Intrinsically disordered proteins in human diseases: introducing the D2 concept

Targeting protein disorder: the next hurdle in drug discovery

Intrinsically disordered proteins have key signalling and regulatory roles in cells and are frequently dysregulated in diseases such as cancer, neurodegeneration, inflammation and autoimmune disorders. Preventing the pathological functions mediated by structural disorder is crucial to successfully target proteins that drive transcription, biomolecular condensation and protein aggregation. However, owing to their heterogeneous, highly dynamic structural states, with ensembles of rapidly interconverting conformations, disordered proteins have been considered largely 'undruggable' by traditional approaches. Here, we review key developments of the field and suggest that the synergy of advanced experimental and computational approaches needs to be pursued to conquer this barrier in drug discovery.

The C-terminal end of PLIN1 displays structural disorder

Lipid droplets (LDs) serve as crucial organelles for lipid storage and metabolism, with their proteome significantly influencing their regulation. Perilipins (PLINs), in particular PLIN1, play vital role in LD metabolism by orchestrating lipolysis. The C-terminal end of PLIN1 regulates lipolysis through interactions with coactivators such as the CGI-58 protein. Despite its importance, the structural characterization of this domain remains limited. Here, we present a comprehensive bioinformatic and biophysical analysis of the C-terminal end of mouse PLIN1 (mPLIN1C). Our findings suggest that mPLIN1C behaves as an intrinsically disordered region (IDR), exhibiting context-dependent properties of the coil-like or pre-molten globule type. Structural analysis reveals a predominance of disordered secondary structure, with circular dichroism spectroscopy indicating a high coil content. Interaction studies with SDS micelles suggest a conformational transition towards a pre-molten globule state. Furthermore, the analysis of molecular recognition features (MoRFs) identifies the EPESE sequence spanning residues 413-417 as a potential binding site for partner molecules. Overall, our findings shed light on the structural properties and potential interaction mechanisms of mPLIN1C, providing insight into its functional role in LD metabolism.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Formation of Polyphasic RNP Granules by Intrinsically Disordered Qβ Coat Proteins and Hairpin-Containing RNA

RNA-protein (RNP) granules are fundamental components in cells, where they perform multiple crucial functions. Many RNP granules form via phase separation driven by protein-protein, protein-RNA, and RNA-RNA interactions. Notably, associated proteins frequently contain intrinsically disordered regions (IDRs) that can associate with multiple partners. Previously, we showed that synthetic RNA molecules containing multiple hairpin coat-protein binding sites can phase-separate, forming granules capable of selectively incorporating proteins inside. Here, we expand this platform by introducing a phage coat protein with a known IDR that facilitates protein-protein interactions. We show that the coat protein phase-separates on its own in vivo and that introduction of hairpin-containing RNA molecules can lead to dissolvement of the protein granules. We further demonstrate via multiple assays that RNA valency, determined by the number of hairpins present on the RNA, leads to distinctly different phase behaviors, effectively forming a polyphasic, programmable RNP granule. Moreover, by incorporating the gene for a blue fluorescent protein into the RNA, we demonstrate a phase-dependent boost of protein titer. These insights not only shed light on the behavior of natural granules but also hold profound implications for the biotechnology field, offering a blueprint for engineering cellular compartments with tailored functionalities.

Unveiling the Conformational Dynamics of the Histone Tails Using Markov State Modeling

Biomolecules predominantly exert their function by altering conformational dynamics. The nucleosome core particle (NCP) is the fundamental unit of chromatin. DNA with ∼146 base pairs wraps around the histone octamer to form a nucleosome. The histone octamer is composed of two copies of each histone protein (H3, H4, H2A, and H2B) with a globular core and disordered N-terminal tails. Epigenetic modifications of the histone N-terminal tails play a critical role in regulating the chromatin structure and biological processes such as transcription and DNA repair. Here, we report all-atom molecular dynamics (MD) simulations of the nucleosome at microsecond time scales to construct Markov state models (MSMs) to elucidate distinct conformations of the histone tails. We employ time-lagged independent component analysis (tICA) to capture their essential slow dynamics, with k-means clustering used to discretize the conformational space. MSMs unveil distinct states and transition probabilities to characterize the dynamics and kinetics of the tails. Next, we focus on the H2B tail, which is one of the least studied tails. We show that acetylation increases secondary structure formation with increased transition rates. These findings will aid in understanding the functional implications of tail conformations for nucleosome stability and gene regulation.

Intrinsic Disorder and Phase Separation Coordinate Exocytosis, Motility, and Chromatin Remodeling in the Human Acrosomal Proteome

Intrinsic disorder refers to protein regions that lack a fixed three-dimensional structure under physiological conditions, enabling conformational plasticity. This flexibility allows for diverse functions, including transient interactions, signaling, and phase separation via disorder-to-order transitions upon binding. Our study focused on investigating the role of intrinsic disorder and liquid-liquid phase separation (LLPS) in the human acrosome, a sperm-specific organelle essential for fertilization. Using computational prediction models, network analysis, Structural Classification of Proteins (SCOP) functional assessments, and Gene Ontology, we analyzed 250 proteins within the acrosomal proteome. Our bioinformatic analysis yielded 97 proteins with high levels (>30%) of structural disorder. Further analysis of functional enrichment identified associations between disordered regions overlapping with SCOP domains and critical acrosomal processes, including vesicle trafficking, membrane fusion, and enzymatic activation. Examples of disordered SCOP domains include the PLC-like phosphodiesterase domain, the t-SNARE domain, and the P-domain of calnexin/calreticulin. Protein-protein interaction networks revealed acrosomal proteins as hubs in tightly interconnected systems, emphasizing their functional importance. LLPS propensity modeling determined that over 30% of these proteins are high-probability LLPS drivers (>60%), underscoring their role in dynamic compartmentalization. Proteins such as myristoylated alanine-rich C-kinase substrate and nuclear transition protein 2 exhibited both high LLPS propensities and high levels of structural disorder. A significant relationship (p < 0.0001, R² = 0.649) was observed between the level of intrinsic disorder and LLPS propensity, showing the role of disorder in facilitating phase separation. Overall, these findings provide insights into how intrinsic disorder and LLPS contribute to the structural adaptability and functional precision required for fertilization, with implications for understanding disorders associated with the human acrosome reaction.

Intrinsic disorder in protein interaction networks linking cancer with metabolic diseases

Complex diseases are usually driven by numerous proteins that operate as intricate network systems. Deciphering of their signals is required for more advanced level understanding of the cellular processes driven by protein interactions. Therefore, placing diseases into a framework, where they can be viewed together, represents an important and fruitful approach. The goal of this study was to assess the intrinsic disorder present in the proteins forming PPI networks linking cancer with different human diseases. To this end, we used a set of bioinformatics tools to explore intrinsic disorder and liquid-liquid phase separation predispositions of 340 proteins reported earlier by Hirsch et al. (Cancer Cell (2010) 17(4), 348-361; doi: 10.1016/j.ccr.2010.01.022) as differently expressed in common chronic diseases, such as cancer, obesity, diabetes, and cardiovascular diseases. We further examined selected proteins from this set for their interactability and intrinsic disorder-based functionality. Our analyses demonstrated that intrinsically disordered proteins and proteins with intrinsically disordered regions may act as active network promoters and operate as highly connected hubs, which further enables them to play key roles in the disease pathways. The study also indicated that differently expressed proteins involved in disease progression could be characterized by diverse degrees of intrinsic disorder and LLPS propensity. We hope that our findings in combination with the outputs of the proteomic and functional genomic analyses contain essential clues that can be used in further medical research leading to the design of better therapies.

Molecular mechanism of the effect of ZnCl2 and MgCl2 solution on the conformation of the tau267-312 monomer

Alzheimer's disease is generally believed to be caused by abnormal aggregation of tau protein; however, there remains a lack of understanding about the aggregation process of tau protein in a solution environment. To explore the conformational properties of the tau protein monomer (tau267-312) in the presence of zinc and magnesium ions, we performed all-atom molecular dynamics simulations of tau267-312 in solutions of zinc chloride and magnesium chloride at different concentrations and compared these results with those obtained in pure water. The calculation results show that the β-sheet content increases significantly in the presence of zinc and magnesium ions, which causes a more compact structure for the tau protein monomers. Furthermore, it was found that stronger interactions between residues, as well as alterations in hydrophilic and hydrophobic interactions, are molecular mechanisms driving structural changes within the tau protein monomers. These findings suggest that zinc and magnesium ions facilitate a more stable conformation and promote the aggregation of tau protein monomers, which is important for understanding the aggregation and folding process of tau protein in the environment of saline solution.

Hypothermia Shifts Neurodegeneration Phenotype in Neonatal Human Hypoxic-Ischemic Encephalopathy but Not in Related Piglet Models: Possible Relationship to Toxic Conformer and Intrinsically Disordered Prion-like Protein Accumulation

Hypothermia (HT) is used clinically for neonatal hypoxic-ischemic encephalopathy (HIE); however, the brain protection is incomplete and selective regional vulnerability and lifelong consequences remain. Refractory damage and impairment with HT cooling/rewarming could result from unchecked or altered persisting cell death and proteinopathy. We tested two hypotheses: (1) HT modifies neurodegeneration type, and (2) intrinsically disordered proteins (IDPs) and encephalopathy cause toxic conformer protein (TCP) proteinopathy neonatally. We studied postmortem human neonatal HIE cases with or without therapeutic HT, neonatal piglets subjected to global hypoxia-ischemia (HI) with and without HT or combinations of HI and quinolinic acid (QA) excitotoxicity surviving for 29-96 h to 14 days, and human oligodendrocytes and neurons exposed to QA for cell models. In human and piglet encephalopathies with normothermia, the neuropathology by hematoxylin and eosin staining was similar; necrotic cell degeneration predominated. With HT, neurodegeneration morphology shifted to apoptosis-necrosis hybrid and apoptotic forms in human HIE, while neurons in HI piglets were unshifting and protected robustly. Oligomers and putative TCPs of α-synuclein (αSyn), nitrated-Syn and aggregated αSyn, misfolded/oxidized superoxide dismutase-1 (SOD1), and prion protein (PrP) were detected with highly specific antibodies by immunohistochemistry, immunofluorescence, and immunoblotting. αSyn and SOD1 TCPs were seen in human HIE brains regardless of HT treatment. αSyn and SOD1 TCPs were detected as early as 29 h after injury in piglets and QA-injured human oligodendrocytes and neurons in culture. Cell immunophenotyping by immunofluorescence showed αSyn detected with antibodies to aggregated/oligomerized protein; nitrated-Syn accumulated in neurons, sometimes appearing as focal dendritic aggregations. Co-localization also showed aberrant αSyn accumulating in presynaptic terminals. Proteinase K-resistant PrP accumulated in ischemic Purkinje cells, and their target regions had PrP-positive neuritic plaque-like pathology. Immunofluorescence revealed misfolded/oxidized SOD1 in neurons, axons, astrocytes, and oligodendrocytes. HT attenuated TCP formation in piglets. We conclude that HT differentially affects brain damage in humans and piglets. HT shifts neuronal cell death to other forms in human while blocking ischemic necrosis in piglet for sustained protection. HI and excitotoxicity also acutely induce formation of TCPs and prion-like proteins from IDPs globally throughout the brain in gray matter and white matter. HT attenuates proteinopathy in piglets but seemingly not in humans. Shifting of cell death type and aberrant toxic protein formation could explain the selective system vulnerability, connectome spreading, and persistent damage seen in neonatal HIE leading to lifelong consequences even after HT treatment.

Evidence of α-Synuclein/Glucocerebrosidase Dual Targeting by Iminosugar Derivatives

Intrinsically disordered proteins (IDPs) are highly flexible molecules often linked to the onset of incurable diseases. Despite their great therapeutic potential, IDPs are often considered as undruggable because they lack defined binding pockets, which constitute the basis of drug discovery approaches. However, small molecules that interact with the intrinsically disordered state of α-synuclein, the protein linked to Parkinson's disease (PD), were recently identified and shown to act as chemical chaperones. Glucocerebrosidase (GCase) is an enzyme crucially involved in PD, since mutations that code for GCase are among the most frequent genetic risk factors for PD. Following the "dual-target" approach, stating that one carefully designed molecule can, in principle, interfere with more than one target, we identified a pharmacological chaperone for GCase that interacts with the intrinsically disordered monomeric form of α-synuclein. This result opens novel avenues to be explored in the search for molecules that act on dual targets, in particular, with challenging targets such as IDPs.

An Analysis of Intrinsic Protein Disorder in Antimicrobial Peptides

Antibiotic resistance, driven by the rise of pathogens like VRE and MRSA, poses a global health threat, prompting the exploration of antimicrobial peptides (AMPs) as alternatives to traditional antibiotics. AMPs, known for their broad-spectrum activity and structural flexibility, share characteristics with intrinsically disordered proteins, which lack a rigid structure and play diverse roles in cellular processes. This study aims to quantify the intrinsic disorder and liquid-liquid phase separation (LLPS) propensity in AMPs, advancing our understanding of their antimicrobial mechanisms and potential therapeutic applications. To investigate the propensity for intrinsic disorder and LLPS in AMPs, we compared the AMPs to the human proteome. The AMP sequences were retrieved from the AMP database (APD3), while the human proteome was obtained from the UniProt database. We analyzed amino acid composition using the Composition Profiler tool and assessed intrinsic disorder using various predictors, including PONDR® and IUPred, through the Rapid Intrinsic Disorder Analysis Online (RIDAO) platform. For LLPS propensity, we employed FuzDrop, and FuzPred was used to predict context-dependent binding behaviors. Statistical analyses, such as ANOVA and χ2 tests, were performed to determine the significance of observed differences between the two groups. We analyzed over 3000 AMPs and 20,000 human proteins to investigate differences in amino acid composition, intrinsic disorder, and LLPS potential. Composition analysis revealed distinct differences in amino acid abundance, with AMPs showing an enrichment in both order-promoting and disorder-promoting amino acids compared to the human proteome. Intrinsic disorder analysis, performed using a range of predictors, consistently demonstrated that AMPs exhibit higher levels of predicted disorder than human proteins, with significant differences confirmed by statistical tests. LLPS analysis, conducted using FuzDrop, showed that AMPs had a lower overall propensity for LLPS compared to human proteins, although specific subsets of AMPs exhibited high LLPS potential. Additionally, redox-dependent disorder predictions highlighted significant differences in how AMP and human proteins respond to oxidative conditions, further suggesting functional divergences between the two proteomes. CH-CDF plot analysis revealed that AMPs and human proteins occupy distinct structural categories, with AMPs showing a greater proportion of highly disordered proteins compared to the human proteome. These findings underscore key molecular differences between AMPs and human proteins, with implications for their antimicrobial activity and potential therapeutic applications. Our study reveals that AMPs possess a significantly higher degree of intrinsic disorder and specific subsets exhibit LLPS potential, distinguishing them from the human proteome. These molecular characteristics likely contribute to their antimicrobial function and adaptability, offering valuable insights for developing novel therapeutic strategies to combat antibiotic resistance.

Drugging Disordered Proteins by Conformational Selection to Inform Therapeutic Intervention

Drugging intrinsically disordered proteins (IDPs) has historically been a major challenge due to their lack of stable binding sites, conformational heterogeneity, and rapid ability to self-associate or bind nonspecific neighbors. Furthermore, it is unclear whether binders of disordered proteins (i) induce entirely new conformations or (ii) target transient prestructured conformations via stabilizing existing states. To distinguish between these two mechanisms, we utilize molecular dynamics simulations to induce structured conformations in islet amyloid polypeptide (IAPP), a disordered endocrine peptide implicated in Type II Diabetes. Using umbrella sampling, we measure conformation-specific affinities of molecules previously shown to bind IAPP to determine if they can discriminate between two distinct IAPP conformations (fixed in either α-helix or β-sheet). We show that our two-state model of IAPP faithfully predicts the experimentally observed selectivity of two classes of IAPP binders while revealing differences in their molecular binding mechanisms. Specifically, the binding preferences of foldamers designed for human IAPP were not fully accounted for by conformational selection, unlike those of β-breaking peptides designed to mimic IAPP self-assembly sequences. Furthermore, the binding of these foldamers, but not β-breaking peptides, was disrupted by changes in the rat IAPP sequence. Taken together, our data quantify the sequence and conformational specificity for IAPP binders and reveal that conformational selection sometimes overrides sequence-level specificity. This work highlights the important role of conformational selection in stabilizing IDPs, and it reveals how fixed conformations can provide a tractable target for developing disordered protein binders.

A cysteine-less and ultra-fast split intein rationally engineered from being aggregation-prone to highly efficient in protein trans-splicing

Split inteins catalyze protein trans-splicing by ligating their extein sequences while undergoing self-excision, enabling diverse protein modification applications. However, many purified split intein precursors exhibit partial or no splicing activity for unknown reasons. The Aes123 PolB1 intein, a representative of the rare cysteine-less split inteins, is of particular interest due to its resistance to oxidative conditions and orthogonality to thiol chemistries. In this work, we identify β-sheet-dominated aggregation of its N-terminal intein fragment as the origin of its low (~30%) splicing efficiency. Using computational, biochemical, and biophysical analyses, we characterize the fully active monomeric fraction and pinpoint aggregation-prone regions. Supported by a crystal structure, we design stably monomeric mutants with nearly complete splicing activity. The optimized CLm intein (Cysteine-Less and monomeric) retains the wild-type's ultra-fast reaction rate and serves as an efficient, thiol-independent protein modification tool. We find that other benchmark split inteins show similar precursor aggregation, suggesting that this general phenomenon arises from the intrinsic challenge to maintain the precursor in a partially disordered state while promoting stable folding upon fragment association.

Dual-Functional Synthetic Linear and Cyclic Peptides with Anti-Amyloid and Antimicrobial Activities for Alzheimer's Disease

Dual-functional peptides exhibiting both anti-amyloid and antimicrobial activities have attracted attention as promising candidates for Alzheimer's disease treatment. The advantage of these peptides lies in their ability to simultaneously target both the amyloid cascade hypothesis and the microbial infection hypothesis, in contrast to single-function inhibitors. However, most of the reported dual-functional peptides to date are natural peptides, and the development of synthetic peptides in this area remains limited. In this study, we propose two strategies to aid in the discovery of synthetic dual-functional peptides. We then report four distinct synthetic dual-functional peptides identified using these strategies, with the Aβ1-40/Aβ1-42 fibrillation system and common bacterial strains serving as a proof-of-concept platform. One strategy involves repurposing existing knowledge, while the other breaks from established conventions. Using the first strategy, we discovered a very short dual-functional linear peptide. With the second strategy, we identified a simple dual-functional cyclic peptide. Furthermore, by combining these two strategies, we developed a hybrid dual-functional peptide incorporating both linear and cyclic structures. We hope that our findings will contribute to the future discovery of more synthetic dual-functional peptides for treating Alzheimer's disease.

Amber ff24EXP-GA, Based on Empirical Ramachandran Distributions of Glycine and Alanine Residues in Water

Molecular dynamics (MD) offers important insights into intrinsically disordered peptides and proteins (IDPs) at a level of detail that often surpasses that available through experiments. Recent studies indicate that MD force fields do not reproduce intrinsic conformational ensembles of amino acid residues in water well, which limits their applicability to IDPs. We report a new MD force field, Amber ff24EXP-GA, derived from Amber ff14SB by optimizing the backbone dihedral potentials for guest glycine and alanine residues in cationic GGG and GAG peptides, respectively, to best match the guest residue-specific spectroscopic data. Amber ff24EXP-GA outperforms Amber ff14SB with respect to conformational ensembles of all 14 guest residues x (G, A, L, V, I, F, Y, Dp, Ep, R, C, N, S, T) in GxG peptides in water, for which complete sets of spectroscopic data are available. Amber ff24EXP-GA captures the spectroscopic data for at least 7 guest residues (G, A, V, F, C, T, Ep) better than CHARMM36m and exhibits more amino acid specificity than both the parent Amber ff14SB and CHARMM36m. Amber ff24EXP-GA reproduces the experimental data on three folded proteins and three longer IDPs well, while outperforming Amber ff14SB on short unfolded peptides.

Structural Heterogeneity of Intermediate States Facilitates CRIPT Peptide Binding to the PDZ3 Domain: Insights from Molecular Dynamics and Markov State Model Analysis

Intrinsically disordered proteins (IDPs), characterized by a lack of defined tertiary structure, are ubiquitous and indispensable components of cellular machinery. These proteins participate in a diverse array of biological processes, often undergoing conformational transitions upon binding to their target, a phenomenon termed "folding-upon-binding." The finding raises the question of how to achieve rapid binding kinetics in the presence of intrinsic disorder, and the underlying molecular mechanism remains elusive. This study investigated the interaction between the C-terminal region of CRIPT and the third PDZ domain of PSD-95, a critical complex in neuronal development. Upon binding, the CRIPT peptide adopts a β-strand conformation, a process meticulously characterized through extensive molecular dynamics simulations totaling 67.7 μs. Our findings reveal a funnel-like binding landscape in which IDPs can adopt multiple conformations prior to binding, forming structurally heterogeneous intermediate complexes and leading to diverse binding pathways. The stabilization of these intermediate complexes necessitates a dynamic interplay of native and non-native interactions. Markov state model analysis underscores the important role of structural heterogeneity as it contributes to accelerated binding. These findings enrich the classical fly-casting mechanism and provide novel insights into the functional advantages conferred by intrinsic disorder.

AIUPred - Binding: Energy Embedding to Identify Disordered Binding Regions

Intrinsically disordered regions (IDRs) play critical roles in various cellular processes, often mediating interactions through disordered binding regions that transition to ordered states. Experimental characterization of these functional regions is highly challenging, underscoring the need for fast and accurate computational tools. Despite their importance, predicting disordered binding regions remains a significant challenge due to limitations in existing datasets and methodologies. In this study, we introduce AIUPred-binding, a novel prediction tool leveraging a high dimensional mathematical representation of structural energies - we call energy embedding - and pathogenicity scores from AlphaMissense. By employing a transfer learning approach, AIUPred-binding demonstrates improved accuracy in identifying functional sites within IDRs. Our results highlight the tool's ability to discern subtle features within disordered regions, addressing biases and other challenges associated with manually curated datasets. We present AIUPred-binding integrated into the AIUPred web framework as a versatile and efficient resource for understanding the functional roles of IDRs. AIUPred-binding is freely accessible at https://aiupred.elte.hu.

IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning

Identification of intrinsically disordered regions (IDRs) in proteins is essential for understanding fundamental cellular processes. The IDRs can be divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their lengths. In previous studies, most computational methods ignored the differences between LDRs and SDRs, and therefore failed to capture the different patterns of LDRs and SDRs. In this study, we propose IDP-EDL, an ensemble of three predictors. The component predictors were first built based on pretrained protein language model and applied task-specific fine-tuning for short, long, and generic disordered regions. A meta predictor was then trained to integrate three task-specific predictors into the final predictor. The results of experiments show that task-specific supervised fine-tuning can capture the different features of LDRs and SDRs and IDP-EDL can achieve stable performance on datasets with different ratios of LDRs and SDRs. More importantly, IDP-EDL can reach or even surpass state-of-the-art performance than other existing predictors on independent test sets. IDP-EDL is available at https://github.com/joestarXjx/IDP-EDL.

Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged by the fact that approximately half of the human proteome is made up of proteins that do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered proteins (IDPs), are involved in numerous physiological functions and are associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), and type 2 diabetes. Targeting these proteins is challenging for two reasons: i) we need to preserve their physiological functions, and ii) drug design by molecular docking is not possible due to the lack of reliable starting conditions. Faced with this challenge, the solutions proposed by artificial intelligence (AI) such as AlphaFold are clearly unsuitable. Instead, we suggest an innovative approach consisting of mimicking, in short synthetic peptides, the conformational flexibility of IDPs. These peptides, which we call adaptive peptides, are derived from the domains of IDPs that become structured after interacting with a ligand. Adaptive peptides are designed with the aim of selectively antagonizing the harmful effects of IDPs, without targeting them directly but through selected ligands, without affecting their physiological properties. This "target the target, not the arrow" strategy is promised to open a new route to drug discovery for currently undruggable proteins.

Structure-Based Modeling of the Gut Bacteria-Host Interactome Through Statistical Analysis of Domain-Domain Associations Using Machine Learning

The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome's influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein-protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial-human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study's findings contribute to the understanding of the intricate gut microbiome-host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.

AFflecto: A web server to generate conformational ensembles of flexible proteins from AlphaFold models

Intrinsically disordered proteins and regions (IDPs/IDRs) leverage their structural flexibility to fulfill essential cellular functions, with dysfunctions often linked to severe diseases. However, the relationships between their sequences, structural dynamics and functional roles remain poorly understood. Understanding these complex relationships is crucial for therapeutic development, highlighting the need for methods to generate plausible IDP/IDR conformational ensembles. While AlphaFold (AF) excels at modeling structured domains, it fails to accurately represent disordered regions, leaving a significant portion of proteomes inaccurately modeled. We present AFflecto, a user-friendly web server for generating large conformational ensembles of proteins that include both structured domains and IDRs from AF structural models. AFflecto identifies IDRs as tails, linkers or loops by analyzing their structural context. Additionally, it incorporates a method to identify conditionally folded IDRs that AF may incorrectly predict as natively folded elements. The conformational space is globally explored using efficient stochastic sampling algorithms. AFflecto's web interface allows users to customize the modeling, by modifying boundaries between ordered and disordered regions, and selecting among several sampling strategies. The web server is freely available at https://moma.laas.fr/applications/AFflecto/.

IDPpred: a new sequence-based predictor for identification of intrinsically disordered protein with enhanced accuracy

Discovery of intrinsically disordered proteins (IDPs) and protein hybrids that contain both intrinsically disordered protein regions (IDPRs) along with ordered regions has changed the sequence-structure-function paradigm of protein. These proteins with lack of persistently fixed structure are often found in all organisms and play vital roles in various biological processes. Some of them are considered as potential drug targets due to their overrepresentation in pathophysiological processes. The major bottlenecks for characterizing such proteins are their occasional overexpression, difficulty in getting purified homogeneous form and the challenge of investigating them experimentally. Sequence-based prediction of intrinsic disorder remains a useful strategy especially for many large-scale proteomic investigations. However, worst accuracy still occurs for short disordered regions with less than ten residues, for the residues close to order-disorder boundaries, for regions that undergo coupled folding and binding in presence of partner, and for prediction of fully disordered proteins. Annotation of fully disordered proteins mostly relies on the far-UV circular dichroism experiment which gives overall secondary structure composition without residue-level resolution. Current methods including that using secondary structure information failed to predict half of target IDPs correctly in the recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment. This study utilized profiles of random sequential appearance of physicochemical properties of amino acids and random sequential appearance of order and disorder promoting amino acids in protein together with the existing CIDER feature for the prediction of IDP from sequence input. Our method was found to significantly outperform the existing predictors across different datasets.Communicated by Ramaswamy H. Sarma.

PDFll: Predictors of Disorder and Function of Proteins from the Language of Life

The identification of intrinsically disordered proteins and their functional roles is largely dependent on the performance of computational predictors, necessitating a high standard of accuracy in these tools. In this context, we introduce a novel series of computational predictors, termed PDFll (Predictors of Disorder and Function of proteins from the Language of Life), which are designed to offer precise predictions of protein disorder and associated functional roles based on protein sequences. PDFll is developed through a two-step process. Initially, it leverages large-scale protein language models (pLMs), trained on an extensive dataset comprising billions of protein sequences. Subsequently, the embeddings derived from pLMs are integrated into streamlined, yet sophisticated, deep-learning models to generate predictions. These predictions notably surpass the performance of existing state-of-the-art predictors, particularly those that forecast disorder and function without utilizing evolutionary information.

Investigating the Restricted Dynamical Environment in and Around Aβ Peptide Oligomers in Aqueous Ionic Liquid Solutions

It is widely believed that the aggregation of amyloid β (Aβ) peptides into soluble oligomers is the root cause behind Alzheimer's disease. In this study, we have performed room-temperature molecular dynamics (MD) simulations of aggregated Aβ oligomers of different sizes (pentamer (O(5)), decamer (O(10)), and hexadecamer (O(16))) in binary aqueous solutions containing 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) ionic liquid (IL). Investigations have been carried out to obtain a microscopic understanding of the effects of the IL on the dynamic environment around the exterior surfaces and within the confined nanocores of the oligomers. The calculations revealed that in contrast to nearly uniform dynamics near the exterior surface, heterogeneous structural distortions of oligomers of varying sizes and nonuniform distributions of water and IL components within their core volumes modify the core dynamics in a differential manner. It is demonstrated that increasingly restricted mobility of water and IL components is the origin behind the longer time scale of dynamic heterogeneity in and around the oligomers. Importantly, due to the equivalent nondirectional nature of the B-F bonds, BF4- anions are found to reorient on a time scale faster than that of water molecules. Further, the structural relaxation of protein-anion (PA) hydrogen bonds around the oligomers has been found to be correlated with sluggish translational motions of the anions but anticorrelated with their reorientational time scale. In addition, it is quantified that compared to the pure aqueous medium, strengthening of protein-water (PW) hydrogen bonds in the presence of the IL leads to their longer lifetimes.

Strategies for inhibiting amyloid fibrillation: Current status and future prospects

One of the hallmarks of multiple neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, is deposition of insoluble amyloid fibrils, which are toxic proteinaceous structures containing cross β-sheets. Several inhibitory strategies have been devised by researchers to impede or slow down the generation of such toxic species. Small compounds, peptides, and antibodies have been studied as possible inhibitors to interfere with key steps in amyloid production. Furthermore, adjusting environmental variables, such as temperature and pH have been known to impact the amyloid fibrillation process. Additionally, strategies are also available to reduce the possibility of protein misfolding so as to inhibit the subsequent development of fibrils, simply by stabilizing native protein conformations. It is very promising to develop targeted inhibitory therapies and comprehend the complexities of amyloid fibrillation in order to develop effective therapeutics to slow the progression of neurodegenerative disorders linked to misfolding and aggregation of proteins.

Molecular crowding and amyloidogenic self-assembly: Emergent perspectives from modern computations

In recent decades, the conventional protein folding paradigm has been challenged by intriguing properties of disordered peptide sequences that do not adopt stably folded conformations. Such intrinsically disordered proteins and protein regions (IDPs and IDRs) are poised uniquely in biology due to their propensity for self-aggregation, amyloidogenesis, and correlations with a cluster of debilitating diseases. Complexities underlying their structural and functional manifestations are enhanced in the presence of molecular crowding via non-specific protein-protein and protein-solvent contacts. Enabled by technological advances, physics-based algorithms, and data science, modern computer simulations provide unprecedented insights into the structure, function, dynamics, and thermodynamics of complex macromolecular systems. These characteristics are frequently correlated and manifest into unique observables. This chapter presents an overview of how such methodologies can lend insights and drive investigations into the molecular trifecta of crowding, protein self-aggregation, and amyloidogenesis. It begins with a general overview of disordered proteins in relation to biological function and of a suite of relevant experimental methods. Specific examples are showcased in the biological context. This is followed by a description of the computational approaches that supplant experimental efforts, with an elaboration on enhanced molecular simulation methods. The chapter concludes by alluding to expanded possibilities in disease amelioration.

Impact of Guanidinium Hydrochloride on the Shapes of Prothymosin-α and α-Synuclein Is Dramatically Different

The effects of guanidinium hydrochloride (GdmCl) on two intrinsically disordered proteins (IDPs) are investigated using simulations of the self-organized polymer-IDP (SOP-IDP) model. The impact of GdmCl is taken into account using the molecular transfer model (MTM). We show that due to the dramatic reduction in the stiffness of the highly charged Prothymosin-α (ProTα) with increasing concentration of GdmCl ([GdmCl]), the radius of gyration (Rg) decreases sharply until about 1.0 M. Above 1.0 M, ProTα expands, caused by the swelling effect of GdmCl. In contrast, Rg of α-Synuclein (αSyn) swells as continuously as [GdmCl] increases, with most of the expansion occurring at concentrations less than 0.2 M. Strikingly, the amplitude of the small-angle X-ray scattering (SAXS) profiles for ProTα increases until [GdmCl] ≈ 1.0 M and decreases beyond 1.0 M. The [GdmCl]-dependent SAXS profiles for αSyn, which has a pronounced bump at small wave vector (q ∼ 0.5 nm-1) at low [GdmCl] (≤0.2 M), monotonically decrease at all values of [GdmCl]. The contrasting behavior predicted by the combination of MTM and SOP-IDP simulations may be qualitatively understood by modeling ProTα as a strongly charged polyelectrolyte with nearly uniform density of charges along the chain contour and αSyn as a nearly neutral polymer, except near the C-terminus, where the uncompensated negatively charged residues are located. The precise predictions for the SAXS profiles as a function of [GdmCl] can be readily tested.

Global analysis of endogenous protein disorder in cells

Disorder and flexibility in protein structures are essential for biological function but can also contribute to diseases, such as neurodegenerative disorders. However, characterizing protein folding on a proteome-wide scale within biological matrices remains challenging. Here we present a method using a bifunctional chemical probe, named TME, to capture in situ, enrich and quantify endogenous protein disorder in cells. TME exhibits a fluorescence turn-on effect upon selective conjugation with proteins with free cysteines in surface-exposed and flexible environments-a distinctive signature of protein disorder. Using an affinity-based proteomic approach, we identify both basal disordered proteins and those whose folding status changes under stress, with coverage to proteins even of low abundance. In lymphoblastoid cells from individuals with Parkinson's disease and healthy controls, our TME-based strategy distinguishes the two groups more effectively than lysate profiling methods. High-throughput TME fluorescence and proteomics further reveal a universal cellular quality-control mechanism in which cells adapt to proteostatic stress by adopting aggregation-prone distributions and sequestering disordered proteins, as illustrated in Huntington's disease cell models.

Techniques for Bioinformatic Applications in Protein Dynamics

A method is described by which bioinformatic concepts and tools can be applied to the study of protein dynamic properties. Sequences are transformed into numerical strings by representing each amino acid by a residue specific average value of the crystallographic alpha carbon B factor. These dynamic sequences are then Fourier transformed. The Fourier coefficients, each of which contains information about the entire sequence, viewed on a specific length scale, can then be used to study a wide variety of dynamic characteristics in a manner which is completely inaccessible using conventional tools.

Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions

INTRODUCTION

Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.

AREAS COVERED

We overview 20 years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides, and lipids. These methods range from simple discriminant analysis to sophisticated deep transformer networks that use protein language models. They generate relatively accurate predictions as evidenced by the results of a recently published community-driven assessment.

EXPERT OPINION

MoRFs prediction is a mature field of research that is poised to continue at a steady pace in the foreseeable future. We anticipate further expansion of the scope of MoRF predictions to additional partner molecules, such as nucleic acids, and continued use of recent machine learning advances. Other future efforts should concentrate on improving availability of MoRF predictions by releasing, maintaining, and popularizing web servers and by depositing MoRF predictions to large databases of protein structure and function predictions. Furthermore, accurate MoRF predictions should be coupled with the equally accurate prediction and modeling of the resulting structures of complexes.

Ion mobility mass spectrometry unveils conformational effects of drug lead EPI-001 on the intrinsically disordered N-terminal domain of the androgen receptor

Intrinsically disordered proteins (IDPs) are important drug targets as they are key actors within cell signaling networks. However, the conformational plasticity of IDPs renders them challenging to characterize, which is a bottleneck in developing small molecule drugs that bind to IDPs and modulate their behavior. In relation to this, ion mobility mass spectrometry (IM-MS) is a useful tool to investigate IDPs, as it can reveal their conformational preferences. It can also offer important insights in drug discovery, as it can measure binding stoichiometry and unveil conformational shifts of IDPs exerted by the binding of small drug-like molecules. Herein, we have used IM-MS to investigate the effect of drug lead EPI-001 on the disordered N-terminal domain of the androgen receptor (AR-NTD). Despite structural heterogeneity rendering the NTD a challenging region of the protein to drug, this domain harbors most, if not all, of the transcriptional activity. We quantify the stoichiometry of EPI-001 binding to various constructs corresponding to functional domains of AR-NTD and show that it binds to separate constructs containing transactivation unit (TAU)-1 and TAU-5, respectively, and that 1-2 molecules bind to a larger construct containing both sequences. We also identify a conformational shift upon EPI-001 binding to the TAU-5, and to a much lesser extent with TAU-1 containing constructs. This work provides novel insight on the interactions of EPI-001 with the AR-NTD, and the structural alterations that it exerts, and positions IM-MS as an informative tool that will enhance the tractability of IDPs, potentially leading to better therapies.

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein

The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defined ligand-binding pockets in its disordered structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able to statistically distinguish the fuzzy ensemble of conformational substates of αS in neat water from those in aqueous fasudil (small molecule of interest) solution. In particular, the presence of fasudil in the solvent either modulates pre-existing states of αS or gives rise to new conformational states of αS, akin to an ensemble-expansion mechanism. The ensembles display strong conformation-dependence in residue-wise interaction with the small molecule. A thermodynamic analysis indicates that small-molecule modulates the structural repertoire of αS by tuning protein backbone entropy, however entropy of the water remains unperturbed. Together, this study sheds light on the intricate interplay between small molecules and IDPs, offering insights into entropic modulation and ensemble expansion as key biophysical mechanisms driving potential therapeutics.

Structures of Oligomeric States of Tau Protein, Amyloid-β, α-Synuclein and Prion Protein Implicated in Alzheimer's Disease, Parkinson's Disease and Prionopathies

In this review, we focus on the biophysical and structural aspects of the oligomeric states of physiologically intrinsically disordered proteins and peptides tau, amyloid-β and α-synuclein and partly disordered prion protein and their isolations from animal models and human brains. These protein states may be the most toxic agents in the pathogenesis of Alzheimer's and Parkinson's disease. It was shown that oligomers are important players in the aggregation cascade of these proteins. The structural information about these structural states has been provided by methods such as solution and solid-state NMR, cryo-EM, crosslinking mass spectrometry, AFM, TEM, etc., as well as from hybrid structural biology approaches combining experiments with computational modelling and simulations. The reliable structural models of these protein states may provide valuable information for future drug design and therapies.

The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.

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.

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

How Salt and Temperature Drive Reentrant Condensation of Aβ40

Within the framework of liquid-liquid phase separation (LLPS), biomolecular condensation orchestrates vital cellular processes, and its dysregulation is implicated in severe pathological conditions. Recent studies highlight the role of intrinsically disordered proteins (IDPs) in LLPS, yet the influence of microenvironmental factors has remained a puzzling factor. Here, via computational simulation of the impact of solution conditions on LLPS behavior of neurologically pathogenic IDP Aβ40, we chanced upon a salt-driven reentrant condensation phenomenon, wherein Aβ40 aggregation increases with low salt concentrations (25-50 mM), followed by a decline with further salt increments. An exploration of the thermodynamic and kinetic signatures of reentrant condensation unveils a nuanced interplay between protein electrostatics and ionic strength as potential drivers. Notably, the charged residues of the N-terminus exhibit a nonmonotonic response to salt screening, intricately linked to the recurrence of reentrant behavior in hydrophobic core-induced condensation. Intriguingly, our findings also unveil the reappearance of similar reentrant condensation phenomena under varying temperature conditions. Collectively, our study illuminates the profoundly context-dependent nature of Aβ40s liquid-liquid phase separation behavior, extending beyond its intrinsic molecular framework, where microenvironmental cues wield significant influence over its aberrant functionality.

Dimerization of Full-Length Aβ-42 Peptide: A Comparison of Different Force Fields and Water Models

Among the two isoforms of amyloid-β i. e., Aβ-40 and Aβ-42, Aβ-42 is more toxic due to its increased aggregation propensity. The oligomerization pathways of amyloid-β may be investigated by studying its dimerization process at an atomic level. Intrinsically disordered proteins (IDPs) lack well-defined structures and are associated with numerous neurodegenerative disorders. Molecular dynamics simulations of these proteins are often limited by the choice of parameters due to inconsistencies in the empirically developed protein force fields and water models. To evaluate the accuracy of recently developed force fields for IDPs, we study the dimerization of full-length Aβ-42 in aqueous solution with three different combinations of AMBER force field parameters and water models such as ff14SB/TIP3P, ff19SB/OPC, and ff19SB/TIP3P using classical MD and Umbrella Sampling method. This work may be used as a benchmark to compare the performance of different force fields for the simulations of IDPs.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.

Simulating the anti-aggregative effect of fasudil in early dimerisation process of α-synuclein

The aggregation of the protein α-synuclein into amyloid deposits is associated with multiple neurological disorders, including Parkinson's disease. Soluble amyloid oligomers are reported to exhibit higher toxicity than insoluble amyloid fibrils, with dimers being the smallest toxic oligomer. Small molecule drugs, such as fasudil, have shown potential in targeting α-synuclein aggregation and reducing its toxicity. In this study, we use atomistic molecular dynamics simulations to demonstrate how fasudil affects the earliest stage of aggregation, namely dimerization. Our results show that the presence of fasudil reduces the propensity for intermolecular contact formation between protein chains. Consistent with previous reports, our analysis confirms that fasudil predominantly interacts with the negatively charged C-terminal region of α-synuclein. However, we also observe transient interactions with residues in the charged N-terminal and hydrophobic NAC regions. Our simulations indicate that while fasudil prominently interacts with the C-terminal region, it is the transient interactions with residues in the N-terminal and NAC regions that effectively block the formation of intermolecular contacts between protein chains and prevent early dimerization of this disordered protein.

Drude2019IDPC polarizable force field reveals structure-function relationship of insulin

Intrinsically disordered proteins (IDPs) lack stable tertiary structures under physiological conditions, yet play key roles in biological processes and associated with human complex diseases. Their conformational characteristics and high content of charged residues make the use of polarizable force fields an advantageous for simulating IDPs. The Drude2019IDP polarizable force field, previously introduced, has demonstrated comprehensive enhancements and improvements in dipeptides, short peptides, and IDPs, achieving a balanced sampling between IDPs and structured proteins. However, the performance in simulating 5 dipeptides was found to be underestimate. Therefore, we individually performed reweighting and grid-based energy correction map (CMAP) optimization for these 5 dipeptides, resulting in the enhanced Drude2019IDPC force field. The performance of Drude2019IDPC was evaluated with 5 dipeptides, 5 disordered short peptides, and a representative IDP. The results demonstrated a marked improvement comparing with original Drude2019IDP. To further substantiate the capabilities of Drude2019IDPC, MD simulation and Markov state model (MSM) were applied to wild type and mutant for insulin, to elucidate the difference of conformational characteristics and transition path. The findings reveal that mutation can maintain the monomorphic characteristics, providing insights for engineered insulin development. These results indicate that Drude2019IDPC could be used to reveal the structure-function relationship for other proteins.

How to drug a cloud? Targeting intrinsically disordered proteins

Biologically active proteins/regions without stable structure (i.e., intrinsically disordered proteins and regions (IDPs and IDRs)) are commonly found in all proteomes. They have a unique functional repertoire that complements the functionalities of ordered proteins and domains. IDPs/IDRs are multifunctional promiscuous binders capable of folding at interaction with specific binding partners on a template- or context-dependent manner, many of which undergo liquid-liquid phase separation, leading to the formation of membrane-less organelles and biomolecular condensates. Many of them are frequently related to the pathogenesis of various human diseases. All this defines IDPs/IDRs as attractive targets for the development of novel drugs. However, their lack of unique structures, multifunctionality, binding promiscuity, and involvement in unusual modes of action preclude direct use of traditional structure-based drug design approaches for targeting IDPs/IDRs, and make disorder-based drug discovery for these "protein clouds" challenging. Despite all these complexities there is continuing progress in the design of small molecules affecting IDPs/IDRs. This article describes the major structural features of IDPs/IDRs and the peculiarities of the disorder-based functionality. It also discusses the roles of IDPs/IDRs in various pathologies, and shows why the approaches elaborated for finding drugs targeting ordered proteins cannot be directly used for the intrinsic disorder-based drug design, and introduces some novel methodologies suitable for these purposes. Finally, it emphasizes that regardless of their multifunctionality, binding promiscuity, lack of unique structures, and highly dynamic nature, "protein clouds" are principally druggable. Significance Statement Intrinsically disordered proteins and regions are highly abundant in nature, have multiple important biological functions, are commonly involved in the pathogenesis of a multitude of human diseases, and are therefore considered as very attractive drug targets. Although dealing with these unstructured multifunctional protein/regions is a challenging task, multiple innovative approaches have been designed to target them by small molecules.

Disentangling the Complexity in Protein Complexes Using Complementary Isotope-Labeling and Multiple-Receiver NMR Spectroscopy

Intrinsically disordered proteins are abundant in eukaryotic systems, but they remain largely elusive pharmacological targets. NMR spectroscopy proved to be a suitable method to study these proteins and their interaction with one another or with drug candidates. Although NMR can give atomistic information about these interplays, molecular complexity due to severe spectral overlap, limited sample stability, and quantity remain an issue and hamper widespread applications. Here, we propose an approach to simultaneously map protein-protein binding sites onto two interacting partners by employing a complementary isotope-labeling strategy and a multiple receiver NMR detection scheme. With one partner being 15N,2H labeled and the interacting one being 13C,1H-labeled, we exploited proton and carbon detection to obtain clean and easily readable information. The method is illustrated with an application to the 50 kDa ternary protein complex formed between the prominent oncogenic transcription factor complex Myc/MAX and the tumor suppressor BRCA1.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Understanding Antimicrobial Peptide Synergy: Differential Binding Interactions and Their Impact on Membrane Integrity

Research on antimicrobial peptides (AMPs) has been conducted as a solution to overcome antibiotic resistance. In particular, the synergistic effect that appears when two or more AMPs are used in combination has been observed. To find an effective synergistic combination, it is necessary to understand the underlying mechanism. However, a consistent explanation for this phenomenon has not yet been provided due to limitations in experimentally determining or predicting the structure of the heteroaggregates formed by the interactions between different AMPs and the interaction of the aggregate surface with the lipid membrane surface. In this study, we conducted molecular dynamics simulations for two heterogeneous aggregates of melittin-indolicidin and pexiganan-indolicidin to observe their structures in the solution phase and their interactions with the lipid membrane. We aimed to determine how the surfaces of these aggregates interact with the lipid membrane. Due to the different amino acid residue sequence characteristics of melittin and pexiganan, we found that when the two AMPs bind to indolicidin, they form aggregates with completely different structural characteristics. Accordingly, the sequence characteristics of pexiganan, which exhibits a relatively unstable structure compared to melittin in aqueous solution or on lipid membranes, allow for a more stable interaction with the lipid membrane when forming aggregates with indolicidin, effectively inhibiting the integrity of the lipid membranes. We also found that the amino acid residues forming the surface of the AMP aggregate show differential binding strengths to different lipid species forming the lipid membrane, thereby disrupting the membrane in a way that weakens its integrity. Through this, we provided insight into the basic principle of how the synergistic effect of AMPs occurs.

Unravelling the involvement of protein disorder in cyanobacterial stress responses

This study has explored the involvement of Intrinsically Disordered Proteins (IDPs) in cyanobacterial stress response. IDPs possess distinct physicochemical properties, which allow them to execute diverse functions. Anabaena PCC 7120, the model photosynthetic, nitrogen-fixing cyanobacterium encodes 688 proteins (11 % of the total proteome) with at least one intrinsically disordered region (IDR). Of these, 130 proteins that showed >30 % overall disorder were designated as IDPs. Physico-chemical analysis, showed these IDPs to adopt shapes ranging from 'globular' to 'tadpole-like'. Upon exposure to NaCl, 41 IDP-encoding genes were found to be differentially expressed. Surprisingly, most of these were induced, indicating the importance of IDP-accumulation in overcoming salt stress. Subsequently, six IDPs were identified to be induced by multiple stresses (salt, ammonium and selenite). Interestingly, the presence of these 6-multiple stress-induced IDPs was conserved in filamentous cyanobacteria. Utilizing the experimental proteomic data of Anabaena, these 6 IDPs were found to interact with many proteins involved in diverse pathways, underscoring their physiological importance as protein hubs. This study lays the framework for IDP-related research in Anabaena by (a) identifying, as well as physiochemically characterizing, all the disordered proteins and (b) uncovering a subset of IDPs that are likely to be critical in adaptation to environmental stresses.

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences

We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.

Regulation of the structural dynamics, aggregation, and pathogenicity of polyQ-expanded Huntingtin by osmolytes

Huntington Disease is an autosomal dominant neurodegenerative disease caused by expansion of the polymorphic trinucleotide CAG repeat of the HTT gene to code for an expanded glutamine track of the mutant Huntingtin protein (mHTT). Like other neurodegenerative diseases, symptomatic presentation of Huntington Disease is age-dependent or age-related. This age-dependent manifestation of an autosomal dominant disease trait underscores important and possibly priming role of age-related changes in cellular physiology that are conducive to disease presentation. Herein, we present studies on the effects of osmolytes on mHTT structuring and aggregation, vis-a-vis pathogenicity. We show that stabilizing polyol osmolytes, by their generic activity in promoting protein structuring and compaction, drive aggregation of the disordered mHTT protein and simultaneously inhibit their binding to and sequestration of key transcription factors for improved homeostasis and cell survival under stress. These and related observations in the literature give strong support to the notion that lower molecular weight and structurally dynamic forms of mHTT contribute importantly to disease pathogenesis. Aging is associated with important changes in the cell environment-disease protein accumulation, reduced hydration, and macromolecular crowding as examples. These changes have significant consequences on the structuring and pathogenicity of the disordered mHTT protein. A crowded and less hydrated aging cell environment is conducive to mHTT binding to and inhibition of cell regulatory protein function on the one hand, and in promoting mHTT aggregation on the other hand, to culminate in Huntington disease presentation.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

Proteome-scale characterisation of motif-based interactome rewiring by disease mutations

Whole genome and exome sequencing are reporting on hundreds of thousands of missense mutations. Taking a pan-disease approach, we explored how mutations in intrinsically disordered regions (IDRs) break or generate protein interactions mediated by short linear motifs. We created a peptide-phage display library tiling ~57,000 peptides from the IDRs of the human proteome overlapping 12,301 single nucleotide variants associated with diverse phenotypes including cancer, metabolic diseases and neurological diseases. By screening 80 human proteins, we identified 366 mutation-modulated interactions, with half of the mutations diminishing binding, and half enhancing binding or creating novel interaction interfaces. The effects of the mutations were confirmed by affinity measurements. In cellular assays, the effects of motif-disruptive mutations were validated, including loss of a nuclear localisation signal in the cell division control protein CDC45 by a mutation associated with Meier-Gorlin syndrome. The study provides insights into how disease-associated mutations may perturb and rewire the motif-based interactome.