Impact of Nonnative Interactions on the Binding Kinetics of Intrinsically Disordered p53 with MDM2: Insights from All-Atom Simulation and Markov State Model Analysis.

Intrinsically disordered proteins (IDPs) lack a well-defined tertiary structure but are essential players in various biological processes. Their ability to undergo a disorder-to-order transition upon binding to their partners, known as the folding-upon-binding process, is crucial for their function. One classical example is the intrinsically disordered transactivation domain (TAD) of the tumor suppressor protein p53, which quickly forms a structured α-helix after binding to its partner MDM2, with clinical significance for cancer treatment. However, the contribution of nonnative interactions between the IDP and its partner to the rapid binding kinetics, as well as their interplay with native interactions, is not well understood at the atomic level. Here, we used molecular dynamics simulation and Markov state model (MSM) analysis to study the folding-upon-binding mechanism between p53-TAD and MDM2. Our results suggest that the system progresses from the nascent encounter complex to the well-structured encounter complex and finally reaches the native complex, following an induced-fit mechanism. We found that nonnative hydrophobic and hydrogen bond interactions, combined with native interactions, effectively stabilize the nascent and well-structured encounter complexes. Among the nonnative interactions, Leu25p53-Leu54MDM2 and Leu25p53-Phe55MDM2 are particularly noteworthy, as their interaction strength is close to the optimum. Evidently, strengthening or weakening these interactions could both adversely affect the binding kinetics. Overall, our findings suggest that nonnative interactions are evolutionarily optimized to accelerate the binding kinetics of IDPs in conjunction with native interactions.

Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding.

In this work, we investigate the role of solvent in the binding reaction of the p53 transactivation domain (TAD) peptide to its receptor MDM2. Previously, our group generated 831 µs of explicit-solvent aggregate molecular simulation trajectory data for the MDM2-p53 peptide binding reaction using large-scale distributed computing and subsequently built a Markov State Model (MSM) of the binding reaction (Zhou et al. 2017). Here, we perform a tICA analysis and construct an MSM with similar hyperparameters while using only solvent-based structural features. We find a remarkably similar landscape but accelerated implied timescales for the slowest motions. The solvent shells contributing most to the first tICA eigenvector are those centered on Lys24 and Thr18 of the p53 TAD peptide in the range of 3-6 Å. Important solvent shells were visualized to reveal solvation and desolvation transitions along the peptide-protein binding trajectories. Our results provide a solvent-centric view of the hydrophobic effect in action for a realistic peptide-protein binding scenario.

Markov State Modeling Analysis Captures Changes in the Temperature-Sensitive N-Terminal and ß-Turn Regions of the p53 DNA-Binding Domain.

The transcription factor p53 is one of the most widely studied cancer proteins. Its temperature-sensitive nature suggests reduction in functionality at physiological temperatures. Temperature-induced conformational variations and their impact on its functional ability still remain unexplored. A total of 20.8 µs molecular dynamics simulations of wildtype p53 in the apo and the DNA-bound states have been performed at 300 K and 310 K. Further, Markov State Modeling (MSM) analyses were performed, considering Cα-Cα distances as reaction coordinates. Filtering of these distances based on correlation with the time-independent components (tICs) resulted in 16 and 32 distances for apo and DNA-bound systems, respectively. Individual MSM analyses using these filtered distances were performed for both p53 systems. These Cα-Cα residue pairs belonged to the N-terminal, S6/7 ß-turn, loop L2, loop L3, and hydrophobic core residues. At physiological temperatures, apo-p53 exhibits exposure of its hydrophobic core, where the temperature-sensitive hotspot residues were also located. This exposure was the result of the S6/7 ß-turn and N-terminal moving apart. In the DNA-bound p53 system, loop L1 attains an open conformation at physiological temperatures, which weakens the DNA binding. It is already known that p53 mutants that lack DNA binding also tend to show similar conformational variations. The S6/7 ß-turn along with the already known functionally important loop L2 may pose as regions to be targeted to overcome the loss in binding of temperature-sensitive wildtype p53. Rescue strategies directed toward these temperature-sensitive regions may be useful to recuperate its strong binding at physiological temperatures.

Markov State Models to Elucidate Ligand Binding Mechanism.

Molecular dynamics simulations can now routinely access the microsecond timescale, making feasible direct sampling of ligand association events. While Markov State Model (MSM) approaches offer a useful framework for analyzing such trajectory data to gain insight into binding mechanisms, accurate modeling of ligand association pathways and kinetics must be done carefully. We describe methods and good practices for constructing MSMs of ligand binding from unbiased trajectory data and discuss how to use time-lagged independent component analysis (tICA) to build informative models, using as an example recent simulation work to model the binding of phenylalanine to the regulatory ACT domain dimer of phenylalanine hydroxylase. We describe a variety of methods for estimating association rates from MSMs and discuss how to distinguish between conformational selection and induced-fit mechanisms using MSMs. In addition, we review some examples of MSMs constructed to elucidate the mechanisms by which p53 transactivation domain (TAD) and related peptides bind the oncoprotein MDM2.

Kinetic Selection and Relaxation of the Intrinsically Disordered Region of a Protein upon Binding.

Here, we investigate the association and dissociation mechanisms of a typical intrinsically disordered region (IDR), transcriptional activation subdomain of tumor suppressor protein p53 (TAD-p53), with murine double-minute clone 2 protein (MDM2). Using a combination of cycles of association and dissociation parallel cascade molecular dynamics, multiple standard molecular dynamics (MD), and the Markov state model, we were successful in obtaining the lowest free energy structure of the MDM2/TAD-p53 complex as the structure closest to the crystal structure without prior knowledge of the crystal structure. This method also reproduced the experimentally measured standard binding free energy, and the association and dissociation rate constants, requiring only an accumulated MD simulation cost of 11.675 µs even though that actual dissociation occurs on the order of seconds. We identified few complex intermediates with similar free energies; yet TAD-p53 first binds MDM2 as the second lowest free energy intermediate kinetically with >90% of the flux, adopting a conformation similar to that of one of these few intermediates in its monomeric state. Even though the mechanism of the first step has a conformational-selection-type aspect, the second step shows induced-fit-like features and occurs as concomitant dehydration of the interface, side-chain π-π stacking, and main-chain hydrogen-bond formation to complete binding as an α-helix. In addition, dehydration is a key process for the final relaxation process around the complex interface. These results demonstrate that TAD-p53 kinetically selects its initial binding form and then relaxes to complete the binding.

Target search on DNA by interacting molecules: First-passage approach.

Gene regulation is one of the most important fundamental biological processes in living cells. It involves multiple protein molecules that locate specific sites on DNA and assemble gene initiation or gene repression multimolecular complexes. While the protein search dynamics for DNA targets has been intensively investigated, the role of intermolecular interactions during the genetic activation or repression remains not well quantified. Here, we present a simple one-dimensional model of target search for two interacting molecules that can reversibly form a dimer molecular complex, which also participates in the search process. In addition, the proteins have finite residence times on specific target sites, and the gene is activated or repressed when both proteins are simultaneously present at the target. The model is analyzed using first-passage analytical calculations and Monte Carlo computer simulations. It is shown that the search dynamics exhibit a complex behavior depending on the strength of intermolecular interactions and on the target residence times. We also found that the search time shows a nonmonotonic behavior as a function of the dissociation rate for the molecular complex. Physical-chemical arguments to explain these observations are presented. Our theoretical approach highlights the importance of molecular interactions in the complex process of gene activation/repression by multiple transcription factor proteins.

Residual Structures and Transient Long-Range Interactions of p53 Transactivation Domain: Assessment of Explicit Solvent Protein Force Fields.

Molecular dynamics simulations using physics-based atomistic force fields have been increasingly used to characterize the heterogeneous structural ensembles of intrinsically disordered proteins (IDPs). To evaluate the accuracy of the latest atomistic explicit-solvent force fields in modeling larger IDPs with nontrivial structural features, we focus on the 61-residue N-terminal transactivation domain (TAD) of tumor suppressor p53, an important protein in cancer biology that has been extensively studied, and abundant experimental data is available for evaluation of simulated ensembles. We performed extensive replica exchange with solute tempering simulations, in excess of 1.0 µs/replica, to generate disordered structural ensembles of p53-TAD using six latest explicit solvent protein force fields. Multiple local and long-range structural properties, including chain dimension, residual secondary structures, and transient long-range contacts, were analyzed and compared against available experimental data. The results show that IDPs such as p53-TAD remain highly challenging for atomistic simulations due to conformational complexity and difficulty in achieving adequate convergence. Structural ensembles of p53-TAD generated using various force fields differ significantly from each other. The a99SB-disp force field demonstrates the best agreement with experimental data at all levels and proves to be suitable for simulating unbound p53-TAD and how its conformational properties may be modulated by phosphorylation and other cellular signals or cancer-associated mutations. Feasibility of such detailed structural characterization is a key step toward establishing the sequence-disordered ensemble-function-disease relationship of p53 and other biologically important IDPs.

Dissociation Process of a MDM2/p53 Complex Investigated by Parallel Cascade Selection Molecular Dynamics and the Markov State Model.

Recently, we efficiently generated dissociation pathways of a protein-ligand complex without applying force bias with parallel cascade selection molecular dynamics (PaCS-MD) and showed that PaCS-MD in combination with the Markov state model (MSM) yielded a binding free energy comparable to experimental values. In this work, we applied the same procedure to a complex of MDM2 protein and the transactivation domain of p53 protein (TAD-p53), the latter of which is known to be very flexible in the unbound state. Using 30 independent MD simulations in PaCS-MD, we successfully generated 25 dissociation pathways of the complex, which showed complete or partial unfolding of the helical region of TAD-p53 during the dissociation process within an average simulation time of 154.8 ± 46.4 ns. The standard binding free energy obtained in combination with one-dimensional-, three-dimensional (3D)- or Cα-MSM was in good agreement with those determined experimentally. Using 3D-MSM based on the center of mass position of TAD-p53 relative to MDM2, the dissociation rate constant was calculated, which was comparable to those measured experimentally. Cα-MSM based on all Cα coordinates of TAD-p53 reproduced the experimentally measured standard binding free energy, and dissociation and association rate constants. We conclude that the combination of PaCS-MD and MSM offers an efficient computational procedure to calculate binding free energies and kinetic rates.

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.

Comprehensive large-scale assessment of intrinsic protein disorder.

MOTIVATION: Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures. RESULTS: MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10% are observed. AVAILABILITY: The datasets are available from Web site at URL: http://mobidb.bio.unipd.it/lsd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conformations of p53 response elements in solution deduced using site-directed spin labeling and Monte Carlo sampling.

The tumor suppressor protein p53 regulates numerous signaling pathways by specifically recognizing diverse p53 response elements (REs). Understanding the mechanisms of p53-DNA interaction requires structural information on p53 REs. However, such information is limited as a 3D structure of any RE in the unbound form is not available yet. Here, site-directed spin labeling was used to probe the solution structures of REs involved in p53 regulation of the p21 and Bax genes. Multiple nanometer distances in the p21-RE and BAX-RE, measured using a nucleotide-independent nitroxide probe and double-electron-electron-resonance spectroscopy, were used to derive molecular models of unbound REs from pools of all-atom structures generated by Monte-Carlo simulations, thus enabling analyses to reveal sequence-dependent DNA shape features of unbound REs in solution. The data revealed distinct RE conformational changes on binding to the p53 core domain, and support the hypothesis that sequence-dependent properties encoded in REs are exploited by p53 to achieve the energetically most favorable mode of deformation, consequently enhancing binding specificity. This work reveals mechanisms of p53-DNA recognition, and establishes a new experimental/computational approach for studying DNA shape in solution that has far-reaching implications for studying protein-DNA interactions.

Binding of two intrinsically disordered peptides to a multi-specific protein: a combined Monte Carlo and molecular dynamics study.

The unique ability of intrinsically disordered proteins (IDPs) to fold upon binding to partner molecules makes them functionally well-suited for cellular communication networks. For example, the folding-binding of different IDP sequences onto the same surface of an ordered protein provides a mechanism for signaling in a many-to-one manner. Here, we study the molecular details of this signaling mechanism by applying both Molecular Dynamics and Monte Carlo methods to S100B, a calcium-modulated homodimeric protein, and two of its IDP targets, p53 and TRTK-12. Despite adopting somewhat different conformations in complex with S100B and showing no apparent sequence similarity, the two IDP targets associate in virtually the same manner. As free chains, both target sequences remain flexible and sample their respective bound, natively [Formula: see text]-helical states to a small extent. Association occurs through an intermediate state in the periphery of the S100B binding pocket, stabilized by nonnative interactions which are either hydrophobic or electrostatic in nature. Our results highlight the importance of overall physical properties of IDP segments, such as net charge or presence of strongly hydrophobic amino acids, for molecular recognition via coupled folding-binding.

The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes.

BACKGROUND: A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate. RESULTS: Training a Profile Hidden Markov Model with corresponding positions matching a combined-palindromic p53-binding motif creates the best p53-RE predictive model. The p53HMM algorithm is available on-line: (http://tools.csb.ias.edu). CONCLUSION: Using Profile Hidden Markov Models with training methods that exploit the redundant information of the homotetramer p53 binding site provides better predictive models than weight matrices (PSSMs). These methods may also boost performance when applied to other transcription factor binding sites.

Targeting the conformational transitions of MDM2 and MDMX: insights into dissimilarities and similarities of p53 recognition.

MDM2 and MDMX are oncogenic homologue proteins that regulate the activity and stability of p53, a tumor suppressor protein involved in more than 50% of human cancers. While the large body of experiments so far accumulated has validated MDM2 as a therapeutically important target for the development of anticancer drugs, it is only recently that MDMX has also become an attractive target for the treatment of tumor cells expressing wild type p53. The availability of structural information of the N-terminal domain of MDM2 in complex with p53-derived peptides and inhibitors, and the very recent disclosure of the crystal structure of the N-terminal domain of MDMX bound to a p53 peptide, offer an unprecedented opportunity to provide insight into the molecular basis of p53 recognition and the identification of discriminating features affecting the binding of the tumor suppressor protein at MDM2 and MDMX. By using coarse graining simulations, in this study we report the exploration of the conformational transitions featured in the pathway leading from the apo-MDM2 and apo-MDMX states to the p53-bound MDM2 and p53-bound MDMX states, respectively. The results have enabled us to identify a pool of diverse conformational states of the oncogenic proteins that affect the binding of p53 and the presence of conserved and non-conserved interactions along the conformational transition pathway that may be exploited in the design of selective and dual modulators of MDM2 and MDMX activity.

Structural assessment of single amino acid mutations: application to TP53 function.

Single amino acid substitution is the type of protein alteration most related to human diseases. Current studies seek primarily to distinguish neutral mutations from harmful ones. Very few methods offer an explanation of the final prediction result in terms of the probable structural or functional effect on the protein. In this study, we describe the use of three novel parameters to identify experimentally-verified critical residues of the TP53 protein (p53). The first two parameters make use of a surface clustering method to calculate the protein surface area of highly conserved regions or regions with high nonlocal atomic interaction energy (ANOLEA) score. These parameters help identify important functional regions on the surface of a protein. The last parameter involves the use of a new method for pseudobinding free-energy estimation to specifically probe the importance of residue side-chains to the stability of protein fold. A decision tree was designed to optimally combine these three parameters. The result was compared to the functional data stored in the International Agency for Research on Cancer (IARC) TP53 mutation database. The final prediction achieved a prediction accuracy of 70% and a Matthews correlation coefficient of 0.45. It also showed a high specificity of 91.8%. Mutations in the 85 correctly identified important residues represented 81.7% of the total mutations recorded in the database. In addition, the method was able to correctly assign a probable functional or structural role to the residues. Such information could be critical for the interpretation and prediction of the effect of missense mutations, as it not only provided the fundamental explanation of the observed effect, but also helped design the most appropriate laboratory experiment to verify the prediction results.

TP53 mutations in breast cancer associated with BRCA1 or BRCA2 germ-line mutations: distinctive spectrum and structural distribution.

Several groups have studied the molecular pathology of inherited breast cancer. By combining several such studies, we show in this study that somatic TP53 abnormalities are more common in breast cancer associated with BRCA1 or BRCA2 germ-line mutations than in sporadic breast cancers (odds ratio, 2.8; P = 0.0003). Then, we compared the spectrum of TP53 mutations for breast cancers in the IARC TP53 mutation database with the 82 mutations reported in BRCA1/2-associated breast cancers. The spectrum differed significantly both in distribution (P < 1 x 10(-6)) and in base changes (P = 0.025). Mutations at A:T bp were more common in BRCA1/2-associated tumors and strand bias suggesting DNA repair abnormalities was found. Changes were common at TP53 codons that are not mutation hotspots. Structural modeling showed that most of these p53 non-hotspot amino acids characterized in breast tumors isolated from patients with deficient BRCA1/2 function are distributed in a region of the protein on the opposite side of the p53 DNA-binding surface. Our results suggest that BRCA1/2 mutations influence the type and distribution of TP53 mutations seen in breast cancer.

Frequency and Markov chain analysis of amino acid sequences of mouse p53.

The amino acid sequence of mouse p53 was measured according to two- and three-amino-acid sequences. The measured frequency and probability were compared with predicted frequency and probability. Of 389 two-amino-acid sequences in mouse p53,85 (21.851%) and 27 (6.941%) sequences can be explained by the predicted frequency and probability according to a purelyrandom mechanism. Of 188 non-appearing two-amino-acid sequences in mouse p53, 86 (45.745%) and 32 (17.021%) can be explained by the predicted frequency and probability according to a purely random mechanism; no more than two-amino-acid sequences in mouse p53 can be explained by a purely random mechanism.