Long range recognition and selection in IDPs: the interactions of the C-terminus of p53

Distinguishing features of fold-switching proteins

Though many folded proteins assume one stable structure that performs one function, a small-but-increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold-switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus-2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold-switching proteins from their single-fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late-breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue-residue coevolution. The review closes by discussing high-throughput and medium-throughput experimental approaches that might be used to identify new fold-switching proteins.

Activation of p53: How phosphorylated Ser15 triggers sequential phosphorylation of p53 at Thr18 by CK1δ

The N-terminal transactivation domain (TAD) of p53 is a disordered region with multiple phosphorylation sites. Phosphorylation at Thr18 is crucial for the release of p53 from its negative regulator, MDM2. In stressed cells, CK1δ is responsible for phosphorylating Thr18, but requires Ser15 to be phosphorylated. To understand the mechanistic underpinnings of this sequential phosphorylation, molecular modeling and molecular dynamics simulation studies of these phosphorylation events were carried out. Our models suggest that a positively charged region on CK1δ near the adenosine triphosphate (ATP) binding pocket, which is conserved across species, sequesters the negatively charged pSer15, thereby constraining the positioning of the rest of the peptide, such that the side chain of Thr18 is positioned close to the γ-phosphate of ATP. Furthermore, our studies show that the phosphorylated p53 TAD1 (p53pSer15) peptide binds more strongly to CK1δ than does p53. p53 adopts a helical structure when bound to CK1δ, which is lost upon phosphorylation at Ser15, thus gaining higher flexibility and ability to morph into the binding site. We propose that upon phosphorylation at Ser15 the p53 TAD1 peptide binds to CK1δ through an electrostatically driven induced fit mechanism resulting in a flanking fuzzy complex.

idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are proteins or protein-domains that do not have a single native structure, rather, they are a class of flexible peptides that can rapidly adopt multiple conformations. IDPs are quite abundant, and their dynamic characteristics provide unique advantages for various biological processes. The field of “unstructured biology” has emerged, in part, because of numerous computational studies that had identified the unique characteristics of IDPs and IDRs. The package ‘idpr’, short for Intrinsically Disordered Proteins in R, implements several R functions that match the established characteristics of IDPs to protein sequences of interest. This includes calculations of residue composition, charge-hydropathy relationships, and predictions of intrinsic disorder. Additionally, idpr integrates several amino acid substitution matrices and calculators to supplement IDP-based workflows. Overall, idpr aims to integrate tools for the computational analysis of IDPs within R, facilitating the analysis of these important, yet under-characterized, proteins. The idpr package can be downloaded from Bioconductor (https://bioconductor.org/packages/idpr/).

FLIPPER: Predicting and Characterizing Linear Interacting Peptides in the Protein Data Bank

A large fraction of peptides or protein regions are disordered in isolation and fold upon binding. These regions, also called MoRFs, SLiMs or LIPs, are often associated with signaling and regulation processes. However, despite their importance, only a limited number of examples are available in public databases and their automatic detection at the proteome level is problematic. Here we present FLIPPER, an automatic method for the detection of structurally linear sub-regions or peptides that interact with another chain in a protein complex. FLIPPER is a random forest classification that takes the protein structure as input and provides the propensity of each amino acid to be part of a LIP region. Models are built taking into consideration structural features such as intra- and inter-chain contacts, secondary structure, solvent accessibility in both bound and unbound state, structural linearity and chain length. FLIPPER is accurate when evaluated on non-redundant independent datasets, 99% precision and 99% sensitivity on PixelDB-25 and 87% precision and 88% sensitivity on DIBS-25. Finally, we used FLIPPER to process the entire Protein Data Bank and identified different classes of LIPs based on different binding modes and partner molecules. We provide a detailed description of these LIP categories and show that a large fraction of these regions are not detected by disorder predictors. All FLIPPER predictions are integrated in the MobiDB 4.0 database.

Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning

Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins and regions (IDP/IDR). Different from protein secondary structure (SS) prediction, the SSP prediction assumes a dynamic assignment of secondary structures that seem correlate with disordered states. In this study, we designed a single-task deep learning framework to predict IDP/IDR and SSP respectively; and multitask deep learning frameworks to allow quantitative predictions of IDP/IDR evidenced by the simultaneously predicted SSP. According to independent test results, single-task deep learning models improve the prediction performance of shallow models for SSP and IDP/IDR. Also, the prediction performance was further improved for IDP/IDR prediction when SSP prediction was simultaneously predicted in multitask models. With p53 as a use case, we demonstrate how predicted SSP is used to explain the IDP/IDR predictions for each functional region.

An integrated view of p53 dynamics, function, and reactivation

The tumor suppressor p53 plays a vital role in responding to cell stressors such as DNA damage, hypoxia, and tumor formation by inducing cell-cycle arrest, senescence, or apoptosis. Expression level alterations and mutational frequency implicates p53 in most human cancers. In this review, we show how both computational and experimental methods have been used to provide an integrated view of p53 dynamics, function, and reactivation potential. We argue that p53 serves as an exceptional case study for developing methods in modeling intrinsically disordered proteins. We describe how these methods can be leveraged to improve p53 reactivation molecule design and other novel therapeutic modalities, such as PROteolysis TARgeting Chimeras (PROTACs).

Exploring Protein Intrinsic Disorder with MobiDB

Nowadays, it is well established that many proteins or regions under physiological conditions lack a fixed three-dimensional structure and are intrinsically disordered. MobiDB is the main repository of protein disorder and mobility annotations, combining different data sources to provide an exhaustive overview of intrinsic disorder. MobiDB includes curated annotations from other databases, indirect disorder evidence from structural data, and disorder predictions from protein sequences. It provides an easy-to-use web server to visualize and explore disorder information. This chapter describes the data available in MobiDB, emphasizing how to use and access the intrinsic disorder data. MobiDB is available at URL http://mobidb.bio.unipd.it .

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.

A single synonymous mutation determines the phosphorylation and stability of the nascent protein

p53 is an intrinsically disordered protein with a large number of post-translational modifications and interacting partners. The hierarchical order and subcellular location of these events are still poorly understood. The activation of p53 during the DNA damage response (DDR) requires a switch in the activity of the E3 ubiquitin ligase MDM2 from a negative to a positive regulator of p53. This is mediated by the ATM kinase that regulates the binding of MDM2 to the p53 mRNA facilitating an increase in p53 synthesis. Here we show that the binding of MDM2 to the p53 mRNA brings ATM to the p53 polysome where it phosphorylates the nascent p53 at serine 15 and prevents MDM2-mediated degradation of p53. A single synonymous mutation in p53 codon 22 (L22L) prevents the phosphorylation of the nascent p53 protein and the stabilization of p53 following genotoxic stress. The ATM trafficking from the nucleus to the p53 polysome is mediated by MDM2, which requires its interaction with the ribosomal proteins RPL5 and RPL11. These results show how the ATM kinase phosphorylates the p53 protein while it is being synthesized and offer a novel mechanism whereby a single synonymous mutation controls the stability and activity of the encoded protein.

HSPA1A conformational mutants reveal a conserved structural unit in Hsp70 proteins

BACKGROUND

The Hsp70 proteins maintain proteome integrity through the capacity of their nucleotide- and substrate-binding domains (NBD and SBD) to allosterically regulate substrate affinity in a nucleotide-dependent manner. Crystallographic studies showed that Hsp70 allostery relies on formation of contacts between ATP-bound NBD and an interdomain linker, accompanied by SBD subdomains docking onto distinct sites of the NBD leading to substrate release. However, the mechanics of ATP-induced SBD subdomains detachment is largely unknown.

METHODS

Here, we investigated the structural and allosteric properties of human HSPA1A using hydrogen/deuterium exchange mass spectrometry, ATPase assays, surface plasmon resonance and fluorescence polarization-based substrate binding assays.

RESULTS

Analysis of HSPA1A proteins bearing mutations at the interface of SBD subdomains close to the interdomain linker (amino acids L399, L510, I515, and D529) revealed that this region forms a folding unit stabilizing the structure of both SBD subdomains in the nucleotide-free state. The introduced mutations modulate HSPA1A allostery as they localize to the NBD-SBD interfaces in the ATP-bound protein.

CONCLUSIONS

These findings show that residues forming the hydrophobic structural unit stabilizing the SBD structure are relocated during ATP-activated detachment of the SBD subdomains to different NBD-SBD docking interfaces enabling HSPA1A allostery.

GENERAL SIGNIFICANCE

Mutation-induced perturbations tuned HSPA1A sensitivity to peptide/protein substrates and to Hsp40 in a way that is common for other Hsp70 proteins. Our results provide an insight into structural rearrangements in the SBD of Hsp70 proteins and highlight HSPA1A-specific allostery features, which is a prerequisite for selective targeting in Hsp-related pathologies.

Computational prediction of MoRFs based on protein sequences and minimax probability machine

Background

Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods.

Results

In this study, a new sequence-based method, named as MoRF_MPM, is proposed for predicting MoRFs. MoRF_MPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRF_CHiBi on the same test sets, MoRF_MPM not only obtains higher AUC, but also obtains higher TPR at low FPR.

Conclusions

The features used in MoRF_MPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRF_MPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.

Studies on Molecular Dynamics of Intrinsically Disordered Proteins and Their Fuzzy Complexes: A Mini-Review

The molecular dynamics (MD) method is a promising approach toward elucidating the molecular mechanisms of intrinsically disordered regions (IDRs) of proteins and their fuzzy complexes. This mini-review introduces recent studies that apply MD simulations to investigate the molecular recognition of IDRs. Firstly, methodological issues by which MD simulations treat IDRs, such as developing force fields, treating periodic boundary conditions, and enhanced sampling approaches, are discussed. Then, several examples of the applications of MD to investigate molecular interactions of IDRs in terms of the two kinds of complex formations; coupled-folding and binding and fuzzy complex. MD simulations provide insight into the molecular mechanisms of these binding processes by sampling conformational ensembles of flexible IDRs. In particular, we focused on all-atom explicit-solvent MD simulations except for studies of higher-order assembly of IDRs. Recent advances in MD methods, and computational power make it possible to dissect the molecular details of realistic molecular systems involving the dynamic behavior of IDRs.

Dynamics and Molecular Mechanisms of p53 Transcriptional Activation

The "guardian of the genome", p53, functions as a tumor suppressor that responds to cell stressors such as DNA damage, hypoxia, and tumor formation by inducing cell-cycle arrest, senescence, or apoptosis. Mutation of p53 disrupts its tumor suppressor function, leading to various types of human cancers. One particular mutant, R175H, is a structural mutant that inactivates the DNA damage response pathway and acquires oncogenic functions that promotes both cancer and drug resistance. Our current work aims to understand how p53 wild-type function is disrupted due to the R175H mutation. We use a series of atomistic integrative models built previously from crystal structures of the full-length p53 tetramer bound to DNA and model the R175H mutant using in silico site-directed mutagenesis. Explicitly solvated all-atom molecular dynamics (MD) simulations on wild-type and the R175H mutant p53 reveal insights into how wild-type p53 searches and recognizes DNA, and how this mechanism is disrupted as a result of the R175H mutation. Specifically, our work reveals the optimal quaternary DNA binding mode of the DNA binding domain and shows how this binding mode is altered via symmetry loss as a result of the R175H mutation, indicating a recognition mechanism that is reminiscent of the asymmetry seen in wild type p53 binding to nonspecific genomic elements. Altogether our work sheds new light into the hitherto unseen molecular mechanisms governing transcription factor, DNA recognition.

Metal cofactor modulated folding and target recognition of HIV-1 NCp7

The HIV-1 nucleocapsid 7 (NCp7) plays crucial roles in multiple stages of HIV-1 life cycle, and its biological functions rely on the binding of zinc ions. Understanding the molecular mechanism of how the zinc ions modulate the conformational dynamics and functions of the NCp7 is essential for the drug development and HIV-1 treatment. In this work, using a structure-based coarse-grained model, we studied the effects of zinc cofactors on the folding and target RNA(SL3) recognition of the NCp7 by molecular dynamics simulations. After reproducing some key properties of the zinc binding and folding of the NCp7 observed in previous experiments, our simulations revealed several interesting features in the metal ion modulated folding and target recognition. Firstly, we showed that the zinc binding makes the folding transition states of the two zinc fingers less structured, which is in line with the Hammond effect observed typically in mutation, temperature or denaturant induced perturbations to protein structure and stability. Secondly, We showed that there exists mutual interplay between the zinc ion binding and NCp7-target recognition. Binding of zinc ions enhances the affinity between the NCp7 and the target RNA, whereas the formation of the NCp7-RNA complex reshapes the intrinsic energy landscape of the NCp7 and increases the stability and zinc affinity of the two zinc fingers. Thirdly, by characterizing the effects of salt concentrations on the target RNA recognition, we showed that the NCp7 achieves optimal balance between the affinity and binding kinetics near the physiologically relevant salt concentrations. In addition, the effects of zinc binding on the inter-domain conformational flexibility and folding cooperativity of the NCp7 were also discussed.

Intrinsic Disorder in a Well-Folded Globular Protein

The folded structure of the heterodimeric sweet protein monellin mimics single-chain proteins with topology β1-α1-β2-β3-β4-β5 (chain A: β3-β4-β5; chain B: β1-α1-β2). Furthermore, like naturally occurring single-chain proteins of a similar size, monellin folds cooperatively with no detectable intermediates. However, the two monellin chains, A and B, are marginally structured in isolation and fold only upon binding to each other. Thus, monellin presents a unique opportunity to understand the design of intrinsically disordered proteins that fold upon binding. Here, we study the folding of a single-chain variant of monellin (scMn) using simulations of an all heavy-atom structure-based model. These simulations can explain mechanistic details derived from scMn experiments performed using several different structural probes. scMn folds cooperatively in our structure-based simulations, as is also seen in experiments. We find that structure formation near the transition-state ensemble of scMn is not uniformly distributed but is localized to a hairpin-like structure which contains one strand from each chain (β2, β3). Thus, the sequence and the underlying energetics of heterodimeric monellin promote the early formation of the interchain interface (β2-β3). By studying computational scMn mutants whose "interchain" interactions are deleted, we infer that this energy distribution allows the two protein chains to remain largely disordered when this interface is not folded. From these results, we suggest that cutting the protein backbone of a globular protein between residues which lie within its folding nucleus may be one way to construct two disordered fragments which fold upon binding.

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.

Mechanisms of transcriptional regulation by p53

p53 is a transcription factor that suppresses tumor growth through regulation of dozens of target genes with diverse biological functions. The activity of this master transcription factor is inactivated in nearly all tumors, either by mutations in the TP53 locus or by oncogenic events that decrease the activity of the wild-type protein, such as overexpression of the p53 repressor MDM2. However, despite decades of intensive research, our collective understanding of the p53 signaling cascade remains incomplete. In this review, we focus on recent advances in our understanding of mechanisms of p53-dependent transcriptional control as they relate to five key areas: (1) the functionally distinct N-terminal transactivation domains, (2) the diverse regulatory roles of its C-terminal domain, (3) evidence that p53 is solely a direct transcriptional activator, not a direct repressor, (4) the ability of p53 to recognize many of its enhancers across diverse chromatin environments, and (5) mechanisms that modify the p53-dependent transcriptional program in a context-dependent manner.

Probing the dynamics of disorder

Intrinsically disordered proteins (IDPs) play an important role in many diseases. IDPs are a large and important class of proteins; estimated to represent a significant fraction of many genomes. In contrast to protein-protein interactions between well-folded proteins, IDPs typically bind to targets using short consecutive stretches of amino acids. Structures of IDPs complexed with a target have shown great diversity in binding modes. However, how this binding diversity is achieved at the molecular level is not well understood. Unfortunately, the prediction and detailed characterization of IDPs experimentally is still a very challenging task; however molecular mechanics based molecular dynamics simulation are well suited for studying the dynamic behavior of IDPs. We look into the current state for force fields for simulating IDPs and an example of how these methods have been applied to the p53 protein. p53 is one of the most extensively studied IDPs, with multiple intrinsically disordered regulatory domains that mediate its interactions with many other proteins engaged in multiple biological pathways. We show how molecular dynamics simulations can be used to elucidate on the mechanisms involved in selection of the different binding partners.

Mechanistic roles of protein disorder within transcription

Understanding the interactions of proteins involved in transcriptional regulation is critical to describing biological systems because they control the expression profile of the cell. Yet sadly they belong to a less well biophysically characterized subset of proteins; they frequently contain long disordered regions that are highly dynamic. A key question therefore is, why? What functional roles does protein disorder play in transcriptional regulation? Experimental data exemplifying these roles are starting to emerge, with common themes being enabling complexity within networks and quick responses. Most recently a role for disorder in mediating phase transitions of membrane-less organelles has been proposed.

The transient manifold structure of the p53 extreme C-terminal domain: insight into disorder, recognition, and binding promiscuity by molecular dynamics simulations

The extreme C-terminus of the p53 tumour suppressor (p53-CTD) is a 30 residue long intrinsically disordered region, responsible for regulating the p53 DNA binding activity. Extensive conformational sampling through MD simulations of a p53-CTD derived peptide in solution highlights its propensity to form short and stable secondary structure motifs, specifically localized within the sequence.