Recognition Dynamics of p53 and MDM2: Implications for Peptide Design

Disruption of 3CLpro protease self-association by short peptides as a potential route to broad spectrum coronavirus inhibitors

Coronaviruses have posed a persistent threat to human health over the last two decades. Despite the accumulated knowledge about coronavirus-related pathogens, development of an effective treatment for its new variant COVID-19 is highly challenging. For the highly-conserved and main coronavirus protease 3CLpro, dimerization is known to be essential for its catalytic activity and thereby for virus proliferation. Here, we assess the potential of short peptide segments to disrupt dimerization of the 3CLpro protease as a route to block COVID-19 proliferation. Based on the X-ray structure of the 3CLpro dimer, we identified the SPSGVY₁₂₆QCAMRP dodecapeptide segment as overlapping the hotspot regions on the 3CLpro dimer interface. Using computational blind docking of the peptide to the 3CLpro monomer, we found that the SPSGVY₁₂₆QCAMRP peptide has favourable thermodynamic binding (ΔG= -5.93 kcal/mol) to the hotspot regions at the 3CLpro dimer interface. Importantly, the peptide was also found to preferentially bind to the hotspot regions compared to other potential binding sites lying away from the dimer interface (ΔΔG=-1.31 kcal/mol). Docking of peptides corresponding to systematic mutation of the V125 and Y126 residues led to the identification of seven peptides, SPSGHAQCAMRP, SPSGVTQCAMRP, SPSGKPQCAMRP, SPSGATQCAMRP, SPSGWLQCAMRP, SPSGAPQCAMRP and SPSGHPQCAMRP, that outperform the wild-type SPSGVY₁₂₆QCAMRP peptide in terms of preferential binding to the 3CLpro dimer interface. These peptides have the potential to disrupt 3CLpro dimerization and therefore could provide lead structures for the development of broad spectrum COVID-19 inhibitors.Communicated by Ramaswamy H. Sarma.

Deciphering the mechanisms of HPV E6 mutations in the destabilization of E6/E6AP/p53 complex

In epithelial tumors, oncoprotein E6 binds with the ubiquitin ligase E6AP to form E6/E6AP heterodimer; then this heterodimer recruits p53 to form E6/E6AP/p53 heterotrimer and induces p53 degradation. Recent experiments demonstrated that three E6 single-site mutants (F47R, R102A, and L50E) can inhibit the E6/E6AP/p53 heterotrimer formation and rescue p53 from the degradation pathway. However, the molecular mechanism underlying mutation-induced heterotrimer inhibition remains largely elusive. Herein, we performed extensive molecular dynamics simulations (totally ∼13 μs) on both heterodimer and heterotrimer to elucidate at an atomic level how each p53-degradation-defective HPV16 E6 mutant reduces the structural stabilities of the two complexes. Our simulations reveal that the three E6 mutations destabilize the structure of E6/E6AP/p53 complex through distinct mechanisms. Although F47R^E6 mutation has no effect on the structure of E6/E6AP heterodimer, it results in an electrostatic repulsion between R47^E6 and R290^p53, which is unfavorable for E6-p53 binding. R102A^E6 mutation destabilizes the structure of E6/E6AP heterodimer and significantly disrupts hydrophobic and cation-π interactions between F47^E6 and E286^p53/L298^p53/R290^p53. L50E^E6 mutation impairs both E6 interdomain interactions (especially F47-K108 cation-π interaction) and E6-E6AP intermolecular interactions important for the stabilization of E6/E6AP heterodimer. This study identifies the intra- and intermolecular interactions crucial for the complex stability, which may provide mechanistic insights into the inhibition of complex formation by the three HPV16 E6 mutations.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Roles of computational modelling in understanding p53 structure, biology, and its therapeutic targeting

The transcription factor p53 plays pivotal roles in numerous biological processes, including the suppression of tumours. The rich availability of biophysical data aimed at understanding its structure–function relationships since the 1990s has enabled the application of a variety of computational modelling techniques towards the establishment of mechanistic models. Together they have provided deep insights into the structure, mechanics, energetics, and dynamics of p53. In parallel, the observation that mutations in p53 or changes in its associated pathways characterize several human cancers has resulted in a race to develop therapeutic modulators of p53, some of which have entered clinical trials. This review describes how computational modelling has played key roles in understanding structural-dynamic aspects of p53, formulating hypotheses about domains that are beyond current experimental investigations, and the development of therapeutic molecules that target the p53 pathway.

Structure-based designing efficient peptides based on p53 binding site residues to disrupt p53-MDM2/X interaction

MDM2 and MDMX are known as overexpressed oncoproteins in several wild-type p53 cancer cells. The development of potent and dual antagonist peptides for p53-MDM2/X is a continuous challenge. In this study, we intended to investigate the pivotal structural points respecting the development of potent and dual inhibitors of MDM2/X. Correspondingly, MD simulation was performed on the experimentally confirmed peptides, comprising p53, pDI, pDIQ, PMI, and computationally screened mutant pDI and pDIQ. A follow-up secondary structure analysis showed the last three C-terminal residues provide the helicity reservation of peptides bound to MDM2/X. Furthermore, a delicate residue-residue examination displayed Met 11 and Ser12 in the modified peptides contribute significantly to dual inhibition of MDM2/X. Additionally, the peptides_MDM2/X complexes' ΔG_binding extracted by the umbrella sampling method were in agreement with the pattern of their experimental affinity values. It was concluded the screened pDI mutants were considered as suitable anti-MDM2/X peptides, and the data obtained could be exploited as the theoretical structure-based guide for rational peptide design. Taking account of results, the suitable C-terminal residues of p53-based peptides especially Met11, and Ser12, as well as higher umbrella sampling, generated ΔG_binding to MDM2/X would be considered as the positive structural markers of a promising anti-cancer agent.

Common cancer mutations R175H and R273H drive the p53 DNA-binding domain towards aggregation-prone conformations

Cancer mutations R175H and R273H induce p53C towards aggregation-prone conformations by increasing their SASA, water exposure of H-bonds and flexibility of loop2.

Brownian Dynamics Simulations of Biological Molecules

Brownian dynamics (BD) is a technique for carrying out computer simulations of physical systems that are driven by thermal fluctuations. Biological systems at the macromolecular and cellular level, while falling in the gap between well-established atomic-level models and continuum models, are especially suitable for such simulations. We present a brief history, examples of important biological processes that are driven by thermal motion, and those that have been profitably studied by BD. We also present some of the challenges facing developers of algorithms and software, especially in the attempt to simulate larger systems more accurately and for longer times.

Characterizing the conformational landscape of MDM2-binding p53 peptides using Molecular Dynamics simulations

The conformational landscapes of p53 peptide variants and phage derived peptide (12/1) variants, all known to bind to MDM2, are studied using hamiltonian replica exchange molecular dynamics simulations. Complementing earlier observations, the current study suggests that the p53 peptides largely follow the ‘conformational selection’ paradigm in their recognition of and complexation by MDM2 while the 12/1 peptides likely undergo some element of conformational selection but are mostly driven by ‘binding induced folding’. This hypothesis is further supported by pulling simulations that pull the peptides away from their bound states with MDM2. This data extends the earlier mechanisms proposed to rationalize the entropically driven binding of the p53 set and the enthalpically driven binding of the 12/1 set. Using our hypothesis, we suggest mutations to the 12/1 peptide that increase its helicity in simulations and may, in turn, shift the binding towards conformational selection. In summary, understanding the conformational landscapes of the MDM2-binding peptides may suggest new peptide designs with bespoke binding mechanisms.

Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations

Understanding and control of structures and rates involved in protein ligand binding are essential for drug design. Unfortunately, atomistic molecular dynamics (MD) simulations cannot directly sample the excessively long residence and rearrangement times of tightly binding complexes. Here we exploit the recently developed multi-ensemble Markov model framework to compute full protein-peptide kinetics of the oncoprotein fragment 25-109Mdm2 and the nano-molar inhibitor peptide PMI. Using this system, we report, for the first time, direct estimates of kinetics beyond the seconds timescale using simulations of an all-atom MD model, with high accuracy and precision. These results only require explicit simulations on the sub-milliseconds timescale and are tested against existing mutagenesis data and our own experimental measurements of the dissociation and association rates. The full kinetic model reveals an overall downhill but rugged binding funnel with multiple pathways. The overall strong binding arises from a variety of conformations with different hydrophobic contact surfaces that interconvert on the milliseconds timescale.

Protein-peptide molecular docking with large-scale conformational changes: the p53-MDM2 interaction

Protein-peptide interactions are often associated with large-scale conformational changes that are difficult to study either by classical molecular modeling or by experiment. Recently, we have developed the CABS-dock method for flexible protein-peptide docking that enables large-scale rearrangements of the protein chain. In this study, we use CABS-dock to investigate the binding of the p53-MDM2 complex, an element of the cell cycle regulation system crucial for anti-cancer drug design. Experimental data suggest that p53-MDM2 binding is affected by significant rearrangements of a lid region - the N-terminal highly flexible MDM2 fragment; however, the details are not clear. The large size of the highly flexible MDM2 fragments makes p53-MDM2 intractable for exhaustive binding dynamics studies using atomistic models. We performed extensive dynamics simulations using the CABS-dock method, including large-scale structural rearrangements of MDM2 flexible regions. Without a priori knowledge of the p53 peptide structure or its binding site, we obtained near-native models of the p53-MDM2 complex. The simulation results match well the experimental data and provide new insights into the possible role of the lid fragment in p53 binding. The presented case study demonstrates that CABS-dock methodology opens up new opportunities for protein-peptide docking with large-scale changes of the protein receptor structure.

Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking

MDM2 is a negative regulator of p53 activity and an important target for cancer therapeutics. The N-terminal lid region of MDM2 modulates interactions with p53 via competition for its binding cleft, exchanging slowly between docked and undocked conformations in the absence of p53. To better understand these dynamics, we constructed Markov State Models (MSMs) from large collections of unbiased simulation trajectories of apo-MDM2, and find strong evidence for diffuse, yet two-state folding and binding of the N-terminal region to the p53 receptor site. The MSM also identifies holo-like receptor conformations highly suitable for computational docking, despite initiating trajectories from closed-cleft receptor structures unsuitable for docking. Fixed-anchor docking studies using a test set of high-affinity small molecules and peptides show simulated receptor ensembles achieve docking successes comparable to cross-docking studies using crystal structures of receptors bound by alternative ligands. For p53, the best-scoring receptor structures have the N-terminal region lid region bound in a helical conformation mimicking the bound structure of p53, suggesting lid region association induces receptor conformations suitable for binding. These results suggest that MD + MSM approaches can sample binding-competent receptor conformations suitable for computational peptidomimetic design, and that inclusion of disordered regions may be essential to capturing the correct receptor dynamics.