Recognizing the Binding Pattern and Dissociation Pathways of the p300 Taz2-p53 TAD2 Complex

Abstract

The dynamic association and dissociation between proteins are the basis of cellular signal transduction. This process becomes much more complicated if one or both interaction partners are intrinsically disordered because intrinsically disordered proteins can undergo disorder-to-order transitions upon binding to their partners. p53, a transcription factor with disordered regions, plays significant roles in many cellular signaling pathways. It is critical to understand the binding/unbinding mechanism involving these disordered regions of p53 at the residue level to reveal how p53 performs its biological functions. Here, we studied the dissociation process of the intrinsically disordered N-terminal transactivation domain 2 (TAD2) of p53 and the transcriptional adaptor zinc-binding 2 (Taz2) domain of transcriptional coactivator p300 using a combination of classical molecular dynamics, steered molecular dynamics, self-organizing maps, and time-resolved force distribution analysis (TRFDA). We observed two different dissociation pathways with different probabilities. One dissociation pathway starts from the TAD2 N-terminus and propagates to the α-helix and finally the C-terminus. The other dissociation pathway is in the opposite order. Subsequent TRFDA results reveal that key residues in TAD2 play critical roles. Besides the residues in agreement with previous experimental results, we also highlighted some other residues that play important roles in the disassociation process. In the dissociation process, non-native interactions were formed to partially compensate for the energy loss due to the breaking of surrounding native interactions. Moreover, our statistical analysis results of other experimentally determined complex structures involving either Taz2 or TAD2 suggest that the binding of the Taz2-TAD2 complex is mainly governed by the binding site of Taz2, which includes three main binding regions. Therefore, the complexes involving Taz2 may follow similar binding/unbinding behaviors, which could be studied together to generate common principles.

Investigating the allosteric response of the PICK1 PDZ domain to different ligands with all‐atom simulations

“…A recent review of allostery in the PDZ family 58 notes that A46 (αA helix) of PTP-BL PDZ2 and A347 (αA helix) of PSD-95 PDZ3 have been consistently identified as allosteric residues in a wide array of computational and experimental efforts. 15,18,31,34,35,41,46,57,[59][60][61][62] Furthermore, in a recent work exploring the interactions and dynamics between the PICK1 PDZ domain and the small molecule inhibitor BIO124, we propose that a structural alignment of PICK1 PDZ, PTP-BL PDZ2, and PSD-95 PDZ3 suggests that this allosteric alanine residue on the αA helix is evolutionarily conserved across all three PDZ domains. 63 This structural alignment also suggests that the interactions between BIO124 and I35 of the PICK1 PDZ domain may have a role in the propagation of signal to A58 of the αA helix.…”

“…Time-resolved force distribution analysis (TRFDA) 56 was performed to reveal the punctual stress on each PICK1 PDZ residue as a result of ligand binding as in previous work. 57 TRFDA was performed over each trajectory, and the per trajectory results were summed over each complex system. The summed results are shown in Figure S7.…”

Insight into the Initial Stages of the Folding Process in Onconase Revealed by UNRES

“…After training, each frame of the simulations is assigned to a neuron on the map, and each neuron represents a protein conformational microstate. A similar protocol was applied in previous studies. , In a second step, the neurons are further grouped in a small, but representative, number of clusters by agglomerative hierarchical clustering using Euclidean distance and complete linkage. An approximate transition matrix between each pair of neurons can be computed from the time-dependent distance approach as explained in previous work .…”

“…A similar protocol was applied in previous studies. 57,58 In a second step, the neurons are further grouped in a small, but representative, number of clusters by agglomerative hierarchical clustering using Euclidean distance and complete linkage.…”

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

“…A Self-Organizing Map (SOM) is an unsupervised learning method that allows visualization of multidimensional data in a low-dimensional representation and their clustering by keeping similar input data close to each other in the map [77][78][79] . Several applications of SOMs to the analysis of biomolecular simulations can be found in the literature ranging from clustering of ligand poses in virtual screening 80 to clustering of protein conformations from MD trajectories 77,81 and analysis of pathways in enhanced sampling MD simulations [82][83][84] . In this work, we used the PathDetect-SOM tool 85 to investigate molecular features of the sampled bound states and recognize differences in the configurations sampled during the MetaD simulations.…”