Fuzziness enables context dependence of protein interactions

Fuzzy protein-DNA interactions and beyond: A common theme in transcription?

Gene expression regulation requires both diversity and specificity. How can these two contradictory conditions be reconciled? Dynamic DNA recognition mechanisms lead to heterogeneous bound conformations, which can be shifted by the cellular cues. Here we summarise recent experimental evidence on how fuzzy interactions contribute to chromatin remodelling, regulation of DNA replication and repair and transcription factor binding. We describe how the binding mode continuum between DNA and regulatory factors lead to variable, multisite contact patterns; polyelectrolyte competitions; on-the-fly shape readouts; autoinhibition controlled by posttranslational modifications or dynamic oligomerisation mechanisms. Increasing experimental evidence supports the rugged energy landscape of the bound protein-DNA assembly, modulation of which leads to distinct functional outcomes. Recent results suggest the evolutionary conservation of these combinatorial mechanisms with moderate sequence constraints in the malleable transcriptional machinery.

The effect of phosphorylation efficiency on the oncogenic properties of the protein E7 from high-risk HPV

The Human papillomavirus (HPV) causes tumors in part by hijacking the host cell cycle and forcing uncontrolled cellular division. While there are >200 genotypes of HPV, 15 are classified as high-risk and have been shown to transform infected cells and contribute to tumor formation. The remaining low-risk genotypes are not considered oncogenic and result in benign skin lesions. In high-risk HPV, the oncoprotein E7 contributes to the dysregulation of cell cycle regulatory mechanisms. High-risk E7 is phosphorylated in cells at two conserved serine residues by Casein Kinase 2 (CK2) and this phosphorylation event increases binding affinity for cellular proteins such as the tumor suppressor retinoblastoma (pRb). While low-risk E7 possesses similar serine residues, it is phosphorylated to a lesser degree in cells and has decreased binding capabilities. When E7 binding affinity is decreased, it is less able to facilitate complex interactions between proteins and therefore has less capability to dysregulate the cell cycle. By comparing E7 protein sequences from both low- and high-risk HPV variants and using site-directed mutagenesis combined with NMR spectroscopy and cell-based assays, we demonstrate that the presence of two key nonpolar valine residues within the CK2 recognition sequence, present in low-risk E7, reduces serine phosphorylation efficiency relative to high-risk E7. This results in significant loss of the ability of E7 to degrade the retinoblastoma tumor suppressor protein, thus also reducing the ability of E7 to increase cellular proliferation and reduce senescence. This provides additional insight into the differential E7-mediated outcomes when cells are infected with high-risk verses low-risk HPV. Understanding these oncogenic differences may be important to developing targeted treatment options for HPV-induced cancers.

Genomic language model predicts protein co-regulation and function

Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM's unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.

Protein structure-function continuum model: Emerging nexuses between specificity, evolution, and structure

The rationale for replacing the old binary of structure-function with the trinity of structure, disorder, and function has gained considerable ground in recent years. A continuum model based on the expanded form of the existing paradigm can now subsume importance of both conformational flexibility and intrinsic disorder in protein function. The disorder is actually critical for understanding the protein-protein interactions in many regulatory processes, formation of membrane-less organelles, and our revised notions of specificity as amply illustrated by moonlighting proteins. While its importance in formation of amyloids and function of prions is often discussed, the roles of intrinsic disorder in infectious diseases and protein function under extreme conditions are also becoming clear. This review is an attempt to discuss how our current understanding of protein function, specificity, and evolution fit better with the continuum model. This integration of structure and disorder under a single model may bring greater clarity in our continuing quest for understanding proteins and molecular mechanisms of their functionality.

A druggable conformational switch in the c-MYC transactivation domain

The c-MYC oncogene is activated in over 70% of all human cancers. The intrinsic disorder of the c-MYC transcription factor facilitates molecular interactions that regulate numerous biological pathways, but severely limits efforts to target its function for cancer therapy. Here, we use a reductionist strategy to characterize the dynamic and structural heterogeneity of the c-MYC protein. Using probe-based Molecular Dynamics (MD) simulations and machine learning, we identify a conformational switch in the c-MYC amino-terminal transactivation domain (termed coreMYC) that cycles between a closed, inactive, and an open, active conformation. Using the polyphenol epigallocatechin gallate (EGCG) to modulate the conformational landscape of coreMYC, we show through biophysical and cellular assays that the induction of a closed conformation impedes its interactions with the transformation/transcription domain-associated protein (TRRAP) and the TATA-box binding protein (TBP) which are essential for the transcriptional and oncogenic activities of c-MYC. Together, these findings provide insights into structure-activity relationships of c-MYC, which open avenues towards the development of shape-shifting compounds to target c-MYC as well as other disordered transcription factors for cancer treatment.

The acidic intrinsically disordered region of the inflammatory mediator HMGB1 mediates fuzzy interactions with CXCL12

Chemokine heterodimers activate or dampen their cognate receptors during inflammation. The CXCL12 chemokine forms with the fully reduced (fr) alarmin HMGB1 a physiologically relevant heterocomplex (frHMGB1•CXCL12) that synergically promotes the inflammatory response elicited by the G-protein coupled receptor CXCR4. The molecular details of complex formation were still elusive. Here we show by an integrated structural approach that frHMGB1•CXCL12 is a fuzzy heterocomplex. Unlike previous assumptions, frHMGB1 and CXCL12 form a dynamic equimolar assembly, with structured and unstructured frHMGB1 regions recognizing the CXCL12 dimerization surface. We uncover an unexpected role of the acidic intrinsically disordered region (IDR) of HMGB1 in heterocomplex formation and its binding to CXCR4 on the cell surface. Our work shows that the interaction of frHMGB1 with CXCL12 diverges from the classical rigid heterophilic chemokines dimerization. Simultaneous interference with multiple interactions within frHMGB1•CXCL12 might offer pharmacological strategies against inflammatory conditions.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

Protein Binding: A Fuzzy Concept

Our understanding of protein binding interactions has matured significantly over the last few years, largely as a result of trying to make sense of the binding interactions of intrinsically disordered proteins. Here, we bring together some disparate ideas that have largely developed independently, and show that they can be linked into a coherent picture that provides insight into quantitative aspects of protein interactions, in particular that transient protein interactions are often optimised for speed, rather than tight binding.

Disordered Domain Shifts the Conformational Ensemble of the Folded Regulatory Domain of the Multidomain Oncoprotein c-Src

c-Src kinase is a multidomain non-receptor tyrosine kinase that aberrantly phosphorylates several signaling proteins in cancers. Although the structural properties of the regulatory domains (SH3-SH2) and the catalytic kinase domain have been extensively characterized, there is less knowledge about the N-terminal disordered region (SH4UD) and its interactions with the other c-Src domains. Here, we used domain-selective isotopic labeling combined with the small-angle neutron scattering contrast matching technique to study SH4UD interactions with SH3-SH2. Our results show that in the presence of SH4UD, the radius of gyration (R_g) of SH3-SH2 increases, indicating that it has a more extended conformation. Hamiltonian replica exchange molecular dynamics simulations provide a detailed molecular description of the structural changes in SH4UD-SH3-SH2 and show that the regulatory loops of SH3 undergo significant conformational changes in the presence of SH4UD, while SH2 remains largely unchanged. Overall, this study highlights how a disordered region can drive a folded region of a multidomain protein to become flexible, which may be important for allosteric interactions with binding partners. This may help in the design of therapeutic interventions that target the regulatory domains of this important family of kinases.

Protein interactions: anything new?

How do proteins interact in the cellular environment? Which interactions stabilize liquid-liquid phase separated condensates? Are the concepts, which have been developed for specific protein complexes also applicable to higher-order assemblies? Recent discoveries prompt for a universal framework for protein interactions, which can be applied across the scales of protein communities. Here, we discuss how our views on protein interactions have evolved from rigid structures to conformational ensembles of proteins and discuss the open problems, in particular related to biomolecular condensates. Protein interactions have evolved to follow changes in the cellular environment, which manifests in multiple modes of interactions between the same partners. Such cellular context-dependence requires multiplicity of binding modes (MBM) by sampling multiple minima of the interaction energy landscape. We demonstrate that the energy landscape framework of protein folding can be applied to explain this phenomenon, opening a perspective toward a physics-based, universal model for cellular protein behaviors.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Are casein micelles extracellular condensates formed by liquid-liquid phase separation?

Casein micelles are extracellular polydisperse assemblies of unstructured casein proteins. Caseins are the major component of milk. Within casein micelles, casein molecules are stabilised by binding to calcium phosphate nanoclusters and, by acting as molecular chaperones, through multivalent interactions. In light of such interactions, we discuss whether casein micelles can be considered as extracellular condensates formed by liquid-liquid phase separation. We analyse the sequence, structure and interactions of caseins in comparison to proteins forming intracellular condensates. Furthermore, we review the similarities between caseins and small heat-shock proteins whose chaperone activity is linked to phase separation of proteins. By bringing these observations together, we describe a regulatory mechanism for protein condensates, as exemplified by casein micelles.

Affinity of disordered protein complexes is modulated by entropy-energy reinforcement

Intrinsically disordered proteins (IDPs), which are very common and essential to many biological activities, sometimes function via interaction with another IDP and form a fuzzy complex, which can be highly stable. It is unclear what the biophysical forces are that govern their thermodynamics and specificity, which are essential for de novo fuzzy complex design. Here, we explored the fuzzy complex formed between ProTα and H1, which are oppositely charged IDPs, by swapping the charges between them, generating variants that have either greater polyampholytic or polyelectrolytic nature as well as different charge patterns. Charge swapping and shuffling dramatically change the affinity of the fuzzy complex, which is contributed to by both enthalpy and entropy, where the latter is dominated by counterion release.

The association between two intrinsically disordered proteins (IDPs) may produce a fuzzy complex characterized by a high binding affinity, similar to that found in the ultrastable complexes formed between two well-structured proteins. Here, using coarse-grained simulations, we quantified the biophysical forces driving the formation of such fuzzy complexes. We found that the high-affinity complex formed between the highly and oppositely charged H1 and ProTα proteins is sensitive to electrostatic interactions. We investigated 52 variants of the complex by swapping charges between the two oppositely charged proteins to produce sequences whose negatively or positively charged residue content was more homogeneous or heterogenous (i.e., polyelectrolytic or polyampholytic, having higher or lower absolute net charges, respectively) than the wild type. We also changed the distributions of oppositely charged residues within each participating sequence to produce variants in which the charges were segregated or well mixed. Both types of changes significantly affect binding affinity in fuzzy complexes, which is governed by both enthalpy and entropy. The formation of H1–ProTa is supported by an increase in configurational entropy and by entropy due to counterion release. The latter can be twice as large as the former, illustrating the dominance of counterion entropy in modulating the binding thermodynamics. Complexes formed between proteins with greater absolute net charges are more stable, both enthalpically and entropically, indicating that enthalpy and entropy have a mutually reinforcing effect. The sensitivity of the thermodynamics of the complex to net charge and the charge pattern within each of the binding constituents may provide a means to achieve binding specificity between IDPs.

FuzDrop on AlphaFold: visualizing the sequence-dependent propensity of liquid-liquid phase separation and aggregation of proteins

Many proteins perform their functions within membraneless organelles, where they form a liquid-like condensed state, also known as droplet state. The FuzDrop method predicts the probability of spontaneous liquid-liquid phase separation of proteins and provides a sequence-based score to identify the regions that promote this process. Furthermore, the FuzDrop method estimates the propensity of conversion of proteins to the amyloid state, and identifies aggregation hot-spots, which can drive the irreversible maturation of the liquid-like droplet state. These predictions can also identify mutations that can induce formation of amyloid aggregates, including those implicated in human diseases. To facilitate the interpretation of the predictions, the droplet-promoting and aggregation-promoting regions can be visualized on protein structures generated by AlphaFold. The FuzDrop server (https://fuzdrop.bio.unipd.it) thus offers insights into the complex behavior of proteins in their condensed states and facilitates the understanding of the functional relationships of proteins.

Liquid-liquid phase separation as an organizing principle of intracellular space: overview of the evolution of the cell compartmentalization concept

At the turn of the twenty-first century, fundamental changes took place in the understanding of the structure and function of proteins and then in the appreciation of the intracellular space organization. A rather mechanistic model of the organization of living matter, where the function of proteins is determined by their rigid globular structure, and the intracellular processes occur in rigidly determined compartments, was replaced by an idea that highly dynamic and multifunctional "soft matter" lies at the heart of all living things. According this "new view", the most important role in the spatio-temporal organization of the intracellular space is played by liquid-liquid phase transitions of biopolymers. These self-organizing cellular compartments are open dynamic systems existing at the edge of chaos. They are characterized by the exceptional structural and compositional dynamics, and their multicomponent nature and polyfunctionality provide means for the finely tuned regulation of various intracellular processes. Changes in the external conditions can cause a disruption of the biogenesis of these cellular bodies leading to the irreversible aggregation of their constituent proteins, followed by the transition to a gel-like state and the emergence of amyloid fibrils. This work represents a historical overview of changes in our understanding of the intracellular space compartmentalization. It also reflects methodological breakthroughs that led to a change in paradigms in this area of science and discusses modern ideas about the organization of the intracellular space. It is emphasized here that the membrane-less organelles have to combine a certain resistance to the changes in their environment and, at the same time, show high sensitivity to the external signals, which ensures the normal functioning of the cell.

The protein disorder cycle

This mini-review represents a brief, disorder-centric consideration of the interplay between order and disorder in proteins. The goal here is to show that inside the cell, folding, non-folding, and misfolding of proteins are interlinked on multiple levels. This is evidenced by the highly heterogeneous spatio-temporal structural organization of a protein molecule, where one can find differently (dis)ordered components that can undergo local or global order-to-disorder and disorder-to-order transitions needed for functionality. This is further illustrated by the fact that at particular moments of their life, most notably during their synthesis and degradation, all proteins are at least partially disordered. In addition to these intrinsic forms of disorder, proteins are constantly facing extrinsic disorder, which is intrinsic disorder in their functional partners. All this comprises the multileveled protein disorder cycle.

FuzDB: a new phase in understanding fuzzy interactions

Fuzzy interactions are specific, variable contacts between proteins and other biomolecules (proteins, DNA, RNA, small molecules) formed in accord to the cellular context. Fuzzy interactions have recently been demonstrated to regulate biomolecular condensates generated by liquid-liquid phase separation. The FuzDB v4.0 database (https://fuzdb.org) assembles experimentally identified examples of fuzzy interactions, where disordered regions mediate functionally important, context-dependent contacts between the partners in stoichiometric and higher-order assemblies. The new version of FuzDB establishes cross-links with databases on structure (PDB, BMRB, PED), function (ELM, UniProt) and biomolecular condensates (PhaSepDB, PhaSePro, LLPSDB). FuzDB v4.0 is a source to decipher molecular basis of complex cellular interaction behaviors, including those in protein droplets.

An intrinsically disordered pathological prion variant Y145Stop converts into self-seeding amyloids via liquid-liquid phase separation

Biomolecular condensation via liquid-liquid phase separation of intrinsically disordered proteins/regions (IDPs/IDRs) along with other biomolecules is proposed to control critical cellular functions, whereas aberrant phase transitions are associated with a range of neurodegenerative diseases. Here, we show that a disease-associated stop codon mutation of the prion protein (PrP) at tyrosine 145 (Y145Stop), resulting in a truncated, highly disordered, N-terminal IDR, spontaneously phase-separates into dynamic liquid-like droplets. Phase separation of this highly positively charged N-terminal segment is promoted by the electrostatic screening and a multitude of weak, transient, multivalent, intermolecular interactions. Single-droplet Raman measurements, in conjunction with an array of bioinformatic, spectroscopic, microscopic, and mutagenesis studies, revealed a highly mobile internal organization within the liquid-like condensates. The phase behavior of Y145Stop is modulated by RNA. Lower RNA:protein ratios promote condensation at a low micromolar protein concentration under physiological conditions. At higher concentrations of RNA, phase separation is abolished. Upon aging, these highly dynamic liquid-like droplets gradually transform into ordered, β-rich, amyloid-like aggregates. These aggregates formed via phase transitions display an autocatalytic self-templating characteristic involving the recruitment and binding-induced conformational conversion of monomeric Y145Stop into amyloid fibrils. In contrast to this intrinsically disordered truncated variant, the wild-type full-length PrP exhibits a much lower propensity for both condensation and maturation into amyloids, hinting at a possible protective role of the C-terminal domain. Such an interplay of molecular factors in modulating the protein phase behavior might have much broader implications in cell physiology and disease.

Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes

Accurate and timely functional genome annotation is essential for translating basic pathogen research into clinically impactful advances. Here, through literature curation and structure-function inference, we systematically update the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv. First, we systematically curated annotations for 589 genes from 662 publications, including 282 gene products absent from leading databases. Second, we modeled 1,711 underannotated proteins and developed a semiautomated pipeline that captured shared function between 400 protein models and structural matches of known function on Protein Data Bank, including drug efflux proteins, metabolic enzymes, and virulence factors. In aggregate, these structure- and literature-derived annotations update 940/1,725 underannotated H37Rv genes and generate hundreds of functional hypotheses. Retrospectively applying the annotation to a recent whole-genome transposon mutant screen provided missing function for 48% (13/27) of underannotated genes altering antibiotic efficacy and 33% (23/69) required for persistence during mouse tuberculosis (TB) infection. Prospective application of the protein models enabled us to functionally interpret novel laboratory generated pyrazinamide (PZA)-resistant mutants of unknown function, which implicated the emerging coenzyme A depletion model of PZA action in the mutants’ PZA resistance. Our findings demonstrate the functional insight gained by integrating structural modeling and systematic literature curation, even for widely studied microorganisms. Functional annotations and protein structure models are available at https://tuberculosis.sdsu.edu/H37Rv in human- and machine-readable formats.

IMPORTANCEMycobacterium tuberculosis, the primary causative agent of tuberculosis, kills more humans than any other infectious bacterium. Yet 40% of its genome is functionally uncharacterized, leaving much about the genetic basis of its resistance to antibiotics, capacity to withstand host immunity, and basic metabolism yet undiscovered. Irregular literature curation for functional annotation contributes to this gap. We systematically curated functions from literature and structural similarity for over half of poorly characterized genes, expanding the functionally annotated Mycobacterium tuberculosis proteome. Applying this updated annotation to recent in vivo functional screens added functional information to dozens of clinically pertinent proteins described as having unknown function. Integrating the annotations with a prospective functional screen identified new mutants resistant to a first-line TB drug, supporting an emerging hypothesis for its mode of action. These improvements in functional interpretation of clinically informative studies underscore the translational value of this functional knowledge. Structure-derived annotations identify hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research. More broadly, they provide a systematic framework for improving prokaryotic reference annotations.

The sequence-ensemble relationship in fuzzy protein complexes

Intrinsically disordered proteins (IDPs) interact with globular proteins through a variety of mechanisms, resulting in the structurally heterogeneous ensembles known as fuzzy complexes. While there exists a reasonable comprehension on how IDP sequence determines the unbound IDP ensemble, little is known about what shapes the structural characteristics of IDPs bound to their targets. Using a statistical thermodynamic model, we show that the target-bound ensembles are determined by a simple code that combines the IDP sequence and the distribution of IDP-target interaction hotspots. These two parameters define the conformational space of target-bound IDPs and rationalize the observed structural heterogeneity of fuzzy complexes. The presented model successfully reproduces the dynamical signatures of target-bound IDPs from the NMR relaxation experiments as well as the changes of interaction affinity and the IDP helicity induced by mutations. The model explains how the target-bound IDP ensemble adapts to mutations in order to achieve an optimal balance between conformational freedom and interaction energy. Taken together, the presented sequence-ensemble relationship of fuzzy complexes explains the different manifestations of IDP disorder in folding-upon-binding processes.

Fuzzy protein theory for disordered proteins

Why proteins are fuzzy? Constant adaptation to the cellular environment requires a wide range of changes in protein structure and interactions. Conformational ensembles of disordered proteins in particular exhibit large shifts to activate or inhibit alternative pathways. Fuzziness is critical for liquid–liquid phase separation and conversion of biomolecular condensates into fibrils. Interpretation of these phenomena presents a challenge for the classical structure-function paradigm. Here I discuss a multi-valued formalism, based on fuzzy logic, which can be applied to describe complex cellular behavior of proteins.

Activity of Chemically Synthesized Peptide Encoded by the miR156A Precursor and Conserved in the Brassicaceae Family Plants

It was recently found that the primary transcripts of some microRNA genes (pri-miRNAs) are able to express peptides with 12 to 40 residues in length. These peptides, called miPEPs, participate in the transcriptional regulation of their own pri-miRNAs. In our previous studies, we used bioinformatic approach for comparative analysis of pri-miRNA sequences in plant genomes to identify a new group of miPEPs (miPEP-156a peptides) encoded by pri-miR156a in several dozen species of the Brassicaceae family. Exogenous miPEP-156a peptides could efficiently penetrate into the plant seedlings through the root system and spread systemically to the leaves. The peptides produced moderate morphological effect accelerating primary root growth. In parallel, the miPEP-156a peptides upregulated expression of their own pri-miR156a. Importantly, the observed effects at both morphological and molecular levels correlated with the peptide ability to quickly translocate into the cell nucleus and to bind chromatin. In this work, we established secondary structure of the miPEP-156a and demonstrated its changes induced by formation of the peptide complex with DNA.

Is the H4 histone tail intrinsically disordered or intrinsically multifunctional?

The structural versatility of histone tails is one of the key elements in the organisation of chromatin, which allows for the compact storage of genomic information. However, this structural diversity also complicates experimental and computational studies. Here, the potential and free energy landscape for the isolated and bound H4 histone tail are explored. The landscapes exhibit a set of distinct structural ensembles separated by high energy barriers, with little difference between isolated and bound tails. This consistency is a desirable feature that facilitates the formation of transient interactions, which are required for the liquid-like chromatin organisation. The existence of multiple, distinct structures on a multifunnel energy landscape is likely to be associated with multifunctionality, i.e. a set of evolved, distinct functions. Contrasting it with previously reported results for other disordered peptides, this type of landscape may be associated with a conformational selection based binding mechanism. Given the similarity to other systems exhibiting similar multifunnel energy landscapes, the disorder in histone tails might be better described in context of multifunctionality.

Generic nature of the condensed states of proteins

Proteins undergoing liquid-liquid phase separation are being discovered at an increasing rate. Since at the high concentrations present in the cell most proteins would be expected to form a liquid condensed state, this state should be considered to be a fundamental state of proteins along with the native state and the amyloid state. Here we discuss the generic nature of the liquid-like and solid-like condensed states, and describe a wide variety of biological functions conferred by these condensed states.

Key phases in the formation of caveolae

Caveolae are abundant plasma membrane pits formed by the coordinated action of peripheral and integral membrane proteins and membrane lipids. Here, we discuss recent studies that are starting to provide a glimpse of how filamentous cavin proteins, membrane-embedded caveolin proteins, and specific plasma membrane lipids are brought together to make the unique caveola surface domain. Protein assembly involves multiple low-affinity interactions that are dependent on 'fuzzy' charge-dependent interactions mediated in part by disordered cavin and caveolin domains. We propose that cavins help generate a lipid domain conducive to full insertion of caveolin into the bilayer to promote caveola formation. The synergistic assembly of these dynamic protein complexes supports the formation of a metastable membrane domain that can be readily disassembled both in response to cellular stress and during endocytic trafficking. We present a mechanistic model for generation of caveolae based on these new insights.

Fuzziness and Frustration in the Energy Landscape of Protein Folding, Function, and Assembly

Are all protein interactions fully optimized? Do suboptimal interactions compromise specificity? What is the functional impact of frustration? Why does evolution not optimize some contacts? Proteins and their complexes are best described as ensembles of states populating an energy landscape. These ensembles vary in breadth from narrow ensembles clustered around a single average X-ray structure to broader ensembles encompassing a few different functional "taxonomic" states on to near continua of rapidly interconverting conformations, which are called "fuzzy" or even "intrinsically disordered". Here we aim to provide a comprehensive framework for confronting the structural and dynamical continuum of protein assemblies by combining the concepts of energetic frustration and interaction fuzziness. The diversity of the protein structural ensemble arises from the frustrated conflicts between the interactions that create the energy landscape. When frustration is minimal after folding, it results in a narrow ensemble, but residual frustrated interactions result in fuzzy ensembles, and this fuzziness allows a versatile repertoire of biological interactions. Here we discuss how fuzziness and frustration play off each other as proteins fold and assemble, viewing their significance from energetic, functional, and evolutionary perspectives.We demonstrate, in particular, that the common physical origin of both concepts is related to the ruggedness of the energy landscapes, intramolecular in the case of frustration and intermolecular in the case of fuzziness. Within this framework, we show that alternative sets of suboptimal contacts may encode specificity without achieving a single structural optimum. Thus, we demonstrate that structured complexes may not be optimized, and energetic frustration is realized via different sets of contacts leading to multiplicity of specific complexes. Furthermore, we propose that these suboptimal, frustrated, or fuzzy interactions are under evolutionary selection and expand the biological repertoire by providing a multiplicity of biological activities. In accord, we show that non-native interactions in folding or interaction landscapes can cooperate to generate diverse functional states, which are essential to facilitate adaptation to different cellular conditions. Thus, we propose that not fully optimized structures may actually be beneficial for biological activities of proteins via an alternative set of suboptimal interactions. The importance of such variability has not been recognized across different areas of biology.This account provides a modern view on folding, function, and assembly across the protein universe. The physical framework presented here is applicable to the structure and dynamics continuum of proteins and opens up new perspectives for drug design involving not fully structured, highly dynamic protein assemblies.

Co-condensation between transcription factor and coactivator p300 modulates transcriptional bursting kinetics

The coactivator p300/CREB-binding protein (CBP) regulates genes by facilitating the assembly of transcriptional machinery and by acetylating histones and other factors. However, it remains mostly unclear how both functions of p300 are dynamically coordinated during gene control. Here, we showed that p300 can orchestrate two functions through the formation of dynamic clusters with certain transcription factors (TFs), which is mediated by the interactions between a TF's transactivation domain (TAD) and the intrinsically disordered regions of p300. Co-condensation can enable spatially defined, all-or-none activation of p300's catalytic activity, priming the recruitment of coactivators, including Brd4. We showed that co-condensation can modulate transcriptional initiation rate and burst duration of target genes, underlying nonlinear gene regulatory functions. Such modulation is consistent with how p300 might shape gene bursting kinetics globally. Altogether, these results suggest an intriguing gene regulation mechanism, in which TF and p300 co-condensation contributes to transcriptional bursting regulation and cooperative gene control.

Cavin1 intrinsically disordered domains are essential for fuzzy electrostatic interactions and caveola formation

Caveolae are spherically shaped nanodomains of the plasma membrane, generated by cooperative assembly of caveolin and cavin proteins. Cavins are cytosolic peripheral membrane proteins with negatively charged intrinsically disordered regions that flank positively charged α-helical regions. Here, we show that the three disordered domains of Cavin1 are essential for caveola formation and dynamic trafficking of caveolae. Electrostatic interactions between disordered regions and α-helical regions promote liquid-liquid phase separation behaviour of Cavin1 in vitro, assembly of Cavin1 oligomers in solution, generation of membrane curvature, association with caveolin-1, and Cavin1 recruitment to caveolae in cells. Removal of the first disordered region causes irreversible gel formation in vitro and results in aberrant caveola trafficking through the endosomal system. We propose a model for caveola assembly whereby fuzzy electrostatic interactions between Cavin1 and caveolin-1 proteins, combined with membrane lipid interactions, are required to generate membrane curvature and a metastable caveola coat.

Recent Developments in the Field of Intrinsically Disordered Proteins: Intrinsic Disorder-Based Emergence in Cellular Biology in Light of the Physiological and Pathological Liquid-Liquid Phase Transitions

This review deals with two important concepts-protein intrinsic disorder and proteinaceous membrane-less organelles (PMLOs). The past 20 years have seen an upsurge of scientific interest in these phenomena. However, neither are new discoveries made in this century, but instead are timely reincarnations of old ideas that were mostly ignored by the scientific community for a long time. Merging these concepts in the form of the intrinsic disorder-based biological liquid-liquid phase separation provides a basis for understanding the molecular mechanisms of PMLO biogenesis.

Liquid-Liquid Phase Separation by Intrinsically Disordered Protein Regions of Viruses: Roles in Viral Life Cycle and Control of Virus-Host Interactions

Intrinsically disordered proteins (IDPs) are unable to adopt a unique 3D structure under physiological conditions and thus exist as highly dynamic conformational ensembles. IDPs are ubiquitous and widely spread in the protein realm. In the last decade, compelling experimental evidence has been gathered, pointing to the ability of IDPs and intrinsically disordered regions (IDRs) to undergo liquid-liquid phase separation (LLPS), a phenomenon driving the formation of membrane-less organelles (MLOs). These biological condensates play a critical role in the spatio-temporal organization of the cell, where they exert a multitude of key biological functions, ranging from transcriptional regulation and silencing to control of signal transduction networks. After introducing IDPs and LLPS, we herein survey available data on LLPS by IDPs/IDRs of viral origin and discuss their functional implications. We distinguish LLPS associated with viral replication and trafficking of viral components, from the LLPS-mediated interference of viruses with host cell functions. We discuss emerging evidence on the ability of plant virus proteins to interfere with the regulation of MLOs of the host and propose that bacteriophages can interfere with bacterial LLPS, as well. We conclude by discussing how LLPS could be targeted to treat phase separation-associated diseases, including viral infections.

The 15-Amino Acid Repeat Region of Adenomatous Polyposis Coli Is Intrinsically Disordered and Retains Conformational Flexibility upon Binding β-Catenin

The tumor suppressor Adenomatous polyposis coli (APC) is a large, multidomain protein with many identified cellular functions. The best characterized role of APC is to scaffold a protein complex that negatively regulates Wnt signaling via β-catenin destruction. This destruction is mediated by β-catenin binding to centrally located 15- and 20-amino acid repeat regions of APC. More than 80% of cancers of the colon and rectum present with an APC mutation. Most carcinomas with mutant APC express a truncated APC protein that retains the ∼200-amino acid long' 15-amino acid repeat region'. This study demonstrates that the 15-amino acid repeat region of APC is intrinsically disordered. We investigated the backbone dynamics in the presence of β-catenin and predicted residues that may contribute to transient secondary features. This study reveals that the 15-amino acid region of APC retains flexibility upon binding β-catenin and that APC does not have a single, observable "highest-affinity" binding site for β-catenin. This flexibility potentially allows β-catenin to be more readily captured by APC and then remain accessible to other elements of the destruction complex for subsequent processing.

Protein phase separation: A novel therapy for cancer?

In recent years, phase separation has been increasingly reported to play a pivotal role in a wide range of biological processes. Due to the close relationships between cancer and disorders in intracellular physiological function, the identification of new mechanisms involved in intracellular regulation has been regarded as a new direction for cancer therapy. Introducing the concept of phase separation into complex descriptions of disease mechanisms may provide many different insights. Here, we review the recent findings on the phase separation of cancer-related proteins, describing the possible relationships between phase separation and key proteins associated with cancer and indicate possible regulatory modalities, especially drug candidates for phase separation, which may provide more effective strategies for cancer therapy.

The precious fluorine on the ring: fluorine NMR for biological systems

The fluorine-19 nucleus was recognized early to harbor exceptional properties for NMR spectroscopy. With 100% natural abundance, a high gyromagnetic ratio (83% sensitivity compared to 1H), a chemical shift that is extremely sensitive to its surroundings and near total absence in biological systems, it was destined to become a favored NMR probe, decorating small and large molecules. However, after early excitement, where uptake of fluorinated aromatic amino acids was explored in a series of animal studies, 19F-NMR lost popularity, especially in large molecular weight systems, due to chemical shift anisotropy (CSA) induced line broadening at high magnetic fields. Recently, two orthogonal approaches, (i) CF3 labeling and (ii) aromatic 19F-13C labeling leveraging the TROSY (Transverse Relaxation Optimized Spectroscopy) effect have been successfully applied to study large biomolecular systems. In this perspective, we will discuss the fascinating early work with fluorinated aromatic amino acids, which reveals the enormous potential of these non-natural amino acids in biological NMR and the potential of 19F-NMR to characterize protein and nucleic acid structure, function and dynamics in the light of recent developments. Finally, we explore how fluorine NMR might be exploited to implement small molecule or fragment screens that resemble physiological conditions and discuss the opportunity to follow the fate of small molecules in living cells.

Crystal Structure of β-Arrestin 2 in Complex with CXCR7 Phosphopeptide

β-Arrestins (βarrs) critically regulate G-protein-coupled receptor (GPCR) signaling and trafficking. βarrs have two isoforms, βarr1 and βarr2. Receptor phosphorylation is a key determinant for the binding of βarrs, and understanding the intricate details of receptor-βarr interaction is the next frontier in GPCR structural biology. The high-resolution structure of active βarr1 in complex with a phosphopeptide derived from GPCR has been revealed, but that of βarr2 remains elusive. Here, we present a 2.3-Å crystal structure of βarr2 in complex with a phosphopeptide (C7pp) derived from the carboxyl terminus of CXCR7. The structural analysis of C7pp-bound βarr2 reveals key differences from the previously determined active conformation of βarr1. One of the key differences is that C7pp-bound βarr2 shows a relatively small inter-domain rotation. Antibody-fragment-based conformational sensor and hydrogen/deuterium exchange experiments further corroborated the structural features of βarr2 and suggested that βarr2 adopts a range of inter-domain rotations.

Sequence-based prediction of protein binding mode landscapes

Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways.

Great advances have been made in the last several decades in deciphering how the behavior of proteins is encoded in their amino acid sequences. A variety of sequence-based prediction methods have been developed to estimate a wide range of properties of proteins, including secondary structure propensity, native state structures, preference for being disordered and tendency to aggregate. Much less is known, however, about the rules that regulate the conformational changes of proteins upon binding. In particular, many proteins change their binding modes upon interacting with different partners, or as a consequence of post-translational modifications or changes in the cellular milieu. Here we address the problem of how amino acid sequences can encode different binding modes depending on their binding partners, and describe the FuzPred method of predicting context-dependent binding modes.

Structural Fuzziness of the RNA-Organizing Protein SERF Determines a Toxic Gain-of-interaction

The mechanisms by which protein complexes convert from functional to pathogenic are the subject of intensive research. Here, we report how functionally unfavorable protein interactions can be induced by structural fuzziness, i.e., by persisting conformational disorder in protein complexes. We show that extreme disorder in the bound state transforms the intrinsically disordered protein SERF1a from an RNA-organizing factor into a pathogenic enhancer of alpha-synuclein (aSyn) amyloid toxicity. We demonstrate that SERF1a promotes the incorporation of RNA into nucleoli and liquid-like artificial RNA-organelles by retaining an unusually high degree of conformational disorder in the RNA-bound state. However, this type of structural fuzziness also determines an undifferentiated interaction with aSyn. RNA and aSyn both bind to one identical, positively charged site of SERF1a by an analogous electrostatic binding mode, with similar binding affinities, and without any observable disorder-to-order transition. The absence of primary or secondary structure discriminants results in SERF1a being unable to select between nucleic acid and amyloidogenic protein, leading the pro-amyloid aSyn:SERF1a interaction to prevail in the cytosol under conditions of cellular stress. We suggest that fuzzy disorder in SERF1a complexes accounts for an adverse gain-of-interaction which favors toxic binding to aSyn at the expense of nontoxic RNA binding, thereby leading to a functionally distorted and pathogenic process. Thus, structural fuzziness constitutes a direct link between extreme conformational flexibility, amyloid aggregation, and the malfunctioning of RNA-associated cellular processes, three signatures of neurodegenerative proteinopathies.

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

The sequence-structure-function paradigm of proteins has been revolutionized by the discovery of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In contrast to traditional ordered proteins, IDPs/IDRs are unstructured under physiological conditions. The absence of well-defined three-dimensional structures in the free state of IDPs/IDRs is fundamental to their function. Folding upon binding is an important mode of molecular recognition for IDPs/IDRs. While great efforts have been devoted to investigating the complex structures and binding kinetics and affinities, our knowledge on the binding mechanisms of IDPs/IDRs remains very limited. Here, we review recent advances on the binding mechanisms of IDPs/IDRs. The structures and kinetic parameters of IDPs/IDRs can vary greatly, and the binding mechanisms can be highly dependent on the structural properties of IDPs/IDRs. IDPs/IDRs can employ various combinations of conformational selection and induced fit in a binding process, which can be templated by the target and/or encoded by the IDP/IDR. Further studies should provide deeper insights into the molecular recognition of IDPs/IDRs and enable the rational design of IDP/IDR binding mechanisms in the future.

Dynamics of the intrinsically disordered protein NUPR1 in isolation and in its fuzzy complexes with DNA and prothymosin α

Intrinsically disordered proteins (IDPs) explore diverse conformations in their free states and, a few of them, also in their molecular complexes. This functional plasticity is essential for the function of IDPs, although their dynamics in both free and bound states is poorly understood. NUPR1 is a protumoral multifunctional IDP, activated during the acute phases of pancreatitis. It interacts with DNA and other IDPs, such as prothymosin α (ProTα), with dissociation constants of ~0.5 μM, and a 1:1 stoichiometry. We studied the structure and picosecond-to-nanosecond (ps-ns) dynamics by using both NMR and SAXS in: (i) isolated NUPR1; (ii) the NUPR1/ProTα complex; and (iii) the NUPR1/double stranded (ds) GGGCGCGCCC complex. Our SAXS findings show that NUPR1 remained disordered when bound to either partner, adopting a worm-like conformation; the fuzziness of bound NUPR1 was also pinpointed by NMR. Residues with the largest values of the relaxation rates (R₁, R_1ρ, R₂ and η_xy), in the free and bound species, were mainly clustered around the 30s region of the sequence, which agree with one of the protein hot-spots already identified by site-directed mutagenesis. Not only residues in this region had larger relaxation rates, but they also moved slower than the rest of the molecule, as indicated by the reduced spectral density approach (RSDA). Upon binding, the energy landscape of NUPR1 was not funneled down to a specific, well-folded conformation, but rather its backbone flexibility was kept, with distinct motions occurring at the hot-spot region.

Sequence-Specific DNA Binding by Noncovalent Peptide-Azocyclodextrin Dimer Complex as a Suitable Model for Conformational Fuzziness

Transcription factors are proteins lying at the endpoint of signaling pathways that control the complex process of DNA transcription. Typically, they are structurally disordered in the inactive state, but in response to an external stimulus, like a suitable ligand, they change their conformation, thereby activating DNA transcription in a spatiotemporal fashion. The observed disorder or fuzziness is functionally beneficial because it can add adaptability, versatility, and reversibility to the interaction. In this context, mimetics of the basic region of the GCN4 transcription factor (Tf) and their interaction with dsDNA sequences would be suitable models to explore the concept of conformational fuzziness experimentally. Herein, we present the first example of a system that mimics the DNA sequence-specific recognition by the GCN4 Tf through the formation of a non- covalent tetra-component complex: peptide–azoβ-CyD(dimer)–peptide–DNA. The non-covalent complex is constructed on the one hand by a 30 amino acid peptide corresponding to the basic region of GCN4 and functionalized with an adamantane moiety, and on the other hand an allosteric receptor, the azoCyDdimer, that has an azobenzene linker connecting two β-cyclodextrin units. The azoCyDdimer responds to light stimulus, existing as two photo-states: the first thermodynamically stable with an E:Z isomer ratio of 95:5 and the second obtained after irradiation with ultraviolet light, resulting in a photostationary state with a 60:40 E:Z ratio. Through electrophoretic shift assays and circular dichroism spectroscopy, we demonstrate that the E isomer is responsible for dimerization and recognition. The formation of the non-covalent tetra component complex occurs in the presence of the GCN4 cognate dsDNA sequence (′5-..ATGA cg TCAT..-3′) but not with (′5-..ATGA c TCAT..-3′) that differs in only one spacing nucleotide. Thus, we demonstrated that the tetra-component complex is formed in a specific manner that depends on the geometry of the ligand, the peptide length, and the ds DNA sequence. We hypothesized that the mechanism of interaction is sequential, and it can be described by the polymorphism model of static fuzziness. We argue that chemically modified peptides of the GCN4 Tf are suitable minimalist experimental models to investigate conformational fuzziness in protein–DNA interactions.

Instructed Assembly as Context-Dependent Signaling for the Death and Morphogenesis of Cells

Context-dependent signaling is a ubiquitous phenomenon in nature, but ways to mimic the essence of these nano- and microscale dynamic molecular processes by noncovalent synthesis in the cellular environment have yet to be developed. Herein we present a dynamic continuum of noncovalent filaments formed by the instructed assembly (iA) of a supramolecular phosphoglycopeptide (sPGP) as context-dependent signals for controlling the death and morphogenesis of cells. Specifically, ectophosphatase enzymes on cancer cells catalyze the formation of sPGP filaments to result in cell death; however, damping of the enzyme activity induces the formation 3D cell spheroids. Similarly, the ratio of stromal and cancer cells in a coculture can be used to modulate the expression of the ectophosphatase, so that the iA process leads to the formation of cell spheroids. The spheroids mimic the tumor microenvironment for drug screening.

Disordered Protein Kinase Regions in Regulation of Kinase Domain Cores

Since publication of the crystal structure of protein kinase (PK)A three decades ago, a structural portrait of the conserved kinase core has been drawn. The next challenge is to elucidate structures of full-length kinases and to address the intrinsically disordered regions (IDRs) that typically flank the core as well as the small linear motifs (SLiMs) that are embedded within the IDRs. It is increasingly apparent that unstructured regions integrate the kinase catalytic chassis into multienzyme-based regulatory networks. The extracellular signal-regulated kinase-ribosomal S6 PK-phosphoinositide-dependent kinase (ERK-RSK-PDK) complex is an excellent example to demonstrate how IDRs and SLiMs govern communication between four different kinase catalytic cores to mediate activation and how in molecular terms these promote the formation of kinase heterodimers in a context dependent fashion.

Ion Mobility Mass Spectrometry Uncovers the Impact of the Patterning of Oppositely Charged Residues on the Conformational Distributions of Intrinsically Disordered Proteins

The global dimensions and amplitudes of conformational fluctuations of intrinsically disordered proteins are governed, in part, by the linear segregation versus clustering of oppositely charged residues within the primary sequence. Ion mobility-mass spectrometry (IM-MS) affords unique advantages for probing the conformational consequences of the linear patterning of oppositely charged residues because it measures and separates proteins electrosprayed from solution on the basis of charge and shape. Here, we use IM-MS to measure the conformational consequences of charge patterning on the C-terminal intrinsically disordered region (p27 IDR) of the cell cycle inhibitory protein p27^Kip1. We report the range of charge states and accompanying collisional cross section distributions for wild-type p27 IDR and two variants with identical amino acid compositions, κ14 and κ56, distinguished by the extent of linear mixing versus segregation of oppositely charged residues. Wild-type p27 IDR (κ31) and κ14, where the oppositely charged residues are more evenly distributed, exhibit a broad distribution of charge states. This is concordant with high degrees of conformational heterogeneity in solution. By contrast, κ56 with linear segregation of oppositely charged residues leads to limited conformational heterogeneity and a narrow distribution of charged states. Gas-phase molecular dynamics simulations demonstrate that the interplay between chain solvation and intrachain interactions (self-solvation) leads to conformational distributions that are modulated by salt concentration, with the wild-type sequence showing the most sensitivity to changes in salt concentration. These results suggest that the charge patterning within the wild-type p27 IDR may be optimized to sample both highly solvated and self-solvated conformational states.

Structural and Dynamical Order of a Disordered Protein: Molecular Insights into Conformational Switching of PAGE4 at the Systems Level

Folded proteins show a high degree of structural order and undergo (fairly constrained) collective motions related to their functions. On the other hand, intrinsically disordered proteins (IDPs), while lacking a well-defined three-dimensional structure, do exhibit some structural and dynamical ordering, but are less constrained in their motions than folded proteins. The larger structural plasticity of IDPs emphasizes the importance of entropically driven motions. Many IDPs undergo function-related disorder-to-order transitions driven by their interaction with specific binding partners. As experimental techniques become more sensitive and become better integrated with computational simulations, we are beginning to see how the modest structural ordering and large amplitude collective motions of IDPs endow them with an ability to mediate multiple interactions with different partners in the cell. To illustrate these points, here, we use Prostate-associated gene 4 (PAGE4), an IDP implicated in prostate cancer (PCa) as an example. We first review our previous efforts using molecular dynamics simulations based on atomistic AWSEM to study the conformational dynamics of PAGE4 and how its motions change in its different physiologically relevant phosphorylated forms. Our simulations quantitatively reproduced experimental observations and revealed how structural and dynamical ordering are encoded in the sequence of PAGE4 and can be modulated by different extents of phosphorylation by the kinases HIPK1 and CLK2. This ordering is reflected in changing populations of certain secondary structural elements as well as in the regularity of its collective motions. These ordered features are directly correlated with the functional interactions of WT-PAGE4, HIPK1-PAGE4 and CLK2-PAGE4 with the AP-1 signaling axis. These interactions give rise to repeated transitions between (high HIPK1-PAGE4, low CLK2-PAGE4) and (low HIPK1-PAGE4, high CLK2-PAGE4) cell phenotypes, which possess differing sensitivities to the standard PCa therapies, such as androgen deprivation therapy (ADT). We argue that, although the structural plasticity of an IDP is important in promoting promiscuous interactions, the modulation of the structural ordering is important for sculpting its interactions so as to rewire with agility biomolecular interaction networks with significant functional consequences.

Is protein context responsible for peptide-mediated interactions?

Many cell signaling pathways are orchestrated by the weak, transient, and reversible peptide-mediated interactions (PMIs). Here, the role of protein context in contributing to the stability and specificity of PMIs is investigated systematically.

Towards a Stochastic Paradigm: From Fuzzy Ensembles to Cellular Functions

The deterministic sequence → structure → function relationship is not applicable to describe how proteins dynamically adapt to different cellular conditions. A stochastic model is required to capture functional promiscuity, redundant sequence motifs, dynamic interactions, or conformational heterogeneity, which facilitate the decision-making in regulatory processes, ranging from enzymes to membraneless cellular compartments. The fuzzy set theory offers a quantitative framework to address these problems. The fuzzy formalism allows the simultaneous involvement of proteins in multiple activities, the degree of which is given by the corresponding memberships. Adaptation is described via a fuzzy inference system, which relates heterogeneous conformational ensembles to different biological activities. Sequence redundancies (e.g., tandem motifs) can also be treated by fuzzy sets to characterize structural transitions affecting the heterogeneous interaction patterns (e.g., pathological fibrillization of stress granules). The proposed framework can provide quantitative protein models, under stochastic cellular conditions.