Intrinsically disordered proteins and biomolecular condensates as drug targets

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

When an underdog becomes a major player: the role of protein structural disorder in the Atg8 conjugation system

The noncanonical ubiquitin-like conjugation cascade involving the E1 (Atg7), E2 (Atg3, Atg10), and E3 (Atg12-Atg5-Atg16 complex) enzymes is essential for incorporation of Atg8 into the growing phagophore via covalent linkage to PE. This process is an indispensable step in autophagy. Atg8 and E1-E3 enzymes are the first subset from the core autophagy protein machinery structures that were investigated in earlier studies by crystallographic analyses of globular domains. However, research over the past decade shows that many important functions in the conjugation machinery are mediated by intrinsically disordered protein regions (IDPRs) - parts of the protein that do not adopt a stable secondary or tertiary structure, which are inherently dynamic and well suited for protein-membrane interactions but are invisible in protein crystals. Here, we summarize earlier and recent findings on the autophagy conjugation machinery by focusing on the IDPRs. This summary reveals that IDPRs, originally considered dispensable, are in fact major players and a driving force in the function of the autophagy conjugation system. Abbreviation: AD, activation domain of Atg7; AH, amphipathic helix; AIM, Atg8-family interacting motif; CL, catalytic loop (of Atg7); CTD, C-terminal domain; FR, flexible region (of Atg3 or Atg10); GUV, giant unilammelar vesicles; HR, handle region (of Atg3); IDPR, intrinsically disordered protein region; IDPs: intrinsically disordered proteins; LIR, LC3-interacting region; NHD: N-terminal helical domain; NMR, nuclear magnetic resonance; PE, phosphatidylethanolamine; UBL, ubiquitin like.

How intrinsically disordered proteins order plant gene silencing

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) possess low sequence complexity of amino acids and display non-globular tertiary structures. They can act as scaffolds, form regulatory hubs, or trigger biomolecular condensation to control diverse aspects of biology. Emerging evidence has recently implicated critical roles of IDPs and IDR-contained proteins in nuclear transcription and cytoplasmic post-transcriptional processes, among other molecular functions. We here summarize the concepts and organizing principles of IDPs. We then illustrate recent progress in understanding the roles of key IDPs in machineries that regulate transcriptional and post-transcriptional gene silencing (PTGS) in plants, aiming at highlighting new modes of action of IDPs in controlling biological processes.

Phase separation-mediated biomolecular condensates and their relationship to tumor

Phase separation is a cellular phenomenon where macromolecules aggregate or segregate, giving rise to biomolecular condensates resembling "droplets" and forming distinct, membrane-free compartments. This process is pervasive in biological cells, contributing to various essential cellular functions. However, when phase separation goes awry, leading to abnormal molecular aggregation, it can become a driving factor in the development of diseases, including tumor. Recent investigations have unveiled the intricate connection between dysregulated phase separation and tumor pathogenesis, highlighting its potential as a novel therapeutic target. This article provides an overview of recent phase separation research, with a particular emphasis on its role in tumor, its therapeutic implications, and outlines avenues for further exploration in this intriguing field.

Design of functional intrinsically disordered proteins

Many proteins do not fold into a fixed three-dimensional structure, but rather function in a highly disordered state. These intrinsically disordered proteins pose a unique challenge to protein engineering and design: How can proteins be designed de novo if not by tailoring their structure? Here, we will review the nascent field of design of intrinsically disordered proteins with focus on applications in biotechnology and medicine. The design goals should not necessarily be the same as for de novo design of folded proteins as disordered proteins have unique functional strengths and limitations. We focus on functions where intrinsically disordered proteins are uniquely suited including disordered linkers, desiccation chaperones, sensors of the chemical environment, delivery of pharmaceuticals, and constituents of biomolecular condensates. Design of functional intrinsically disordered proteins relies on a combination of computational tools and heuristics gleaned from sequence-function studies. There are few cases where intrinsically disordered proteins have made it into industrial applications. However, we argue that disordered proteins can perform many roles currently performed by organic polymers, and that these proteins might be more designable due to their modularity.

A Novel Druggable Dual-Specificity tYrosine-Regulated Kinase3/Calmodulin Kinase-like Vesicle-Associated Signaling Module with Therapeutic Implications in Neuroblastoma

High-risk neuroblastoma is a very aggressive pediatric cancer, accounting for ~15% of childhood cancer mortality. Therefore, novel therapeutic strategies for the treatment of neuroblastoma are urgently sought. Here, we focused on the potential implications of the Dual-specificity tYrosine-Regulated Kinase (DYRK) family and downstream signaling pathways. We used bioinformatic analysis of public datasets from neuroblastoma cohorts and cell lines to search correlations between patient survival and expression of DYRK kinases. Additionally, we performed biochemical, molecular, and cellular approaches to validate and characterize our observations, as well as an in vivo orthotopic murine model of neuroblastoma. We identified the DYRK3 kinase as a critical mediator of neuroblastoma cell proliferation and in vivo tumor growth. DYRK3 has recently emerged as a key regulator of several biomolecular condensates and has been linked to the hypoxic response of neuroblastoma cells. Our data suggest a role for DYRK3 as a regulator of the neuroblastoma-specific protein CAMKV, which is also required for neuroblastoma cell proliferation. CAMKV is a very understudied member of the Ca2+/calmodulin-dependent protein kinase family, originally described as a pseudokinase. We show that CAMKV is phosphorylated by DYRK3, and that inhibition of DYRK3 kinase activity induces CAMKV aggregation, probably mediated by its highly disordered C-terminal half. Importantly, we provide evidence that the DYRK3/CAMKV signaling module could play an important role for the function of the mitotic spindle during cell division. Our data strongly support the idea that inhibition of DYRK3 and/or CAMKV in neuroblastoma cells could constitute an innovative and highly specific intervention to fight against this dreadful cancer.

Rational optimization of a transcription factor activation domain inhibitor

Transcription factors are among the most attractive therapeutic targets but are considered largely 'undruggable' in part due to the intrinsically disordered nature of their activation domains. Here we show that the aromatic character of the activation domain of the androgen receptor, a therapeutic target for castration-resistant prostate cancer, is key for its activity as transcription factor, allowing it to translocate to the nucleus and partition into transcriptional condensates upon activation by androgens. On the basis of our understanding of the interactions stabilizing such condensates and of the structure that the domain adopts upon condensation, we optimized the structure of a small-molecule inhibitor previously identified by phenotypic screening. The optimized compounds had more affinity for their target, inhibited androgen-receptor-dependent transcriptional programs, and had an antitumorigenic effect in models of castration-resistant prostate cancer in cells and in vivo. These results suggest that it is possible to rationally optimize, and potentially even to design, small molecules that target the activation domains of oncogenic transcription factors.

Molecular mechanisms of phase separation and amyloidosis of ALS/FTD-linked FUS and TDP-43

FUS and TDP-43, two RNA-binding proteins from the heterogeneous nuclear ribonucleoprotein family, have gained significant attention in the field of neurodegenerative diseases due to their association with amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). They possess folded domains for binding ATP and various nucleic acids including DNA and RNA, as well as substantial intrinsically disordered regions (IDRs) including prion-like domains (PLDs) and RG-/RGG-rich regions. They play vital roles in various cellular processes, including transcription, splicing, microRNA maturation, RNA stability and transport and DNA repair. In particular, they are key components for forming ribonucleoprotein granules and stress granules (SGs) through homotypic or heterotypic liquid-liquid phase separation (LLPS). Strikingly, liquid-like droplets formed by FUS and TDP-43 may undergo aging to transform into less dynamic assemblies such as hydrogels, inclusions, and amyloid fibrils, which are the pathological hallmarks of ALS and FTD. This review aims to synthesize and consolidate the biophysical knowledge of the sequences, structures, stability, dynamics, and inter-domain interactions of FUS and TDP-43 domains, so as to shed light on the molecular mechanisms underlying their liquid-liquid phase separation (LLPS) and amyloidosis. The review further delves into the mechanisms through which ALS-causing mutants of the well-folded hPFN1 disrupt the dynamics of LLPS of FUS prion-like domain, providing key insights into a potential mechanism for misfolding/aggregation-prone proteins to cause neurodegenerative diseases and aging by gain of functions. With better understanding of different biophysical aspects of FUS and TDP-43, the ultimate goal is to develop drugs targeting LLPS and amyloidosis, which could mediate protein homeostasis within cells and lead to new treatments for currently intractable diseases, particularly neurodegenerative diseases such as ALS, FTD and aging. However, the study of membrane-less organelles and condensates is still in its infancy and therefore the review also highlights key questions that require future investigation.

Using the structural diversity of RNA: protein interfaces to selectively target RNA with small molecules in cells: methods and perspectives

In recent years, RNA has gained traction both as a therapeutic molecule and as a therapeutic target in several human pathologies. In this review, we consider the approach of targeting RNA using small molecules for both research and therapeutic purposes. Given the primary challenge presented by the low structural diversity of RNA, we discuss the potential for targeting RNA: protein interactions to enhance the structural and sequence specificity of drug candidates. We review available tools and inherent challenges in this approach, ranging from adapted bioinformatics tools to in vitro and cellular high-throughput screening and functional analysis. We further consider two critical steps in targeting RNA/protein interactions: first, the integration of in silico and structural analyses to improve the efficacy of molecules by identifying scaffolds with high affinity, and second, increasing the likelihood of identifying on-target compounds in cells through a combination of high-throughput approaches and functional assays. We anticipate that the development of a new class of molecules targeting RNA: protein interactions to prevent physio-pathological mechanisms could significantly expand the arsenal of effective therapeutic compounds.

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein

The Atg12 protein in yeast is an indispensable polypeptide in the highly conserved ubiquitin-like conjugation system operating in the macroautophagy/autophagy pathway. Atg12 is covalently conjugated to Atg5 through the action of Atg7 and Atg10; the Atg12-Atg5 conjugate binds Atg16 to form an E3 ligase that functions in a separate conjugation pathway involving Atg8. Atg12 is comprised of a ubiquitin-like (UBL) domain preceded at the N terminus by an intrinsically disordered protein region (IDPR), a domain that comprises a major portion of the protein but remains elusive in its conformation and function. Here, we show that the IDPR in unconjugated Atg12 is positioned in proximity to the UBL domain, a configuration that is important for the functional structure of the protein. A major deletion in the IDPR disrupts intactness of the UBL domain at the unconjugated C terminus, and a mutation in the predicted α0 helix in the IDPR prevents Atg12 from binding to Atg7 and Atg10, which ultimately affects the protein function in the ubiquitin-like conjugation cascade. These findings provide evidence that the IDPR is an indispensable part of the Atg12 protein from yeast.

Phase separation and pathologic transitions of RNP condensates in neurons: implications for amyotrophic lateral sclerosis, frontotemporal dementia and other neurodegenerative disorders

Liquid-liquid phase separation results in the formation of dynamic biomolecular condensates, also known as membrane-less organelles, that allow for the assembly of functional compartments and higher order structures within cells. Multivalent, reversible interactions between RNA-binding proteins (RBPs), including FUS, TDP-43, and hnRNPA1, and/or RNA (e.g., RBP-RBP, RBP-RNA, RNA-RNA), result in the formation of ribonucleoprotein (RNP) condensates, which are critical for RNA processing, mRNA transport, stability, stress granule assembly, and translation. Stress granules, neuronal transport granules, and processing bodies are examples of cytoplasmic RNP condensates, while the nucleolus and Cajal bodies are representative nuclear RNP condensates. In neurons, RNP condensates promote long-range mRNA transport and local translation in the dendrites and axon, and are essential for spatiotemporal regulation of gene expression, axonal integrity and synaptic function. Mutations of RBPs and/or pathologic mislocalization and aggregation of RBPs are hallmarks of several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease. ALS/FTD-linked mutations of RBPs alter the strength and reversibility of multivalent interactions with other RBPs and RNAs, resulting in aberrant phase transitions. These aberrant RNP condensates have detrimental functional consequences on mRNA stability, localization, and translation, and ultimately lead to compromised axonal integrity and synaptic function in disease. Pathogenic protein aggregation is dependent on various factors, and aberrant dynamically arrested RNP condensates may serve as an initial nucleation step for pathologic aggregate formation. Recent studies have focused on identifying mechanisms by which neurons resolve phase transitioned condensates to prevent the formation of pathogenic inclusions/aggregates. The present review focuses on the phase separation of neurodegenerative disease-linked RBPs, physiological functions of RNP condensates, and the pathologic role of aberrant phase transitions in neurodegenerative disease, particularly ALS/FTD. We also examine cellular mechanisms that contribute to the resolution of aberrant condensates in neurons, and potential therapeutic approaches to resolve aberrantly phase transitioned condensates at a molecular level.

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering

The intrinsically disordered 4E-BP2 protein regulates mRNA cap-dependent translation through interaction with the predominantly folded eukaryotic initiation factor 4E (eIF4E). Phosphorylation of 4E-BP2 dramatically reduces the level of eIF4E binding, in part by stabilizing a binding-incompatible folded domain. Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). We then applied an integrative Bayesian approach to obtain NP and 5P conformational ensembles that agree with experimental data from nuclear magnetic resonance, small-angle X-ray scattering, and single-molecule Förster resonance energy transfer (smFRET). For the NP state, inter-residue distance scaling and 2D maps revealed the role of charge segregation and pi interactions in driving contacts between distal regions of the chain (∼70 residues apart). The 5P ensemble shows prominent contacts of the N-IDR region with the two phosphosites in the folded domain, pT37 and pT46, and, to a lesser extent, delocalized interactions with the C-IDR region. Agglomerative hierarchical clustering led to partitioning of each of the two ensembles into four clusters with different global dimensions and contact maps. This helped delineate an NP cluster that, based on our smFRET data, is compatible with the eIF4E-bound state. 5P clusters were differentiated by interactions of C-IDR with the folded domain and of the N-IDR with the two phosphosites in the folded domain. Our study provides both a better visualization of fundamental structural poses of 4E-BP2 and a set of falsifiable insights on intrachain interactions that bias folding and binding of this protein.

Ultrasmall Gold Nanoparticles as Clients of Biomolecular Condensates

Liquid-liquid phase separation (LLPS) of biopolymers to form condensates is a widespread phenomenon in living cells. Agents that target or alter condensation can help uncover elusive physiological and pathological mechanisms. Owing to their unique material properties and modes of interaction with biomolecules, nanoparticles represent attractive condensate-targeting agents. Our work focused on elucidating the interaction between ultrasmall gold nanoparticles (usGNPs) and diverse types of condensates of tau, a representative phase-separating protein associated with neurodegenerative disorders. usGNPs attract considerable interest in the biomedical community due to unique features, including emergent optical properties and good cell penetration. We explored the interaction of usGNPs with reconstituted self-condensates of tau, two-component tau/polyanion and three-component tau/RNA/alpha-synuclein coacervates. The usGNPs were found to concentrate into condensed liquid droplets, consistent with the formation of dynamic client (nanoparticle) - scaffold (tau) interactions, and were observable thanks to their intrinsic luminescence. Furthermore, usGNPs were capable to promote LLPS of a protein domain which is unable to phase separate on its own. Our study demonstrates the ability of usGNPs to interact with and illuminate protein condensates. We anticipate that nanoparticles will have broad applicability as nanotracers to interrogate phase separation, and as nanoactuators controlling the formation and dissolution of condensates.

Challenges in studying the liquid-to-solid phase transitions of proteins using computer simulations

"Membraneless organelles," also referred to as biomolecular condensates, perform a variety of cellular functions and their dysregulation is implicated in cancer and neurodegeneration. In the last two decades, liquid-liquid phase separation (LLPS) of intrinsically disordered and multidomain proteins has emerged as a plausible mechanism underlying the formation of various biomolecular condensates. Further, the occurrence of liquid-to-solid transitions within liquid-like condensates may give rise to amyloid structures, implying a biophysical link between phase separation and protein aggregation. Despite significant advances, uncovering the microscopic details of liquid-to-solid phase transitions using experiments remains a considerable challenge and presents an exciting opportunity for the development of computational models which provide valuable, complementary insights into the underlying phenomenon. In this review, we first highlight recent biophysical studies which provide new insights into the molecular mechanisms underlying liquid-to-solid (fibril) phase transitions of folded, disordered and multi-domain proteins. Next, we summarize the range of computational models used to study protein aggregation and phase separation. Finally, we discuss recent computational approaches which attempt to capture the underlying physics of liquid-to-solid transitions along with their merits and shortcomings.

Thermodynamic and kinetic approaches for drug discovery to target protein misfolding and aggregation

INTRODUCTION

Protein misfolding diseases, including Alzheimer's and Parkinson's diseases, are characterized by the aberrant aggregation of proteins. These conditions are still largely untreatable, despite having a major impact on our healthcare systems and societies.

AREAS COVERED

We describe drug discovery strategies to target protein misfolding and aggregation. We compare thermodynamic approaches, which are based on the stabilization of the native states of proteins, with kinetic approaches, which are based on the slowing down of the aggregation process. This comparison is carried out in terms of the current knowledge of the process of protein misfolding and aggregation, the mechanisms of disease and the therapeutic targets.

EXPERT OPINION

There is an unmet need for disease-modifying treatments that target protein misfolding and aggregation for the over 50 human disorders known to be associated with this phenomenon. With the approval of the first drugs that can prevent misfolding or inhibit aggregation, future efforts will be focused on the discovery of effective compounds with these mechanisms of action for a wide range of conditions.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.

Screen for Modulation of Nucleocapsid Protein Condensation Identifies Small Molecules with Anti-Coronavirus Activity

Biomolecular condensates formed by liquid-liquid phase separation have been implicated in multiple diseases. Modulation of condensate dynamics by small molecules has therapeutic potential, but so far, few condensate modulators have been disclosed. The SARS-CoV-2 nucleocapsid (N) protein forms phase-separated condensates that are hypothesized to play critical roles in viral replication, transcription, and packaging, suggesting that N condensation modulators might have anti-coronavirus activity across multiple strains and species. Here, we show that N proteins from all seven human coronaviruses (HCoVs) vary in their tendency to undergo phase separation when expressed in human lung epithelial cells. We developed a cell-based high-content screening platform and identified small molecules that both promote and inhibit condensation of SARS-CoV-2 N. Interestingly, these host-targeted small molecules exhibited condensate-modulatory effects across all HCoV Ns. Some have also been reported to exhibit antiviral activity against SARS-CoV-2, HCoV-OC43, and HCoV-229E viral infections in cell culture. Our work reveals that the assembly dynamics of N condensates can be regulated by small molecules with therapeutic potential. Our approach allows for screening based on viral genome sequences alone and might enable rapid paths to drug discovery with value for confronting future pandemics.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

Macromolecular Crowding Is Surprisingly Unable to Deform the Structure of a Model Biomolecular Condensate

The crowded interior of a living cell makes performing experiments on simpler in vitro systems attractive. Although these reveal interesting phenomena, their biological relevance can be questionable. A topical example is the phase separation of intrinsically disordered proteins into biomolecular condensates, which is proposed to underlie the membrane-less compartmentalization of many cellular functions. How a cell reliably controls biochemical reactions in compartments open to the compositionally-varying cytoplasm is an important question for understanding cellular homeostasis. Computer simulations are often used to study the phase behavior of model biomolecular condensates, but the number of relevant parameters increases as the number of protein components increases. It is unfeasible to exhaustively simulate such models for all parameter combinations, although interesting phenomena are almost certainly hidden in their high-dimensional parameter space. Here, we have studied the phase behavior of a model biomolecular condensate in the presence of a polymeric crowding agent. We used a novel compute framework to execute dozens of simultaneous simulations spanning the protein/crowder concentration space. We then combined the results into a graphical representation for human interpretation, which provided an efficient way to search the model's high-dimensional parameter space. We found that steric repulsion from the crowder drives a near-critical system across the phase boundary, but the molecular arrangement within the resulting biomolecular condensate is rather insensitive to the crowder concentration and molecular weight. We propose that a cell may use the local cytoplasmic concentration to assist the formation of biomolecular condensates, while relying on the dense phase to reliably provide a stable, structured, fluid milieu for cellular biochemistry despite being open to its changing environment.

Characterizing Properties of Biomolecular Condensates Below the Diffraction Limit In Vivo

Fluorescence microscopy assays enable the investigation of endogenous biomolecular condensates directly in their cellular context. With appropriate experimental designs, these assays yield quantitative information on condensate material properties and inform on biophysical mechanisms of condensate formation. Single-molecule super-resolution and tracking experiments grant access to the smallest condensates and early condensation stages not resolved by conventional imaging approaches. Here, we discuss considerations for using single-molecule assays to extract quantitative information about biomolecular condensates directly in their cellular context.

Portability of a Small-Molecule Binding Site between Disordered Proteins

Intrinsically disordered proteins (IDPs) are important in both normal and disease states. Small molecules can be targeted to disordered regions, but we currently have only a limited understanding of the nature of small-molecule binding sites in IDPs. Here, we show that a minimal small-molecule binding sequence of eight contiguous residues derived from the Myc protein can be ported into a different disordered protein and recapitulate small-molecule binding activity in the new context. We also find that the residue immediately flanking the binding site can have opposing effects on small-molecule binding in the different disordered protein contexts. The results demonstrate that small-molecule binding sites can act modularly and are portable between disordered protein contexts but that residues outside of the minimal binding site can modulate binding affinity.

Controlling amyloid formation of intrinsically disordered proteins and peptides: slowing down or speeding up?

The pathological assembly of intrinsically disordered proteins/peptides (IDPs) into amyloid fibrils is associated with a range of human pathologies, including neurodegeneration, metabolic diseases and systemic amyloidosis. These debilitating disorders affect hundreds of millions of people worldwide, and the number of people affected is increasing sharply. However, the discovery of therapeutic agents has been immensely challenging largely because of (i) the diverse number of aggregation pathways and the multi-conformational and transient nature of the related proteins or peptides and (ii) the under-development of experimental pipelines for the identification of disease-modifying molecules and their mode-of-action. Here, we describe current approaches used in the search for small-molecule modulators able to control or arrest amyloid formation commencing from IDPs and review recently reported accelerators and inhibitors of amyloid formation for this class of proteins. We compare their targets, mode-of-action and effects on amyloid-associated cytotoxicity. Recent successes in the control of IDP-associated amyloid formation using small molecules highlight exciting possibilities for future intervention in protein-misfolding diseases, despite the challenges of targeting these highly dynamic precursors of amyloid assembly.

Biological soft matter: intrinsically disordered proteins in liquid-liquid phase separation and biomolecular condensates

The facts that many proteins with crucial biological functions do not have unique structures and that many biological processes are compartmentalized into the liquid-like biomolecular condensates, which are formed via liquid-liquid phase separation (LLPS) and are not surrounded by the membrane, are revolutionizing the modern biology. These phenomena are interlinked, as the presence of intrinsic disorder represents an important requirement for a protein to undergo LLPS that drives biogenesis of numerous membrane-less organelles (MLOs). Therefore, one can consider these phenomena as crucial constituents of a new IDP-LLPS-MLO field. Furthermore, intrinsically disordered proteins (IDPs), LLPS, and MLOs represent a clear link between molecular and cellular biology and soft matter and condensed soft matter physics. Both IDP and LLPS/MLO fields are undergoing explosive development and generate the ever-increasing mountain of crucial data. These new data provide answers to so many long-standing questions that it is difficult to imagine that in the very recent past, protein scientists and cellular biologists operated without taking these revolutionary concepts into account. The goal of this essay is not to deliver a comprehensive review of the IDP-LLPS-MLO field but to provide a brief and rather subjective outline of some of the recent developments in these exciting fields.

Principles and functions of condensate modifying drugs

Biomolecular condensates are compartmentalized communities of biomolecules, which unlike traditional organelles, are not enclosed by membranes. Condensates play roles in diverse cellular processes, are dysfunctional in many disease states, and are often enriched in classically "undruggable" targets. In this review, we provide an overview for how drugs can modulate condensate structure and function by phenotypically classifying them as dissolvers (dissolve condensates), inducers (induce condensates), localizers (alter localization of the specific condensate community members) or morphers (alter the physiochemical properties). We discuss the growing list of bioactive molecules that function as condensate modifiers (c-mods), including small molecules, oligonucleotides, and peptides. We propose that understanding mechanisms of condensate perturbation of known c-mods will accelerate the discovery of a new class of therapies for difficult-to-treat diseases.

Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states

Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design.

The evolution and polymorphism of mono-amino acid repeats in androgen receptor and their regulatory role in health and disease

Androgen receptor (AR) is a key member of nuclear hormone receptors with the longest intrinsically disordered N-terminal domain (NTD) in its protein family. There are four mono-amino acid repeats (polyQ1, polyQ2, polyG, and polyP) located within its NTD, of which two are polymorphic (polyQ1 and polyG). The length of both polymorphic repeats shows clinically important correlations with disease, especially with cancer and neurodegenerative diseases, as shorter and longer alleles exhibit significant differences in expression, activity and solubility. Importantly, AR has also been shown to undergo condensation in the nucleus by liquid-liquid phase separation, a process highly sensitive to protein solubility and concentration. Nonetheless, in prostate cancer cells, AR variants also partition into transcriptional condensates, which have been shown to alter the expression of target gene products. In this review, we summarize current knowledge on the link between AR repeat polymorphisms and cancer types, including mechanistic explanations and models comprising the relationship between condensate formation, polyQ1 length and transcriptional activity. Moreover, we outline the evolutionary paths of these recently evolved amino acid repeats across mammalian species, and discuss new research directions with potential breakthroughs and controversies in the literature.

Model biomolecular condensates have heterogeneous structure quantitatively dependent on the interaction profile of their constituent macromolecules

Biomolecular condensates play numerous roles in cells by selectively concentrating client proteins while excluding others. These functions are likely to be sensitive to the spatial organization of the scaffold proteins forming the condensate. We use coarse-grained molecular simulations to show that model intrinsically-disordered proteins phase separate into a heterogeneous, structured fluid characterized by a well-defined length scale. The proteins are modelled as semi-flexible polymers with punctate, multifunctional binding sites in good solvent conditions. Their dense phase is highly solvated with a spatial structure that is more sensitive to the separation of the binding sites than their affinity. We introduce graph theoretic measures to quantify their heterogeneity, and find that it increases with increasing binding site number, and exhibits multi-timescale dynamics. The model proteins also swell on passing from the dilute solution to the dense phase. The simulations predict that the structure of the dense phase is modulated by the location and affinity of binding sites distant from the termini of the proteins, while sites near the termini more strongly affect its phase behaviour. The relations uncovered between the arrangement of weak interaction sites on disordered proteins and the material properties of their dense phase can be experimentally tested to give insight into the biophysical properties, pathological effects, and rational design of biomolecular condensates.

Evolving Experimental Techniques for Structure-Based Drug Design

Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Biomolecular condensates: new opportunities for drug discovery and RNA therapeutics

Biomolecular condensates organize cellular functions in the absence of membranes. These membraneless organelles can form through liquid-liquid phase separation coalescing RNA and proteins into well-defined, yet dynamic, structures distinct from the surrounding cellular milieu. Numerous physiological and disease-causing processes link to biomolecular condensates, which could impact drug discovery in several ways. First, disruption of pathological condensates seeded by mutated proteins or RNAs may provide new opportunities to treat disease. Second, condensates may be leveraged to tackle difficult-to-drug targets lacking binding pockets whose function depends on phase separation. Third, condensate-resident small molecules and RNA therapeutics may display unexpected pharmacology. We discuss the potential impact of phase separation on drug discovery and RNA therapeutics, leveraging concrete examples, towards novel clinical opportunities.

PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure

Motivation

AlphaFold has been a major advance in predicting protein structure, but still leaves the problem of determining which sub-molecular components of a protein are essential for it to carry out its function within the cell. Direct coupling analysis predicts two- and three-amino acid contacts, but there may be essential interdependencies that are not proximal within the 3D structure. The problem to be addressed is to design a computational method that locates and ranks essential non-proximal interdependencies within a protein involving five or more amino acids, using large, multiple sequence alignments (MSAs) for both globular and intrinsically unstructured proteins.

Results

We developed PSICalc (Protein Subdomain Interdependency Calculator), a laptop-friendly, pattern-discovery, bioinformatics software tool that analyzes large MSAs for both structured and unstructured proteins, locates both proximal and non-proximal inter-dependent sites, and clusters them into pairwise (second order), third-order and higher-order clusters using a k-modes approach, and provides ranked results within minutes. To aid in visualizing these interdependencies, we developed a graphical user interface that displays these subdomain relationships as a polytree graph. To demonstrate, we provide examples of both proximal and non-proximal interdependencies documented for eukaryotic topoisomerase II including between the unstructured C-terminal domain and the N-terminal domain.

Availability and implementation

https://github.com/jdeweeselab/psicalc-package.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

More is simpler: Decomposition of ligand-binding affinity for proteins being disordered

In statistical mechanics, it is well known that the huge number of degrees of freedom does not complicate the problem as it seems, but actually greatly simplifies the analysis (e.g., to give a Boltzmann distribution). Here, we reveal that the ensemble averaging from the vast conformations of intrinsically disordered proteins (IDPs) greatly simplifies the nature of binding affinity, which can be reliably decomposed into a sum of the ligandability of IDP and the capacity of ligand. Such an unexpected regularity is applied to facilitate the virtual screening upon IDPs. It also provides essential insight in understanding the specificity difference between IDPs and conventional ordered proteins since the specificity is caused by deviation from the baseline behavior of protein-ligand binding.

Intrinsically Disordered Proteins: Critical Components of the Wetware

Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.

Phase separation of the mammalian prion protein: physiological and pathological perspectives

Abnormal phase transitions have been implicated in the occurrence of proteinopathies. Disordered proteins with nucleic acid binding ability drive the formation of reversible micron-sized condensates capable of controlling nucleic acid processing/transport. This mechanism, achieved via liquid-liquid phase separation (LLPS), underlies the formation of long-studied membraneless organelles (e.g., nucleolus) and various transient condensates formed by driver proteins. The prion protein (PrP) is not a classical nucleic acid-binding protein. However, it binds nucleic acids with high affinity, undergoes nucleocytoplasmic shuttling, contains a long intrinsically disordered region rich in glycines and evenly spaced aromatic residues, among other biochemical/biophysical properties of bona fide drivers of phase transitions. Because of this, our group and others have characterized LLPS of recombinant PrP. In vitro phase separation of PrP is modulated by nucleic acid aptamers, and, depending on the aptamer conformation, the liquid droplets evolve to solid-like species. Herein we discuss recent studies and previous evidence supporting PrP phase transitions. We focus on the central role of LLPS related to PrP physiology and pathology, with a special emphasis on the interaction of PrP with different ligands, such as proteins and nucleic acids, which can play a role in prion disease pathogenesis. Finally, we comment on therapeutic strategies directed at the nonfunctional phase separation that could potentially tackle prion diseases or other protein misfolding disorders.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Structural Protein Analysis of Driver Gene Mutations in Conjunctival Melanoma

In recent years, there has been tremendous enthusiasm with respect to detailing the genetic basis of many neoplasms, including conjunctival melanoma (CM). We aim to analyze five proteins associated with CM, namely BRAF, NRAS, c-KIT, NF1, and PTEN. We evaluated each protein for its intrinsically disordered protein regions (IDPRs) and its protein-protein interactions (PPI) with the Predictor of Natural Disordered Protein Regions (PONDR^®) and the Search Tool for the Retrieval of Interacting Genes (STRING^®). Our PONDR^® analysis found high levels of IDPRs in all five proteins with mutations linked to CM. The highest levels of IDPRs were in BRAF (45.95%), followed by PTEN (31.76%), NF1 (22.19%), c-KIT (21.82%), and NRAS (14.81%). Our STRING analysis found that each of these five proteins had more predicted interactions then expected (p-value < 1.0 × 10⁻¹⁶). Our analysis demonstrates that the mutations linked to CM likely affected IDPRs and possibly altered their highly complex PPIs. Quantifying IDPRs in BRAF, NRAS, c-KIT, NF1, and PTEN and understanding these protein regions are important processes as IDPRs can be possible drug targets for novel targeted therapies for treating CM.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Small-angle X-ray scattering for the proteomics community: current overview and future potential

Introduction: Proteins are biological nanoparticles. For structural proteomics and hybrid structural biology, complementary methods are required that allow both high throughput and accurate automated data analysis. Small-angle X-ray scattering (SAXS) is a method for observing the size and shape of particles, such as proteins and complexes, in solution. SAXS data can be used to model both the structure, oligomeric state, conformational changes, and flexibility of biomolecular samples.Areas covered: The key principles of SAXS, its sample requirements, and its current and future applications for structural proteomics are briefly reviewed. Recent technical developments in SAXS experiments are discussed, and future potential of the method in structural proteomics is evaluated.Expert opinion: SAXS is a method suitable for several aspects of integrative structural proteomics, with current technical developments allowing for higher throughput and time-resolved studies, as well as the analysis of complex samples, such as membrane proteins. Increasing automation and streamlined data analysis are expected to equip SAXS for structure-based screening workflows. Originally, structural genomics had a heavy focus on folded, crystallizable proteins and complexes - SAXS is a method allowing an expansion of this focus to flexible and disordered systems.

Low amounts of heavy water increase the phase separation propensity of a fragment of the androgen receptor activation domain

The phase equilibria of intrinsically disordered proteins are exquisitely sensitive to changes in solution conditions and this can be used to investigate the driving forces of phase separation in vitro as well as the biological roles of phase transitions in live cells. Here we investigate how using D₂ O as co-solvent in an aqueous buffer changes the phase equilibrium of a fragment of the activation domain of the androgen receptor, a transcription factor that plays a role in the development of the male phenotype and is a therapeutic target for castration resistant prostate cancer. We show how replacing even small fractions of H₂ O with D₂ O increases the propensity of this fragment to undergo liquid-liquid phase separation, likely reflecting a stabilization of the hydrophobic interactions that drive condensation. Our results indicate that it is necessary to take this effect into consideration when studying phase separation phenomena with biophysical methods that require using D₂ O as a co-solvent. In addition, they suggest that additions of D₂ O may be used to enhance phase separation phenomena in cells, facilitating their observation.