Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling

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.

Nonlinear amplification of nano bowl surface concavity on the critical response threshold to biosignals

Polymer nanoparticles that can sharply sense and detect biological signals in cells are promising candidates for biomedical and theranostic nanomaterials. However, the response ability of current polymer assemblies poorly matches the requirement of trace concentration level (10-6 ~ 10-9 mol/L) of cellular biosignals due to their linear signal input-to-function output mode, which impedes their practical applications in vivo. Here we report a kind of nanobowl system with pH-tunable invaginated morphology that can nonlinearly amplify the response abilities toward biosignals by modulating the surface concavity. Compared to conventional spherical nanoparticles, nonspherical nanobowls with a specific concave structure reduce the critical response threshold of polymers by up to 5 orders of magnitude, from millimole to nanomole level, covering most of biosignal concentration windows. Moreover, we find that this nonlinear signal gain effect is originated from the collective impact of a single signal on transitioning the polymer chain aggregation state of individual assemblies, rather than just altering a certain unit or chain. This nonlinear signal-to-response mechanism is potential to solve the tricky problems of probing and sensing endogenous signals with trace physiological concentration.

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.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

Unraveling the role of the nucleocapsid protein in SARS-CoV-2 pathogenesis: From viral life cycle to vaccine development

BACKGROUND

The nucleocapsid protein (N protein) is the most abundant protein in SARS-CoV-2. Viral RNA and this protein are bound by electrostatic forces, forming cytoplasmic helical structures known as nucleocapsids. Subsequently, these nucleocapsids interact with the membrane (M) protein, facilitating virus budding into early secretory compartments.

SCOPE OF REVIEW

Exploring the role of the N protein in the SARS-CoV-2 life cycle, pathogenesis, post-sequelae consequences, and interaction with host immunity has enhanced our understanding of its function and potential strategies for preventing SARS-CoV-2 infection.

MAJOR CONCLUSION

This review provides an overview of the N protein's involvement in SARS-CoV-2 infectivity, highlighting its crucial role in the virus-host protein interaction and immune system modulation, which in turn influences viral spread.

GENERAL SIGNIFICANCE

Understanding these aspects identifies the N protein as a promising target for developing effective antiviral treatments and vaccines against SARS-CoV-2.

Interplay between anion-receptor and anion-solvent interactions in halide receptor complexes characterized with ultrafast infrared spectroscopies

The competition between host-guest binding and solvent interactions is a crucial factor in determining the binding affinities and selectivity of molecular receptor species. The interplay between these competing interactions, however, have been difficult to disentangle. In particular, the development of molecular-level descriptions of solute-solvent interactions remains a grand experimental challenge. Herein, we investigate the prototypical halide receptor meso-octamethylcalix[4]pyrrole (OMCP) complexed with either chloride or bromide anions in both dichloromethane (DCM) and chloroform (trichloromethane, TCM) solvent using ultrafast infrared transient absorption and 2D IR spectroscopies. OMCP·Br- complexes in both solvents display slower vibrational relaxation dynamics of the OMCP pyrrole NH stretches, consistent with weaker H-bonding interactions with OMCP compared to chloride and less efficient intermolecular relaxation to the solvent. Further, OMCP·Br- complexes show nearly static spectral diffusion dynamics compared to OMCP·Cl-, indicating larger structural fluctuations occur within chloride complexes. Importantly, distinct differences in the vibrational spectra and dynamics are observed between DCM and TCM solutions. The data are consistent with stronger and more perturbative solvent effects in TCM compared to DCM, despite DCM's larger dielectric constant and smaller reported OMCP·X- binding affinities. These differences are attributed to the presence of weak H-bond interactions between halides and TCM, in addition to competing interactions from the bulky tetrabutylammonium countercation. The data provide important experimental benchmarks for quantifying the role of solvent and countercation interactions in anion host-guest complexes.

Exploring Intrinsic Disorder in Human Synucleins and Associated Proteins

In this work, we explored the intrinsic disorder status of the three members of the synuclein family of proteins-α-, β-, and γ-synucleins-and showed that although all three human synucleins are highly disordered, the highest levels of disorder are observed in γ-synuclein. Our analysis of the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members revealed that the pathological mutations A30P, E46K, H50Q, A53T, and A53E associated with the early onset of Parkinson's disease caused some increase in the local disorder propensity of human α-synuclein. A comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluation of their levels of intrinsic disorder using a set of commonly used bioinformatics tools revealed that, irrespective of their origin, all members of the synuclein family analyzed in this study were predicted to be highly disordered proteins, indicating that their intrinsically disordered nature represents an evolutionary conserved and therefore functionally important feature. A detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members utilizing a set of commonly used disorder analysis tools showed that the human α-synuclein interactome has relatively higher levels of intrinsic disorder as compared with the interactomes of human β- and γ- synucleins and revealed that, relative to the β- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. Although proteins interacting with three human synucleins were characterized by highly diversified functionalities, this analysis also revealed that the interactors of three human synucleins were involved in three common functional pathways, such as the synaptic vesicle cycle, serotonergic synapse, and retrograde endocannabinoid signaling. Taken together, these observations highlight the critical importance of the intrinsic disorder of human synucleins and their interactors in various neuronal processes.

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

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

Inter-BRCT linker is probably the most intolerant region of the BRCA1 BRCT domain

Pathogenic mutations in BRCA1 are associated with an increased risk of hereditary breast, ovarian, and some other cancers; however, the clinical significance of many mutations in this gene remains unknown (Variants of Unknown Significance/VUS). Since mutations in intolerant regions of a protein lead to dysfunction and pathogenicity, identifying these regions helps to predict the clinical importance of VUSs. This study aimed to identify intolerant regions of BRCA1 and understand the possible root of this susceptibility. Intolerant regions appear to carry more pathogenic mutations than expected due to their lower tolerance to missense variations. Therefore, we hypothesized that among the BRCA1 regions, the higher the mutation density, the greater the intolerance. Thus, pathogenic mutation density and regional intolerance scores were calculated to identify BRCA1-intolerant regions. To investigate the pathogenic mechanisms of missense-intolerant regions in BRCA1, transcription activation (TA) experiments and molecular dynamics (MD) simulations were also performed. The results showed that the RING domain, followed by the BRCT domain, has the highest density of pathogenic mutations. In the BRCT domain, a higher density of pathogenic mutations was observed in the inter-BRCT linker. Additionally, scores generated by Missense Tolerance Ratio-3D (MTR3D) and the Missense Tolerance Ratio consensus (MTRX) showed that the inter-BRCT linker is more intolerant than other regions of the BRCT domain. The MD results showed that mutations in the inter-BRCT linker led to cancer susceptibility, likely due to disruption of the interaction between BRCA1 and phosphopeptides. TA laboratory assays further supported the importance of the inter-BRCT linker.Communicated by Ramaswamy H. Sarma.

Amyloids, amorphous aggregates and assemblies of peptides - Assessing aggregation

Amyloid and amorphous aggregates represent the two major categories of aggregates associated with diseases, and although exhibiting distinct features, researchers often treat them as equivalent, which demonstrates the need for more thorough characterization. Here, we compare amyloid and amorphous aggregates based on their biochemical properties, kinetics, and morphological features. To further decipher this issue, we propose the use of peptide self-assemblies as minimalistic models for understanding the aggregation process. Peptide building blocks are significantly smaller than proteins that participate in aggregation, however, they make a plausible means to bridge the gap in discerning the aggregation process at the more complex, protein level. Additionally, we explore the potential use of peptide-inspired models to research the liquid-liquid phase separation as a feasible mechanism preceding amyloid formation. Connecting these concepts can help clarify our understanding of aggregation-related disorders and potentially provide novel drug targets to impede and reverse these serious illnesses.

Molecular mechanisms behind the generation of pro-oncogenic HIV-1 matrix protein p17 variants

HIV-1 matrix protein p17 variants (vp17s), characterized by amino acid insertions at the COOH-terminal region of the viral protein, have been recently identified and studied for their biological activity. Different from their wild-type counterpart (refp17), vp17s display a potent B cell growth and clonogenic activity. Recent data have highlighted the higher prevalence of vp17s in people living with HIV-1 (PLWH) with lymphoma compared with those without lymphoma, suggesting that vp17s may play a key role in lymphomagenesis. Molecular mechanisms involved in vp17 development are still unknown. Here we assessed the efficiency of HIV-1 Reverse Transcriptase (RT) in processing this genomic region and highlighted the existence of hot spots of mutation in Gag, at the end of the matrix protein and close to the matrix-capsid junction. This is possibly due to the presence of inverted repeats and palindromic sequences together with a high content of Adenine in the 322-342 nucleotide portion, which constrain HIV-1 RT to pause on the template. To define the recombinogenic properties of hot spots of mutation in the matrix gene, we developed plasmid vectors expressing Gag and a minimally modified Gag variant, and measured homologous recombination following cell co-nucleofection by next-generation sequencing. Data obtained allowed us to show that a wide range of recombination events occur in concomitance with the identified hot spots of mutation and that imperfect events may account for vp17s generation.

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

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

High-throughput assay exploiting disorder-to-order conformational switches: application to the proteasomal Rpn10:E6AP complex

Conformational switching is pervasively driven by protein interactions, particularly for intrinsically disordered binding partners. We developed a dually orthogonal fluorescence-based assay to monitor such events, exploiting environmentally sensitive fluorophores. This assay is applied to E3 ligase E6AP, as its AZUL domain induces a disorder-to-order switch in an intrinsically disordered region of the proteasome, the so-named Rpn10 AZUL-binding domain (RAZUL). By testing various fluorophores, we developed an assay appropriate for high-throughput screening of Rpn10:E6AP-disrupting ligands. We found distinct positions in RAZUL for fluorophore labeling with either acrylodan or Atto610, which had disparate spectral responses to E6AP binding. E6AP caused a hypsochromic shift with increased fluorescence of acrylodan-RAZUL while decreasing fluorescence intensity of Atto610-RAZUL. Combining RAZUL labeled with either acrylodan or Atto610 into a common sample achieved robust and orthogonal measurement of the E6AP-induced conformational switch. This approach is generally applicable to disorder-to-order (or vice versa) transitions mediated by molecular interactions.

The inhibitor protein IF1 from mammalian mitochondria inhibits ATP hydrolysis but not ATP synthesis by the ATP synthase complex

The hydrolytic activity of the ATP synthase in bovine mitochondria is inhibited by a protein called IF1, but bovine IF1 has no effect on the synthetic activity of the bovine enzyme in mitochondrial vesicles in the presence of a proton motive force. In contrast, it has been suggested based on indirect observations that human IFI inhibits both the hydrolytic and synthetic activities of the human ATP synthase and that the activity of human IF1 is regulated by the phosphorylation of Ser-14 of mature IF1. Here, we have made both human and bovine IF1 which are 81 and 84 amino acids long, respectively, and identical in 71.4% of their amino acids and have investigated their inhibitory effects on the hydrolytic and synthetic activities of ATP synthase in bovine submitochondrial particles. Over a wide range of conditions, including physiological conditions, both human and bovine IF1 are potent inhibitors of ATP hydrolysis, with no effect on ATP synthesis. Also, substitution of Ser-14 with phosphomimetic aspartic and glutamic acids had no effect on inhibitory properties, and Ser-14 is not conserved throughout mammals. Therefore, it is unlikely that the inhibitory activity of mammalian IF1 is regulated by phosphorylation of this residue.

N-Terminal Proline Editing for the Synthesis of Peptides with Mercaptoproline and Selenoproline: Mechanistic Insights Lead to Greater Efficiency in Proline Native Chemical Ligation

Native chemical ligation (NCL) at proline has been limited by cost and synthetic access. In addition, prior examples of NCL using mercaptoproline have exhibited stalling of the reaction after thioester exchange, due to inefficient S → N acyl transfer. Herein, we develop methods, using inexpensive Boc-4R-hydroxyproline, for the solid-phase synthesis of peptides containing N-terminal 4R-mercaptoproline and 4R-selenoproline. The synthesis proceeds via proline editing on the N-terminus of fully synthesized peptides on the solid phase, converting an N-terminal Boc-4R-hydroxyproline to the 4S-bromoproline, followed by an SN2 reaction with potassium thioacetate or selenobenzoic acid. After cleavage from the resin and deprotection, peptides with functionalized N-terminal proline amino acids were obtained. NCL reactions with mercaptoproline proceeded slowly under standard NCL conditions, with the S-acyl transthioesterification intermediate observed as a major species. Computational investigations indicated that the bicyclic intermediates and transition states for S → N acyl transfer are sufficiently low in energy (10-15 kcal mol-1 above starting material) that ring strain cannot explain the slow S → N acyl transfer. Instead, the bicyclic zwitterionic tetrahedral intermediate has a low barrier for reversion to the S-acyl intermediate, causing reversion to the thioester (reverse reaction) to occur preferentially over elimination to generate the amide (forward reaction). We hypothesized that a buffer capable of general acid and/or general base catalysis could promote S → N acyl transfer and thus achieve greater efficiency in proline NCL. In the presence of 2 M imidazole at pH 6.8, NCL with mercaptoproline proceeded efficiently to generate the peptide with a native amide bond. NCL with selenoproline also proceeded efficiently to generate the desired products when a thiophenol thioester was employed as a ligation partner. After desulfurization or deselenization, the products obtained were identical to those synthesized directly, confirming that the solid-phase proline editing reactions proceeded stereospecifically and without epimerization.

Biological importance of arginine: A comprehensive review of the roles in structure, disorder, and functionality of peptides and proteins

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain make it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Functional unfoldomics: Roles of intrinsic disorder in protein (multi)functionality

Intrinsically disordered proteins (IDPs), which are functional proteins without stable tertiary structure, and hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) constitute prominent parts of all proteomes collectively known as unfoldomes. IDPs/IDRs exist as highly dynamic structural ensembles of rapidly interconverting conformations and are characterized by the exceptional structural heterogeneity, where their different parts are (dis)ordered to different degree, and their overall structure represents a complex mosaic of foldons, inducible foldons, inducible morphing foldons, non-foldons, semifoldons, and even unfoldons. Despite their lack of unique 3D structures, IDPs/IDRs play crucial roles in the control of various biological processes and the regulation of different cellular pathways and are commonly involved in recognition and signaling, indicating that the disorder-based functional repertoire is complementary to the functions of ordered proteins. Furthermore, IDPs/IDRs are frequently multifunctional, and this multifunctionality is defined by their structural flexibility and heterogeneity. Intrinsic disorder phenomenon is at the roots of the structure-function continuum model, where the structure continuum is defined by the presence of differently (dis)ordered regions, and the function continuum arises from the ability of all these differently (dis)ordered parts to have different functions. In their everyday life, IDPs/IDRs utilize a broad spectrum of interaction mechanisms thereby acting as interaction specialists. They are crucial for the biogenesis of numerous proteinaceous membrane-less organelles driven by the liquid-liquid phase separation. This review introduces functional unfoldomics by representing some aspects of the intrinsic disorder-based functionality.

CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions

Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions. The resulting CoMemMoRFPred method is available as an easy-to-use webserver at http://biomine.cs.vcu.edu/servers/CoMemMoRFPred. This tool will aid future studies of MemMoRFs in the context of exploring their abundance, cellular functions, and roles in pathologic phenomena.

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

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

Energy landscape quantifications of histone H3.3 recognition by chaperone DAXX reveal an uncoupled binding specificity and affinity

Histone variant H3.3 differs from the canonical histone H3.1 by only five amino acids, yet its chaperone death domain-associated protein (DAXX) can specifically recognize H3.3 over H3.1, despite having a large DAXX-interacting surface on the H3.3-H4 heterodimer common to that on the H3.1-H4 complex. This observation gives rise to the question of, from the binding energy point view, how high binding specificity may be achieved with small differences of the overall binding energy for protein-protein interactions in general. Here we investigate the mechanism of coupling of binding specificity and affinity in protein-protein interactions using the DAXX-H3.3-H4 complex as a model. Using a multi-scale method, we found that the hydrophobic interactions between DAXX and the H3.3-specific region contributed to their initial binding process. And the structural flexibility of the interacting partners contributed to the binding affinity after their encounter. By quantifying the free energy landscape, we revealed that the interaction between the specific residues of H3.3 and DAXX decreased the encounter barrier height while the folding of H3.3-H4 and DAXX increased the depth of the free energy basin of the final binding state. The encounter barrier height, which is not coupled to the thermodynamic stability of the final binding state, had a marked effect on the initial binding rate of flexible histones and chaperones. Based on the energy landscape theory, we found that the intrinsic binding energy funnel of this uncoupled recognition process was affected by the structural flexibility and the flexibility modulated the degree of coupling between binding specificity and affinity. Our work offers a biophysical explanation of the specific recognition between the histones and their chaperones, and also extends the use of energy landscape theory for understanding molecular recognitions in general.

Evolution of Intrinsic Disorder in Protein Loops

Intrinsic disorder accounts for the flexibility of protein loops, molecular building blocks that are largely responsible for the processes and molecular functions of the living world. While loops likely represent early structural forms that served as intermediates in the emergence of protein structural domains, their origin and evolution remain poorly understood. Here, we conduct a phylogenomic survey of disorder in loop prototypes sourced from the ArchDB classification. Tracing prototypes associated with protein fold families along an evolutionary chronology revealed that ancient prototypes tended to be more disordered than their derived counterparts, with ordered prototypes developing later in evolution. This highlights the central evolutionary role of disorder and flexibility. While mean disorder increased with time, a minority of ordered prototypes exist that emerged early in evolutionary history, possibly driven by the need to preserve specific molecular functions. We also revealed the percolation of evolutionary constraints from higher to lower levels of organization. Percolation resulted in trade-offs between flexibility and rigidity that impacted prototype structure and geometry. Our findings provide a deep evolutionary view of the link between structure, disorder, flexibility, and function, as well as insights into the evolutionary role of intrinsic disorder in loops and their contribution to protein structure and function.

Conformational Manifold Sampled by Two Short Linear Motif Segments Probed by Circular Dichroism, Vibrational, and Nuclear Magnetic Resonance Spectroscopy

Disordered protein segments called short linear motifs (SLiM) serve as recognition sites for a variety of biological processes and act as targeting signals, modification, and ligand binding sites. While SLiMs do not adopt one of the known regular secondary structures, the conformational distribution might still reflect the structural propensities of their amino acid residues and possible interactions between them. In the past, conformational analyses of short peptides provided compelling evidence for the notion that individual residues are less conformationally flexible than locally expected for a random coil. Here, we combined various spectroscopies (NMR, IR, vibrational, and UV circular dichroism) to determine the Ramachandran plots of two SLiM motifs, i.e., GRRDSG and GRRTSG. They are two representatives of RxxS motifs that are capable of being phosphorylated by protein kinase A, an enzyme that plays a fundamental role in a variety of biological processes. Our results reveal that the nearest and non-nearest interactions between residues cause redistributions between polyproline II and β-strand basins while concomitantly stabilizing extended relative to turn-forming and helical structures. They also cause shifts in basin positions. With increasing temperature, β-strand populations become more populated at the expense of polyproline II. While molecular dynamics simulations with Amber ff14SB and CHARMM 36m force fields indicate residue-residue interactions, they do not account for the observed structural changes.

Chaotic aging: intrinsically disordered proteins in aging-related processes

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Intrinsically disordered proteins (IDPs) play a critical role in many biological processes. Due to the inherent structural flexibility of IDPs, experimental methods present significant challenges for sampling their conformational information at the atomic level. Therefore, molecular dynamics (MD) simulations have emerged as the primary tools for modeling IDPs whose accuracy depend on force field and water model. To enhance the accuracy of physical modeling of IDPs, several force fields have been developed. However, current water models lack precision and underestimate the interaction between water molecules and proteins. Here, we used Monte-Carlo re-weighting method to re-parameterize a three-point water model based on OPC3 for IDPs (named OPC3-B). We benchmarked the performance of OPC3-B compared with nine different water models for 10 IDPs and three ordered proteins. The results indicate that the performance of OPC3-B is better than other water models for both IDPs and ordered proteins. At the same time, OPC3-B possess the power of transferability with other force field to simulate IDPs. This newly developed water model can be used to insight into the research of sequence-disordered-function paradigm for IDPs.

The Sherpa hypothesis: Phenotype-Preserving Disordered Proteins stabilize the phenotypes of neurons and oligodendrocytes

Intrinsically disordered proteins (IDPs), which can interact with many partner proteins, are central to many physiological functions and to various pathologies that include neurodegeneration. Here, we introduce the Sherpa hypothesis, according to which a subset of stable IDPs that we term Phenotype-Preserving Disordered Proteins (PPDP) play a central role in protecting cell phenotypes from perturbations. To illustrate and test this hypothesis, we computer-simulate some salient features of how cells evolve and differentiate in the presence of either a single PPDP or two incompatible PPDPs. We relate this virtual experiment to the pathological interactions between two PPDPs, α-synuclein and Tubulin Polymerization Promoting Protein/p25, in neurodegenerative disorders. Finally, we discuss the implications of the Sherpa hypothesis for aptamer-based therapies of such disorders.

Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins

We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 s compared to 1200 s for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1 = 0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.

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.

Aggregation propensities of proteins with varying degrees of disorder

The hydration thermodynamics of a globular protein (AcP), three intrinsically disordered protein regions (1CD3, 1MVF, 1F0R) and a fully disordered protein (α-synuclein) is studied by an approach that combines an all-atom explicit water molecular dynamics simulations and three-dimensional reference interaction site model (3D-RISM) theory. The variation in hydration free energy with percentage disorder of the selected proteins is investigated through its nonelectrostatic and electrostatic components. A decrease in hydration free energy is observed with an increase in percentage disorder, indicating favorable interactions of the disordered proteins with the solvent. This confirms the role of percentage disorder in determining the aggregation propensity of proteins which is measured in terms of the hydration free energy in addition to their respective mean net charge and mean hydrophobicity. The hydration free energy is decoupled into energetic and entropic terms. A residue-wise decomposition analysis of the hydration free energy for the selected proteins is evaluated. The decomposition shows that the disordered regions contribute more than the ordered ones for the intrinsically disordered protein regions. The dominant role of electrostatic interactions is confirmed from the residue-wise decomposition of the hydration free energy. The results depict that the negatively charged residues contribute more to the total hydration free energy for the proteins with negative mean net charge, while the positively charged residues contribute more for proteins with positive mean net charge.

Current research on viral proteins that interact with fibrillarin

The nucleolus is a multifunctional nuclear domain primarily dedicated to ribosome biogenesis. Certain viruses developed strategies to manipulate host nucleolar proteins to facilitate their replication by modulating ribosomal RNA (rRNA) processing. This association interferes with nucleolar functions resulting in overactivation or arrest of ribosome biogenesis, induction or inhibition of apoptosis, and affecting stress response. The nucleolar protein fibrillarin (FBL) is an important target of some plant and animal viruses. FBL is an essential and highly conserved S-adenosyl methionine (SAM) dependent methyltransferase, capable of rRNA degradation by its intrinsically disordered region (IDR), the glycine/arginine-rich (GAR) domain. It forms a ribonucleoprotein complex that directs 2'-O-methylations in more than 100 sites of pre-rRNAs. It is involved in multiple cellular processes, including initiation of transcription, oncogenesis, and apoptosis, among others. The interaction with animal viruses, including human viruses, triggered its redistribution to the nucleoplasm and cytoplasm, interfering with its role in pre-rRNA processing. Viral-encoded proteins with IDRs as nucleocapsids, matrix, Tat protein, and even a viral snoRNA, can associate with FBL, forcing the nucleolar protein to undergo atypical functions. Here we review the molecular mechanisms employed by animal and human viruses to usurp FBL functions and the effect on cellular processes, particularly in ribosome biogenesis.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Proteasome Interactome and Its Role in the Mechanisms of Brain Plasticity

Abstract

Proteasomes are highly conserved multienzyme complexes responsible for proteolytic degradation of the short-lived, regulatory, misfolded, and damaged proteins. They play an important role in the processes of brain plasticity, and decrease in their function is accompanied by the development of neurodegenerative pathology. Studies performed in different laboratories both on cultured mammalian and human cells and on preparations of the rat and rabbit brain cortex revealed a large number of proteasome-associated proteins. Since the identified proteins belong to certain metabolic pathways, multiple enrichment of the proteasome fraction with these proteins indicates their important role in proteasome functioning. Extrapolation of the experimental data, obtained on various biological objects, to the human brain suggests that the proteasome-associated proteins account for at least 28% of the human brain proteome. The proteasome interactome of the brain contains a large number of proteins involved in the assembly of these supramolecular complexes, regulation of their functioning, and intracellular localization, which could be changed under different conditions (for example, during oxidative stress) or in different phases of the cell cycle. In the context of molecular functions of the Gene Ontology (GO) Pathways, the proteins of the proteasome interactome mediate cross-talk between components of more than 30 metabolic pathways annotated in terms of GO. The main result of these interactions is binding of adenine and guanine nucleotides, crucial for realization of the nucleotide-dependent functions of the 26S and 20S proteasomes. Since the development of neurodegenerative pathology is often associated with regioselective decrease in the functional activity of proteasomes, a positive therapeutic effect would be obviously provided by the factors increasing proteasomal activity. In any case, pharmacological regulation of the brain proteasomes seems to be realized through the changes in composition and/or activity of the proteins associated with proteasomes (deubiquitinase, PKA, CaMKIIα, etc.).

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Coupled binding and folding of disordered SPIN N-terminal region in myeloperoxidase inhibition

Gram-positive pathogenic bacteria Staphylococcus express and secret staphylococcal peroxidase inhibitor (SPIN) proteins to help evade neutrophil-mediated immunity by inhibiting the activity of the main oxidative-defense player myeloperoxidase (MPO) enzyme. SPIN contains a structured 3-helix bundle C-terminal domain, which can specifically bind to MPO with high affinity, and an intrinsically disordered N-terminal domain (NTD), which folds into a structured β-hairpin and inserts itself into the active site of MPO for inhibition. Mechanistic insights of the coupled folding and binding process are needed in order to better understand how residual structures and/or conformational flexibility of NTD contribute to the different strengths of inhibition of SPIN homologs. In this work, we applied atomistic molecular dynamics simulations on two SPIN homologs, from S. aureus and S. delphini, respectively, which share high sequence identity and similarity, to explore the possible mechanistic basis for their different inhibition efficacies on human MPO. Direct simulations of the unfolding and unbinding processes at 450 K reveal that these two SPIN/MPO complexes systems follow surprisingly different mechanisms of coupled binding and folding. While coupled binding and folding of SPIN-aureus NTD is highly cooperative, SPIN-delphini NTD appears to mainly utilize a conformational selection-like mechanism. These observations are in contrast to an overwhelming prevalence of induced folding-like mechanisms for intrinsically disordered proteins that fold into helical structures upon binding. Further simulations of unbound SPIN NTDs at room temperature reveal that SPIN-delphini NTD has a much stronger propensity of forming β-hairpin like structures, consistent with its preference to fold and then bind. These may help explain why the inhibition strength is not well correlated with binding affinity for different SPIN homologs. Altogether, our work establishes the relationship between the residual conformational stability of SPIN-NTD and their inhibitory function, which can help us develop new strategies towards treating Staphylococcal infections.

Pre-Molten, Wet, and Dry Molten Globules en Route to the Functional State of Proteins

Transitions between the unfolded and native states of the ordered globular proteins are accompanied by the accumulation of several intermediates, such as pre-molten globules, wet molten globules, and dry molten globules. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling

The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.

Biophysical Characterization of the C-Terminal Tail of T. rubrum PacC Reveals an Inherent Intrinsically Disordered Structure with pH-Induced Structural Plasticity

PacC is a key transcriptional regulator of human pathogenic fungus Trichophyton rubrum with pivotal roles in pH homeostasis and virulence. We report the first biophysical characterization of the C-terminal inhibitory tail of PacC, pertinent to its physiological role in maintaining the inactive state of PacC at acidic pH which undergoes conformational changes for its proteolytic removal and activation, at alkaline pH. To gain insights into the structural features of PacC that enable the required conformational flexibility, we performed gel filtration chromatography, dynamic light scattering, circular dichroism, and 1-anilino-8-naphthalenesulfonate binding and showed that the tail exhibits properties similar to intrinsically disordered proteins, as also predicted by bioinformatics tools. We demonstrate that the C-terminal tail is conformationally flexible and attains a molten globule-like state at extremely acidic pH and undergoes biphasic GdmCl-induced unfolding in a noncooperative manner with an intermediate X state. We hypothesize that the conformational plasticity of the C-terminal tail of PacC may play a significant role in modulating its pH-dependent transcriptional activation.

Functional benefit of structural disorder for the replication of measles, Nipah and Hendra viruses

Measles, Nipah and Hendra viruses are severe human pathogens within the Paramyxoviridae family. Their non-segmented, single-stranded, negative-sense RNA genome is encapsidated by the nucleoprotein (N) within a helical nucleocapsid that is the substrate used by the viral RNA-dependent-RNA-polymerase (RpRd) for transcription and replication. The RpRd is a complex made of the large protein (L) and of the phosphoprotein (P), the latter serving as an obligate polymerase cofactor and as a chaperon for N. Both the N and P proteins are enriched in intrinsically disordered regions (IDRs), i.e. regions devoid of stable secondary and tertiary structure. N possesses a C-terminal IDR (NTAIL), while P consists of a large, intrinsically disordered N-terminal domain (NTD) and a C-terminal domain (CTD) encompassing alternating disordered and ordered regions. The V and W proteins, two non-structural proteins that are encoded by the P gene via a mechanism of co-transcriptional edition of the P mRNA, are prevalently disordered too, sharing with P the disordered NTD. They are key players in the evasion of the host antiviral response and were shown to phase separate and to form amyloid-like fibrils in vitro. In this review, we summarize the available information on IDRs within the N, P, V and W proteins from these three model paramyxoviruses and describe their molecular partnership. We discuss the functional benefit of disorder to virus replication in light of the critical role of IDRs in affording promiscuity, multifunctionality, fine regulation of interaction strength, scaffolding functions and in promoting liquid-liquid phase separation and fibrillation.

Soft disorder modulates the assembly path of protein complexes

The relationship between interactions, flexibility and disorder in proteins has been explored from many angles over the years: folding upon binding, flexibility of the core relative to the periphery, entropy changes, etc. In this work, we provide statistical evidence for the involvement of highly mobile and disordered regions in complex assembly. We ordered the entire set of X-ray crystallographic structures in the Protein Data Bank into hierarchies of progressive interactions involving identical or very similar protein chains, yielding 40205 hierarchies of protein complexes with increasing numbers of partners. We then examine them as proxies for the assembly pathways. Using this database, we show that upon oligomerisation, the new interfaces tend to be observed at residues that were characterised as softly disordered (flexible, amorphous or missing residues) in the complexes preceding them in the hierarchy. We also rule out the possibility that this correlation is just a surface effect by restricting the analysis to residues on the surface of the complexes. Interestingly, we find that the location of soft disordered residues in the sequence changes as the number of partners increases. Our results show that there is a general mechanism for protein assembly that involves soft disorder and modulates the way protein complexes are assembled. This work highlights the difficulty of predicting the structure of large protein complexes from sequence and emphasises the importance of linking predictors of soft disorder to the next generation of predictors of complex structure. Finally, we investigate the relationship between the Alphafold2’s confidence metric pLDDT for structure prediction in unbound versus bound structures, and soft disorder. We show a strong correlation between Alphafold2 low confidence residues and the union of all regions of soft disorder observed in the hierarchy. This paves the way for using the pLDDT metric as a proxy for predicting interfaces and assembly paths.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

DeepCLD: An Efficient Sequence-Based Predictor of Intrinsically Disordered Proteins

Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.

Prediction of protein-protein interaction sites in intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

The Phosphofurin Acidic Cluster Sorting Protein 2 (PACS-2) E209K Mutation Responsible for PACS-2 Syndrome Increases Susceptibility to Apoptosis

Phosphofurin acidic cluster sorting protein 2 (PACS-2) is a multifunctional cytosolic membrane trafficking protein with distinct roles in maintaining cellular homeostasis. Recent clinical reports have described 28 individuals possessing a de novo PACS-2 E209K mutation that present with epileptic seizures and cerebellar dysgenesis. As the PACS-2 E209K missense mutation has become a marker for neurodevelopmental disorders, we sought to characterize its biochemical properties. Accordingly, we observed that the PACS-2 E209K protein exhibited a slower turnover rate relative to PACS-2 wild type (WT) upon cycloheximide treatment in 293T cells. The longer half-life of PACS-2 E209K suggests a disruption in its proteostasis, with the potential for altered protein-protein interactions. Indeed, a regulatory protein in neurodevelopment known as 14-3-3ε was identified as having an increased association with PACS-2 E209K. Subsequently, when comparing the effect of PACS-2 WT and E209K expression on the staurosporine-induced apoptosis response, we found that PACS-2 E209K increased susceptibility to staurosporine-induced apoptosis in HCT 116 cells. Overall, our findings suggest PACS-2 E209K alters PACS-2 proteostasis and favors complex formation with 14-3-3ε, leading to increased cell death in the presence of environmental stressors.

Lighting up Nobel Prize-winning studies with protein intrinsic disorder

Intrinsically disordered proteins and regions (IDPs and IDRs) and their importance in biology are becoming increasingly recognized in biology, biochemistry, molecular biology and chemistry textbooks, as well as in current protein science and structural biology curricula. We argue that the sequence → dynamic conformational ensemble → function principle is of equal importance as the classical sequence → structure → function paradigm. To highlight this point, we describe the IDPs and/or IDRs behind the discoveries associated with 17 Nobel Prizes, 11 in Physiology or Medicine and 6 in Chemistry. The Nobel Laureates themselves did not always mention that the proteins underlying the phenomena investigated in their award-winning studies are in fact IDPs or contain IDRs. In several cases, IDP- or IDR-based molecular functions have been elucidated, while in other instances, it is recognized that the respective protein(s) contain IDRs, but the specific IDR-based molecular functions have yet to be determined. To highlight the importance of IDPs and IDRs as general principle in biology, we present here illustrative examples of IDPs/IDRs in Nobel Prize-winning mechanisms and processes.

Optimizing Epitope Conformational Ensembles Using α-Synuclein Cyclic Peptide "Glycindel" Scaffolds: A Customized Immunogen Method for Generating Oligomer-Selective Antibodies for Parkinson's Disease

Effectively presenting epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases, particularly neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing the healthy forms of the protein that are often more abundant. To this end, we computationally modeled scaffolded epitopes in cyclic peptides by inserting/deleting a variable number of flanking glycines ("glycindels") to best mimic a misfolding-specific conformation of an epitope of α-synuclein enriched in the oligomer ensemble, as characterized by a region most readily disordered and solvent-exposed in a stressed, partially denatured protofibril. We screen and rank the cyclic peptide scaffolds of α-synuclein in silico based on their ensemble overlap properties with the fibril, oligomer-model and isolated monomer ensembles. We present experimental data of seeded aggregation that support nucleation rates consistent with computationally predicted cyclic peptide conformational similarity. We also introduce a method for screening against structured off-pathway targets in the human proteome by selecting scaffolds with minimal conformational similarity between their epitope and the same solvent-exposed primary sequence in structured human proteins. Different cyclic peptide scaffolds with variable numbers of glycines are predicted computationally to have markedly different conformational ensembles. Ensemble comparison and overlap were quantified by the Jensen-Shannon divergence and a new measure introduced here, the embedding depth, which determines the extent to which a given ensemble is subsumed by another ensemble and which may be a more useful measure in developing immunogens that confer conformational selectivity to an antibody.

Dendritic peptide-conjugated polymeric nanovectors for non-toxic delivery of plasmid DNA and enhanced non-viral transfection of immune cells

Plasmid DNA (pDNA) transfection is advantageous for gene therapies requiring larger genetic elements, including "all-in-one" CRISPR/Cas9 plasmids, but is limited by toxicity as well as poor intracellular release and transfection efficiency in immune cell populations. Here, we developed a synthetic non-viral gene delivery platform composed of poly(ethylene glycol)-b-poly(propylene sulfide) copolymers linked to a cationic dendritic peptide (DP) via a reduceable bond, PEG-b-PPS-ss-DP (PPDP). A library of self-assembling PPDP polymers was synthesized and screened to identify optimal constructs capable of transfecting macrophages with small (pCMV-DsRed, 4.6 kb) and large (pL-CRISPR.EFS.tRFP, 11.7 kb) plasmids. The optimized PPDP construct transfected macrophages, fibroblasts, dendritic cells, and T cells more efficiently and with less toxicity than a commercial Lipo2K reagent, regardless of pDNA size and under standard culture conditions in the presence of serum. The PPDP technology described herein is a stimuli-responsive polymeric nanovector that can be leveraged to meet diverse challenges in gene delivery.

Electronic Polarization at the Interface between the p53 Transactivation Domain and Two Binding Partners

Intrinsically disordered proteins (IDPs) are an abundant class of highly charged proteins that participate in numerous crucial biological processes, often in regulatory roles. IDPs do not have one major free energy minimum with a dominant structure, instead existing as conformational ensembles of multiple semistable conformations. p53 is a prototypical protein with disordered regions and binds to many structurally diverse partners, making it a useful model for exploring the role of electrostatic interactions at IDP binding interfaces. In this study, we used the Drude-2019 force field to simulate the p53 transactivation domain with two protein partners to probe the role of electrostatic interactions in IDP protein-protein interactions. We found that the Drude-2019 polarizable force field reasonably reproduced experimental chemical shifts of the p53 transactivation domain (TAD) in one complex for which these data are available. We also found that the proteins in these complexes displayed dipole response at specific residues of each protein and that residues primarily involved in binding showed a large percent change in dipole moment between the unbound and complexed states. Probing the role of electrostatic interactions in IDP binding can allow us greater fundamental understanding of these interactions and may help with targeting p53 or its partners for drug design.

HIV-1 mutants expressing B cell clonogenic matrix protein p17 variants are increasing their prevalence worldwide

In the combined antiretroviral therapy era, lymphomas still represent the most frequent cause of death in HIV-1-infected patients. We expand previous observations dealing with the prevalence of HIV-1 matrix protein p17 variants (vp17s), characterized by peculiar amino acid insertions and endowed of B cell clonogenic activity, in HIV-1 patients with lymphoma as compared with patients without lymphoma. We show that the prevalence of HIV-1 mutants expressing vp17s is increasing worldwide over time. Moreover, we describe a cluster of HIV-1 mutants expressing a B cell clonogenic vp17 and highlight that insertions can be fixed and that viruses displaying clonogenic vp17s are actively spreading. This knowledge advocates for an extensive genomic surveillance program to monitor the evolution of such mutant virions worldwide.

AIDS-defining cancers declined after combined antiretroviral therapy (cART) introduction, but lymphomas are still elevated in HIV type 1 (HIV-1)–infected patients. In particular, non-Hodgkin’s lymphomas (NHLs) represent the majority of all AIDS-defining cancers and are the most frequent cause of death in these patients. We have recently demonstrated that amino acid (aa) insertions at the HIV-1 matrix protein p17 COOH-terminal region cause protein destabilization, leading to conformational changes. Misfolded p17 variants (vp17s) strongly impact clonogenic B cell growth properties that may contribute to B cell lymphomagenesis as suggested by the significantly higher frequency of detection of vp17s with COOH-terminal aa insertions in plasma of HIV-1–infected patients with NHL. Here, we expand our previous observations by assessing the prevalence of vp17s in large retrospective cohorts of patients with and without lymphoma. We confirm the significantly higher prevalence of vp17s in lymphoma patients than in HIV-1–infected individuals without lymphoma. Analysis of 3,990 sequences deposited between 1985 and 2017 allowed us to highlight a worldwide increasing prevalence of HIV-1 mutants expressing vp17s over time. Since genomic surveillance uncovered a cluster of HIV-1 expressing a B cell clonogenic vp17 dated from 2011 to 2019, we conclude that aa insertions can be fixed in HIV-1 and that mutant viruses displaying B cell clonogenic vp17s are actively spreading.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.