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We cast this quandary in the framework of allostery and we turned to methods to shed light on how the signaling within the protein under these various states may propagate through the protein. For this we turn to tools we developed addressing the networks of residues within the protein achieved through pairwise decomposition based on their cooperative distance MD-Sectors and energetic variance MD-END.

We then compared the findings of each of our constructs. The covariance matrices reveal stark differences in pairwise interactions of all the amino acid residues between the WT and the YC mutant. Highest correlations are indicated in blue and low correlations are indicated in yellow. The PK bound YC mutant shows a dampened signal that contrasts the highly interactive covariance matrix of the YC mutant system.

Figure 5 B shows the MD sector residues together with the positions observed in the form of a Venn diagram. Spectral analysis of the MD-calculated motional covariance matrix resulted in an MD sector of 39 residue positions with five residues, R, I, R, N, and E, that are consistent among all constructs Figure 6 , panel B, in yellow. We observe that many sector residues coincide with regions of highly scrutinized sites such as hotspot mutations, zinc binding residues, and DNA contact residues Table 1 , verifying that the analysis indeed identifies functionally crucial residues.

Table 1. Functional Sector Residues Ranked a. Our first pass of analysis on the three configurations of the YC energetic interaction networks involved generating three representative networks one for each energy channel studied from the frames of energy data to try and observe any obvious topological differences. This was done by simply by summing over common edges in each frame. In we refer to the adjacency matrix storing the total energy as T.

Unsurprisingly, across all three energy channels, the networks of the WT, bound and mutated systems were all quite similar as shown in Figure 6. T is then normalized and locally thresholded. Using the open-source program Gephi, we create a visualization of all three T networks for both electrostatics and Van Der Waals forces. In these images as shown in Figure 6 , the more opaque an edge is the higher its weight is. In general, the drawing of a graph will have no relation to the underlying dynamics of the system as a there is no notion of the distance on graph structured data.

However, these images were generated using the ForceAtlas2 layout algorithm in Gephi in which nodes are represented as repulsing entities like charged particles, while edges attract their nodes like springs. Therefore, these images highlight some of the dynamics that are important in allosteric signaling and thus can offer us good intuition regarding the types of properties that future analyses may focus on.

In the electrostatic networks, we can see a trend that there are certain residues that tend to have a higher degree and a larger weight. In this study we assess the extent to which a mutant protein can be restored by an allosteric drug by restoring the native protein dynamics.

To this end we have explored this with the p53 DBD having the YC mutation which has been rescued by the compound PK by an unknown mechanism which we hypothesize to be via allosteric modulation. A recent report of the allosteric effector PK that rescues the YC hotspot mutation of p53 7 presents an ideal test case to study the effect of allosteric disruption and points of control for rescue.

Here we discuss our findings and their implications for allosteric drug design. YC exhibits aggregative properties via indiscriminate hydrogen bonding with the consensus DNA sequence. Statistical coupling analysis SCA by pairwise decomposition of distance-based and energy-based variables from our simulations reveal an allosteric hub of amino acids that propagate energetic signaling between Y and the active site via beta sheet residues — Sector residues identified 39 residues that are motionally covariant.

The RMSF results identified a number of segments of residues whose dynamics are disrupted by the YC mutation and are restored by the presence of PK Many of these, interestingly, correspond to the location of the binding sites of antibodies capable of distinguishing between WT and mutant p53, suggesting that they are sensitized to the dynamics in the region and that some similarities in role of destabilization may be shared between mutants.

For example, in our results, the average flux of the residues across corresponding to the WT specific antibody epitope PAb is identifiably deviant only in the YC mutant system.

The same epitope regions, where in our prior work on RH hotspot, points to other mechanisms that could be at play involving the opening of a hydrophobic patch that destabilizes the p This combination molecular therapeutics could be valuable particularly with patients suffering from late-stage cancers that have evolved an ensemble of mutations.

We also note that some regions were not reinstated to native dynamics. This suggests that full rescue may not be required for the restoration of essential function. Alternately, some dynamics variation may be unimportant for its functionality. The extent to which p53 may stay functional was measured via the interaction interface between p53 and the DNA.

Hydrogen-bonding analysis at the interaction interface of p53 with its cognate DNA provides insight into the changes in H-bond pattern occurring in the presence of the YC mutation.

The structural consequences of allosteric perturbation and rescue should reflect back onto the direct readout and recognition of the DNA sequence. We analyzed the hydrogen-bonding network in the interface of the centroidal structures of each system Figures 3 and 4 that were derived from our MD-MSM analysis.

DNA recognition has been restored in the presence of PK, raising questions about how the information may be energetically propagated through an allosteric network which we have taken up with the network analysis.

Furthermore, we have noticed the residue K, has an increased residence time interacting with the DNA in the presence of the PK This may provide additional stabilization and its binding orientation can be a good indicator of p53 activation or deactivation.

This also suggests a means of differentiation of binding sites for p53, extending the contact region specificity to this position. The finding echoes the observation from the RMSF analysis that restoration need not be exactly the same, and that the level of resolution by which MD-MSMs reveal similar overall conformations is adequate for capturing allosteric activity.

While much of the energetic network stayed consistent throughout all systems, the sectors reveal a network that are vastly different from one another, sharing only very few residues all throughout. The bottleneck of essential residues leaves the protein vulnerable to even single point mutations like that of the YC mutant. This suggests that the residues shown in column three of Table 1 are reorienting to adopt a wild-type like state thereby rescuing YC from binding nonspecifically to the DNA consensus sequence.

Many of the WT sector residues are also in regions of low atomic fluctuations along the beta sheet, while conversely, the YC mutant sectors are crowded in highly mobile loop regions in proximity to the drug binding site. When PK is covalently introduced to the mutant, we see that many of the sectors begin to cluster in regions involved in DNA contact mainly in the C terminus alpha helix and the L1 loop. The sectors that overlap between the three cohorts present a subnetwork of residues where the kinetic signals from the allosteric perturbation propagate.

The overlapping residues of WT and PK bound Yc mutant Figure 6 B, teal , occupy the same beta sheet region between the L3 loop and Strand loop residues — as the five bottleneck residues that are present in all systems. R, DNA-binding residue and also a hotspot mutation site is also located in this region. Taken together, our findings reveal an allosteric signaling hub at play in the presence of allosteric disruptor YC and YC reactivator, PK Protein wide measures were taken to examine the extent to which communication between the distal points of the mutation and binding could be linked to the readout interface indicated by some level of interaction via an allosteric signaling network.

MD sector results reveal a subset of unique residues that are correlated with each other by their motional displacements. MD-END, elucidates the information network and the kinetic transfer based on nonbonded atomic interaction energies, while MD-Sectors unveils only the motional response of individual residues to allosteric disruption, together with MD-END we gain additional insight into the energetic network of covariant residues.

Materials and Methods. Energy minimization with decreasing constraints on the protein solute was carried out, followed by heating to K and temperature maintained using the Berendsen algorithm. Hydrogen-bonding analysis was carried out using cpptraj from the Amber14 package, with standard distance and angle cutoff were set to 3. The first eigenvalue of a spectral decomposition is by definition unity, and the remaining eigenvalues define modes which comprise a series expansion of the matrix in covariance space.

Distance covariance was used to measure the correlations of the joint independence of any two vectors. By computing the pairwise covariance between all residues over the simulation time, pairwise covariance was mapped for all residues as defined by Lakhani et al. Computing the pairwise interaction energy for every frame in the trajectory is very computationally costly. To limit the computational burden, we computed pairwise energies for a smaller subset of frames from each trajectory.

To choose which frames were included in our sample, we implemented a simple Monte-Carlo procedure. First, we sample frames at some fixed interval 2 frames per nanosecond.

Next, we iterated the sampling over all three trajectories and tested to see if the RMSD of the sample to the Markov state modeled cluster centroid was less than the average RMSD of all points in the cluster to the centroid.

This allows us to determine if the sample is representative of structures in its cluster. If the RMSD is greater than the average, we simply skip the frame. After collecting these representative samples, we select our final batch of samples for each cluster proportional to the Boltzmann distribution for each trajectory. We computed the energy for each of the residues residue numbers 96— of p53 and chose two combinations of residue pairs by using a bash script that performed the cpptraj energy command with each pair of residues.

Finally, we used python3 to parse the cpptraj outputs into tensors of shape N,X,D,D where N is the number of frames in the trajectory X is the number of types of energy being studied and D the number of residues.

We then simply summed over each energy to compute one representative network per energy channel. Finally, the edges of the network are assigned thresholds by normalizing the edge weights on a per node basis and picking all nodes above that threshold. All of the datasets used in this study are available from the authors upon request.

Supporting Information. Author Information. In Sub M. Kelly M. The authors declare no competing financial interest. A survey on graph kernels. Cell , 27 , — , DOI: The role of distant mutations and allosteric regulation on LovD active site dynamics. Nature Publishing Group. Natural enzymes have evolved to perform their cellular functions under complex selective pressures, which often require their catalytic activities to be regulated by other proteins.

We contrasted a natural enzyme, LovD, which acts on a protein-bound LovF acyl substrate, with a lab. The resulting mutant variant is fold more efficient in the synthesis of the drug simvastatin than the wild-type LovD. This is to our knowledge the first nonpatent report of the enzyme currently used for the manuf. Crystal structures and microsecond-scale mol. Mutations markedly altered conformational dynamics of the catalytic residues, obviating the need for allosteric modulation by the acyl carrier LovF.

Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. Science , , — , DOI: Mutations in the p53 tumor suppressor are the most frequently obsd. The majority of the mutations occur in the core domain which contains the sequence-specific DNA binding activity of the p53 protein residues , and they result in loss of DNA binding.

The crystal structure of a complex contg. R factor of The two loops, which are held together in part by a tetrahedrally coordinated zinc atom, and the loop-sheet-helix motif form the DNA binding surface of p Residues from the loop-sheet-helix motif interact in the major groove of the DNA, while an arginine from one of the two large loops interacts in the minor groove.

The loops and the loop-sheet-helix motif consists of the conserved regions of the core domain and contain the majority of the p53 mutations identified in tumors. The structure supports the hypothesis that DNA binding is crit. Mutant conformation of p Precise epitope mapping using a filamentous phage epitope library. Many naturally occurring point mutations in the p53 gene lead to a proportion of the encoded protein mols.

Here the PAb epitope is defined using a filamentous phage epitope library. The hexapeptides displayed by the PAbbinding phage isolated from the library were all highly related and allowed both direct localization of the epitope and prediction of a specific interaction between PAb and Xenopus TFIIIA. This study demonstrates the power of phage epitope libraries in the precise definition of previously unmapped epitopes. Identification of the PAb epitope precisely defines a region of the p53 mol.

Relationship between hot spot residues and ligand binding hot spots in protein-protein interfaces. Zerbe, Brandon S. American Chemical Society. In the context of protein-protein interactions, the term "hot spot" refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as detd. In contrast, in pharmaceutical research, a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery.

Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small mols. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or exptl.

Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however.

In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are addnl. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by exptl.

Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model GNM , a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency.

Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using F1 measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity.

The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinfom. Conformational selection and induced fit are well-known contributors to ligand binding and allosteric effects in proteins.

Here, we investigated protein-ligand binding and allostery based on a Markov state model MSM with states and rates obtained from all-atom MD simulations. As an exemplary case, we considered the single domain protein Par-6 PDZ with and without ligand and allosteric effector.

This is one of the smallest proteins in which allostery has been exptl. In spite of the increased complexity intrinsic to a statistical ensemble perspective, we found that conformational selection and induced fit mechanisms could be readily identified in the anal.

However, the allosteric pathway requires an activation step that involves a conformational change induced by allosteric effector Cdc Once in the allosterically activated state, we found that ligand binding could proceed by conformational selection.

The MD-MSM model predicted that allostery in this and possibly other systems involves both induced fit and conformational selection, not just one or the other. Protein sectors: evolutionary units of three-dimensional structure. Cell , , — , DOI: Cell Press. Current installers such as pymol-XXrX-bin-win The file size of the latest downloadable installer is Our built-in antivirus scanned this download and rated it as virus free.

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