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Protein kinase Cα (PKCα) belongs to the family of AGC kinases that phosphorylate multiple peptide substrates. Although the consensus sequence motif has been identified and used to explain substrate specificity for PKCα, it does not inform the structural basis of substrate-binding and kinase activity for diverse substrates phosphorylated by this kinase. The transient, dynamic, and unstructured nature of this protein–protein interaction has limited structural mapping of kinase–substrate interfaces. Here, using multiscale MD simulation-based predictions and FRET sensor-based experiments, we investigated the conformational dynamics of the kinase–substrate interface. We found that the binding strength of the kinase–substrate interaction is primarily determined by long-range columbic interactions between basic (Arg/Lys) residues located N-terminally to the phosphorylated Ser/Thr residues in the substrate and by an acidic patch in the kinase catalytic domain. Kinase activity stemmed from conformational flexibility in the region C-terminal to the phosphorylated Ser/Thr residues. Flexibility of the substrate–kinase interaction enabled an Arg/Lys two to three amino acids C-terminal to the phosphorylated Ser/Thr to prime a catalytically active conformation, facilitating phosphoryl transfer to the substrate. The structural mechanisms determining substrate binding and catalytic activity formed the basis of diverse binding affinities and kinase activities of PKCα for 14 substrates with varying degrees of sequence conservation. Our findings provide insight into the dynamic properties of the kinase–substrate interaction that govern substrate binding and turnover. Moreover, this study establishes a modeling and experimental method to elucidate the structural dynamics underlying substrate selectivity among eukaryotic kinases.
), which has the sensitivity to measure weak protein–protein interactions and is scalable to multiple substrates. Using SPASM measurements on 14 different substrate peptides (shown in supplemental Fig. S1) with PKCα, we showed that above a minimum-required binding affinity to induce kinase activity, the kinase-specific activity is inversely correlated with the substrate-binding affinity (
). This inverse correlation between the binding affinity and kinase activity toward substrates are shown in supplemental Fig. S1. Consequently, substrates with high-affinity but low activity outcompete their low-affinity but high activity counterparts, suggesting a simple biochemical mechanism for selective substrate phosphorylation in cells. However, our FRET sensor measurements lack the structural resolution to dissect the conformational dynamics of the interaction, and identify specific residues that modulate binding affinity and activity. Given that the region of the substrate peptide that binds to PKCα is largely disordered, multiscale MD simulation methods are ideally suited to study the ensemble of conformations that the substrate peptides adopt when they bind to PKCα.
The generalized Newton-Euler inverse mass operator (GNEIMO) internal coordinate molecular dynamics method has several advanced multiresolution features that are essential for the enriched conformational sampling of disordered substrate binding to PKCα (
). In GNEIMO torsional MD, the helices/β-sheets in the catalytic domain are treated as a rigid body, whereas the loops and peptide-binding surfaces in the kinase and the entire substrate peptide are modeled as flexible torsions (see supplemental Fig. S2). Use of such a coarse grain model permits a broader range of conformational sampling of the kinase-substrate conformational ensemble. Here, we have used the GNEIMO coarse grain dynamic model to perform torsional MD simulations of the binding of 14 peptide substrates (shown in supplemental Table S1) to PKCα. The predictions of the residues in the substrate that contribute to binding and kinase activity from the GNEIMO MD model have been tested extensively with SPASM FRET sensors and kinase activity assays to gain insight into the structural dynamics of kinase–substrate interactions. The schematic of the workflow combining the GNEIMO with SPASM FRET methods to probe the dynamics of the kinase peptide substrate interactions is shown in Fig. 1. Details of this workflow are described under “Experimental procedures.”
Using this combination of computational and experimental methods, we find that the substrate region N-terminal to the phosphorylated serine residue determines the kinase–substrate interaction affinity. In contrast, high kinase activity requires a high degree of conformation flexibility within the substrate region C-terminal to the phosphorylated serine residue. High activity substrates show greater conformational flexibility within the kinase active site compared with their low activity counterparts. An arginine residue located either two or three amino acids C-terminal to the phosphorylated serine residue, optimally positions the phosphorylated serine for catalysis. Taken together, our study reveals two distinct mechanisms that dictate substrate-binding affinity and activity, whereas emphasizing the importance of substrate conformational dynamics in determining the relative activity of a kinase for distinct substrates.
The residues in the N terminus of the peptide substrates contribute to substrate binding in PKCα
We have studied the dynamics of 14 peptide substrates binding to the catalytic domain of PKCα using GNEIMO coarse grain MD simulations followed by all-atom MD simulations. Fig. 2A shows the binding groove of the tight binding peptide p12 that shows the lowest kinase activity among the 14 peptides (see supplemental Fig. S1 for the relative binding affinities and kinase activity of all 14 substrate peptides). Our MD simulation results indicate that an acidic patch of residues located in the substrate-binding groove of PKCα make strong electrostatic interactions with the basic residues in the N terminus of the peptide substrates (Fig. 2A and supplemental Fig. S3). In contrast, the residues in the kinase that interact with the C terminus of the peptide are hydrophobic, resulting in a deep insertion of the peptide C terminus (Fig. 2A). We analyzed the residues in PKCα that make sustained (present in over 40% of the snapshots from MD simulations) salt bridge, hydrogen bond, or van der Waals contact with residues in the N terminus of the peptides. The acidic patch of residues consisting of Asp383, Asp470, Asp506, Glu533, and Glu544, Glu548, and Asp542 (shown in spheres and sticks in Fig. 2A) make close contact with the N terminus residues of the peptide substrates (see the electrostatic surface shown in supplemental Fig. S3). Many of these acidic residues listed above are conserved across the PKC family as shown in supplemental Fig. S4.
The stronger electrostatic interactions of the peptide N terminus compared with the C terminus suggested that this region contributes substantially to the peptide-binding energy. To test this possibility, we calculated the average interaction energies, averaged across the three all-atom MD trajectories for each peptide substrate and compared them to previous FRET measurements (
). The calculated interaction energies between the N terminus residues of the peptide substrate with the catalytic domain of PKCα correlate linearly with the measured binding affinity of kinase–substrate peptide interaction (
) with a R2 = 0.8, as shown in Fig. 2B. However, the calculated interaction energies of the entire peptide substrate with the catalytic domain of PKCα did not show a good correlation (R2 = 0.48; supplemental Fig. S5).
The structural ensemble for the peptide–PKC interface interactions were generated using GNEIMO-MD simulations followed by all-atom MD simulations as described under “Experimental procedures.” To test these predicted peptide–kinase interactions, we proposed mutations in the poor binding and high activity peptides such as p1, p2, p4, and p6 to improve their binding affinity to the kinase without affecting the kinase activity toward these peptides. We calculated the average interaction energy of each residue in every peptide substrate with the kinase. Using the residues that contribute weakly to the substrate–kinase interaction, we predicted mutations in these four weak binding peptides and these mutants were expressed as FRET sensors along with the catalytic domain of PKCα (see “Experimental procedures”). Residue numbering on the peptide substrates is from the N to C terminus with the phosphorylated Ser/Thr numbered as position 9 for all peptide substrates (see supplemental Table S1). Each of these mutations showed a significant increase in FRET ratio, suggesting an increase in binding affinity of the peptide for the catalytic domain (Fig. 2D). It is clearly seen that mutation of N terminus residues to a basic residue such as Arg increases affinity for the acidic patch of PKCα and therefore improves their binding.
As seen in Fig. 2E, most of the mutations in the N terminus residues of p1, p2, p4, and p6 showed a small but not significant increase in kinase activity. This suggests that the kinase activity on these 14 peptide substrates may be governed additionally by other mechanisms. Interestingly, the kinase–substrate-binding affinity correlates linearly with a recognition metric derived based on the consensus motif (
) characterized the substrate recognition motif of PKCα using a degenerate peptide library phosphorylated by the kinase. For each location relative to the phosphorylated serine/threonine, they reported the fold-enrichment in substrate phosphorylation with a specific amino acid compared with a degenerate sequence. With the assumption that the reported fold-enrichments are additive, we linearly combined these values for all conserved residues to arrive at a recognition metric. Our finding highlights the role of consensus motif and its correlation to the peptide substrate-binding affinity correlation to the peptide substrate-binding affinity and not to the kinase activity. We examined further the properties of the dynamics of the peptide substrate that would correlate to the kinase activity. The peptide substrates with good kinase activity show an enrichment of catalytic conformations during the dynamics simulations.
) solved crystal structures of PKA that represent different snapshots of the kinase in the catalytic process of transferring the PO4 group, to show a plausible “catalytic conformation” of PKA. Such a catalytic conformation shows the Ser/Thr that gets phosphorylated in the peptide substrate close to the Asp166 (residue number as in PKA) as well as the γ-PO4 group of the ATP. The Asp166 is involved in abstracting the proton from the hydroxyl group of the pSer that enables the transfer of the PO4 group from ATP to pSer/Thr in the peptide substrate. We used this definition of the catalytic conformation to analyze the differences in the population of the catalytic conformation in the conformational ensemble derived from MD trajectories for the high activity peptides p1, p2, and p3 compared with the poor activity peptides p10, p11, and p12.
Fig. 3A shows a snapshot from the MD simulation trajectory of high activity peptide p2 that shows the shortest distance between the Ser in p2 and Asp466 and the γ-PO4 group of the ATP. This conformation is representative of a plausible catalytic conformation of PKCα as seen in our dynamics simulations. The oxygen atom of the Ser in the peptide is about 4 Å from both the Asp466 of PKCα and the γ-PO4 group of the ATP. We also observed that the Na+ ions used to neutralize charges in the MD simulations cluster around the PO4 groups of the ATP. The position of these Na+ ions is similar to the Ca2+ ions in the crystal structure (PDB code 4XW5) of the catalytic conformation of PKA (
). Fig. 3B shows the snapshot with the closest distance of the poor activity peptide p12 to Asp466 and γ-PO4 group of the ATP extracted from the MD simulations. As anticipated, the catalytic distances in this low activity peptide is longer than that of the good activity of p2, thus leading to lower kinase activity of this peptide substrate. We clustered the MD ensemble for peptides p1, p2, p3, p9, p11, and p12 by the two distances of the phosphorylated Ser/Thr to Asp466 and the γ-PO4 group of ATP as shown in Fig. 3C and supplemental Fig. S6. This population distribution shows that the highest population cluster for the good activity peptides p1, p2, and p3 is around shorter distances and is much tighter for high activity peptides compared with their low activity counterparts p9, p11, and p12 (Fig. 3C and supplemental Fig. S6). This suggests that peptides with high kinase activity are more likely to populate catalytic conformations compared with the low activity peptides as shown in Fig. 3D.
) show a rotamer flip in the χ1 angle of the phosphorylated Ser/Thr as shown in Fig. 3E. The side chain rotamer of the Ser/Thr in the peptide substrate flips its rotamer after the PO4 transfer to the Ser/Thr of the peptide. To verify if we could capture this rotamer flip in the pSer/Thr in the substrate, we performed MD simulations after transferring the PO4 group from ATP to the pSer/Thr in the substrate. To this end we extracted the best catalytic conformation with the shortest distance of Ser/Thr (OG) to the γ-PO4 of ATP as well as to Asp466 (OD), in the peptide p1 as detailed under ”Experimental procedures.“ We then transferred the PO4 group to the Ser/Thr of the peptide, leaving ADP in the ATP-binding site. We performed all-atom MD simulations on these two peptides bound to PKCα. We then examined the rotamer conformations of the Ser/Thr residue in the peptide substrate before and after phosphoryl transfer. Fig. 3F shows the rotamer flip of the χ1 angle of the Ser/Thr in peptide substrates before and after the phosphoryl transfer. The rotamer change leads to moving the side chain of Ser/Thr away from Asp466 (Fig. 3F). The repulsion between the PO4 group in pSer/Thr with Asp466 could trigger the rotamer flip in the pSer/Thr, and we speculate that this could be the initiating event for the peptide substrate to move away from the catalytic conformation before it dissociates from the kinase. In summary, our simulations show that high activity peptides show a higher population of the kinase–substrate conformations that are conducive to catalysis, with subsequent phosphoryl transfer leading to an expected change in rotamer of the pSer as seen in the crystal structures. Our dynamics simulations capture the conformational changes that lead to the catalysis of PO4 transfer.
Basic residues at either two or three positions C-terminal to the phosphorylated serine/threonine in the peptide substrate are required to elicit good kinase activity
Why do high activity peptides more frequently populate active conformations? Analysis of the MD simulation results showed that long range Coulombic attraction of the Arg/Lys, located two or three residues C-terminal to the phosphorylated residue (positions 11 and 12), to the γ-PO4 group of the ATP ushers the Ser/Thr that is adjacent to this residue closer to the ATP thereby priming phosphoryl transfer. Fig. 4A shows the plot of distance between the γ-PO4 group of ATP and the residues at positions (Ser) 9 and 11 in the peptide substrates p2 and p12 with time in nanoseconds. In the high activity peptide p2, the Arg11 in p2 gets close to the γ-PO4 group of ATP thereby ushering in its neighbor Ser at position 9 in the peptide substrate p2. Such a long range Coulombic attraction between Phe11 in peptide p12 and the γ-PO4 group of ATP is not possible in the low activity peptide p12. Fig. 4B shows a snapshot from the MD simulations demonstrating the proximity of the γ-PO4 group of ATP to Ser9 and Arg11 of peptide p2. To test this further using our kinase Glo assays, we mutated to Arg/Lys these positions in the low activity peptides p9, p10, and p12 (supplemental Fig. S1). Mutation of residues at positions 11 and 12 in low activity peptides p9, p10, and p12 to Arg or Lys shows improved activity in all the three peptides (Fig. 4C). Interestingly, peptide p2 has only two basic residues, Arg4 and Arg11, but exhibits high kinase activity. Mutagenesis of Arg11 to Ile is sufficient to completely abolish the activity of this peptide (Fig. 4C). Likewise, transfer of Arg to position 12 (R11I and G12R) retains partial peptide activity attesting to the importance of the long-range Coulomb interaction mediated by an Arg/Lys residue 2–3 amino acids C-terminal to the phosphorylated Ser/Thr in driving kinase-specific activity. These single point and double mutations to Arg in the peptides p2, p9, p10, and p12 do not change their FRET ratio and therefore their binding affinities, as shown in Fig. 4D.
High activity peptides exhibit greater conformational flexibility when bound to the kinase catalytic domain
As seen in supplemental Fig. S1, peptide substrates p10 and p13 show similar binding affinities but p13 shows significantly different kinase activity. What are the differences in the dynamics of p10 and p13 that explain the differences in their kinase activities? To examine the dynamics of the peptide substrate when bound to the kinase, we compared the root mean square fluctuation (r.m.s. fluctuation) of p10 and p13. The r.m.s. fluctuation for every residue in the peptide substrate reflects the flexibility of the peptide in the binding groove in PKCα. Supplemental Fig. S7 shows the population density of r.m.s. fluctuation for every residue in p10 and p13 when bound to PKCα during all-atom explicit MD simulations. The r.m.s. fluctuation is calculated with respect to the average structure derived from the MD trajectories as reference. Position 9 in the figure is the position of the Ser/Thr that gets phosphorylated by PKCα. Although residues N-terminal to the phosphorylated Ser/Thr display overlapping distributions of r.m.s. fluctuation, the C-terminal residues of p13 have a considerably broader distribution than p10. We examined the peptide substrates p4, p5, and p13 that exhibit similar kinase activity but substantial differences in binding affinity (supplemental Fig. S1; p4 > p5 > p13). These three peptides show similar flexibility (by r.m.s. fluctuation) for all the residues in the peptides (shown in supplemental Fig. S8). However, the interaction energies of the residues N terminus to pSer/Thr in these substrates correspond to the reported binding strengths of these peptides (shown in supplemental Table S2). Thus, higher flexibility in the region C-terminal to pSer/Thr in the peptide substrates corresponds to the better kinase activity.
The goal of this study is to provide mechanistic insights into the structural and dynamic features of the PKCα–peptide interface that contribute to the binding and kinase activity of 14 different peptide substrates. We identify that the basic residues (Arg/Lys) located N-terminal to the pSer/Thr in the peptide substrates contribute significantly to their binding affinity. The acidic residues Asp383, Asp470, Asp506, Glu533, and Glu544, Glu548, and Asp542 in PKCα form a negatively charged patch to embed the Arg/Lys-rich N terminus of the tight binding peptide substrates. This is in agreement with Nishikawa's metric (
) for consensus motif in the peptide sequences. However, the consensus motif metric does not correlate with the kinase activity toward these peptides. We observed that flexibility of the amino acid residues located C-terminal to the pSer/Thr in the peptides regulate the kinase activity. The more flexible the C terminus of the peptide, the better is its activity toward PKCα. The Arg/Lys positioned one or two residues from the pSer/Thr plays an important role in long-range attraction of ATP and threads the Ser/Thr close to the γ-phosphate of ATP and the catalytic residue Asp466 in PKCα. These dynamics lead to formation of catalytically competent conformations. As shown in Fig. 5, the peptide substrates that show high structural flexibility when bound to PKCα increase the proportion of conformations that position the serine/threonine residue in proximity of both the γ-PO4 and the aspartic acid (Asp466) that is important for proton abstraction. These findings explain the wide range of kinase-specific activity observed for a range of PKCα peptide substrates.
The strength of the kinase–substrate interaction as determined by the FRET ratio of our SPASM sensors correlates linearly with the computed interaction energies of the kinase with the N terminus of the substrate ensemble. Interestingly, our binding rather than activity measurements correlate with an average recognition metric derived from the previously reported substrate recognition motif for PKCα. Hence, our study shows that whereas the recognition motif can guide kinase–substrate selectivity, a high degree of conservation does not imply high activity. In fact, we have shown an inverse correlation between kinase activity and binding affinity, suggesting that beyond a threshold, increased conservation of the recognition motif may reduce rather than increase kinase activity. For instance, PTP1B has a much higher specific activity that EGFR despite more residues in the latter matching the consensus motif.
Structural studies of the kinase–substrate interface have been limited by the dynamic, transient nature of this interaction. Our study showcases the synergies between multiscale molecular dynamics and SPASM FRET biosensors to gain insights into the kinase–substrate conformational ensemble. Specifically, both the multiscale MD and FRET sensors are scalable to compare and contrast distinct kinase–substrate pairings and rigorously test those using FRET measurements and kinase assays. Although the current study is focused on PKCα, the conceptual insights and technologies can be readily generalized to other kinase–substrate interactions. One of the caveats to be noted is that the absolute binding energy of the peptide to the kinase would be affected by the full-length substrate protein and not just the peptide that has been modeled in this work. However, we believe that the relative binding energies calculated from MD simulations and binding constants measured with the FRET sensors for various peptides would be proportional to the relative binding constants of the whole protein, although the absolute values of the on and off rates of the peptides may differ from the full protein substrates.
S. L., T. D., H. D. S., M. S., A. L., R. F. S., A. J., N. V., and S. S. planned, designed the experiments, and contributed equally for this work. S. L., T. D., and H. D. all contributed equally for the experiments and analyzing the results. S. L., T. D., H. D. S., N. V., and S. S. wrote the manuscript.
This work was supported in part by National Institutes of Health Director's New Innovator Award 1DP2 CA186752-01 (to S. S.) and the Beckman Research Institute of the City of Hope (to N. V.). The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.