape of profile) difference for TEV, HCV, GrB, and HIV, respectively. Information difference is equal to the predicted bits minus the experimental bits. An information difference that is close to zero approximates the experimental information content well; a highly positive information difference indicates a more peaked predicted than experimental profile while a highly negative information difference denotes a flatter predicted than experimental profile.</p

Proof-of-concept for design.

<p><b>Changes in specificity profile upon granzyme B protease mutation are recapitulated by MFPred. (a)</b> Experimental (bold) specificity (average of Harris et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005614#pcbi.1005614.ref046" target="_blank">46</a>] and Ruggles et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005614#pcbi.1005614.ref047" target="_blank">47</a>]) and predicted P3 specificity for WT granzyme B protease. <b>(b)-(c)</b>, WT granzyme B protease structure. <b>(d)</b> R192E granzyme B protease active site. <b>(e)</b> Experimental specificity (bold) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005614#pcbi.1005614.ref046" target="_blank">46</a>] and predicted P3 specificity for R192E granzyme B protease. <b>(f)</b> R192E/N218A granzyme B protease active site. <b>(g)</b> Experimental specificity (bold) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005614#pcbi.1005614.ref047" target="_blank">47</a>] and predicted P3 specificity for R192E/N218A granzyme B protease.</p

Substrates for proteases and PRDs.

<p>Substrates for proteases and PRDs.</p

Computational Design of a Photocontrolled Cytosine Deaminase

There is growing interest in designing spatiotemporal control over enzyme activities using noninvasive stimuli such as light. Here, we describe a structure-based, computation-guided predictive method for reversibly controlling enzyme activity using covalently attached photoresponsive azobenzene groups. Applying the method to the therapeutically useful enzyme yeast cytosine deaminase, we obtained a ∼3-fold change in enzyme activity by the photocontrolled modulation of the enzyme’s active site lid structure, while fully maintaining thermostability. Multiple cycles of switching, controllable in real time, are possible. The predictiveness of the method is demonstrated by the construction of a variant that does not photoswitch as expected from computational modeling. Our design approach opens new avenues for optically controlling enzyme function. The designed photocontrolled cytosine deaminases may also aid in improving chemotherapy approaches that utilize this enzyme

Design and Evolution of a Macrocyclic Peptide Inhibitor of the Sonic Hedgehog/Patched Interaction

The hedgehog (Hh) signaling pathway plays a central role during embryonic development, and its aberrant activation has been implicated in the development and progression of several human cancers. Major efforts toward the identification of chemical modulators of the hedgehog pathway have yielded several antagonists of the GPCR-like smoothened receptor. In contrast, potent inhibitors of the sonic hedgehog/patched interaction, the most upstream event in ligand-induced activation of this signaling pathway, have been elusive. To address this gap, a genetically encoded cyclic peptide was designed based on the sonic hedgehog (Shh)-binding loop of hedgehog-interacting protein (HHIP) and subjected to multiple rounds of affinity maturation through the screening of macrocyclic peptide libraries produced in <i>E. coli</i> cells. Using this approach, an optimized macrocyclic peptide inhibitor (HL2-m5) was obtained that binds Shh with a <i>K</i>_D of 170 nM, which corresponds to a 120-fold affinity improvement compared to the parent molecule. Importantly, HL2-m5 is able to effectively suppress Shh-mediated hedgehog signaling and Gli-controlled gene transcription in living cells (IC₅₀ = 230 nM), providing the most potent inhibitor of the sonic hedgehog/patched interaction reported to date. This first-in-class macrocyclic peptide modulator of the hedgehog pathway is expected to provide a valuable probe for investigating and targeting ligand-dependent hedgehog pathway activation in cancer and other pathologies. This work also introduces a general strategy for the development of cyclopeptide inhibitors of protein–protein interactions