Roles, Characteristics, and Analysis of Intrinsically Disordered Proteins: A Minireview

Self-packed size-exclusion columns enable versatile high-throughput native, top-down, and ion mobility-mass spectrometry studies on proteins and complexes

Native MS (nMS) is a key structural biology technique that makes it possible to study intact proteins and their interactions. Unfortunately, non-volatile salts are incompatible with nMS, which demands a laborious desalting procedure. Non-denaturing size-exclusion chromatography (SEC) allows both rapid desalting and separation and has previously been explored for nMS automation. However, SEC at conventional scale requires rather large sample amounts as well as harsh ESI conditions, which can cause protein unfolding. Capillary LC allows softer conditions; however, the few commercially available SEC columns appropriate for this flow rate are prohibitively expensive for many laboratories. Existing protocols for packing buffer exchange columns rely on specialized equipment and/or result in columns that have limited capacity for size-based protein separation. Here, we present self-packed miniaturized SEC columns with different stationary phases and customizable dimensions. The columns, produced via slurry packing with an ordinary LC pump were used across a range of samples in several applications including nMS, top-down MS (TDMS), ligand screening, and ion mobility (IM)-MS. Native separation allowed acquisition of data from samples containing more than one protein. We acquired native TDMS data of 3 proteins in 12 min, with up to 47 % sequence coverage. IM-MS of alpha-synuclein at different charge states was measured in ca. 60 min (including calibrants), with results that match the literature. Finally, we used SEC-nMS to rapidly screen proteolysis-targeting chimera candidates and performed collision-induced unfolding (CIU) of a PROTAC-induced ternary complex. Through this work, we highlight the potential of SEC to support developments in structural MS.

Prediction of interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins from dihedral angles

Intrinsically disordered proteins (IDPs) are proteins that do not form uniquely defined three-dimensional (3-D) structures. Experimental research on IDPs is difficult since they go against the traditional protein structure-function paradigm. Although there are several predictors of disorder based on amino acid sequences, but very limited based on the 3-D structures of proteins. Dihedral angles have a significant role in predicting protein structure because they establish a protein's backbone, which, coupled with its side chain, establishes its overall shape. Here, we have carried out atomistic Molecular Dynamics (MD) simulations on four different proteins: one ordered protein (Monellin), two partially disordered proteins (p53-TAD and Amyloid beta (Aβ1-42) peptide), and one completely disordered protein (Histatin 5). The MD simulation trajectories for the corresponding four proteins were used to conduct dihedral angle (ϕ and ѱ) analysis. Then, the average dihedral angles for each of the residues were calculated and plotted against the residue index. We noticed steep rises or falls in the average ϕ value at certain locations in the plot. These sudden shifts in the average ϕ value reflect the interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins. Using this method, the probable conformer of a protein with a higher degree of disorder can be found among the ensembles of structures sampled during the MD simulations. The results of our study offer new understandings on precisely identifying regions of various degrees of disorder in intrinsically disordered proteins.

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

Protein Fold Usages in Ribosomes: Another Glance to the Past

The analysis of protein fold usage, similar to codon usage, offers profound insights into the evolution of biological systems and the origins of modern proteomes. While previous studies have examined fold distribution in modern genomes, our study focuses on the comparative distribution and usage of protein folds in ribosomes across bacteria, archaea, and eukaryotes. We identify the prevalence of certain 'super-ribosome folds,' such as the OB fold in bacteria and the SH3 domain in archaea and eukaryotes. The observed protein fold distribution in the ribosomes announces the future power-law distribution where only a few folds are highly prevalent, and most are rare. Additionally, we highlight the presence of three copies of proto-Rossmann folds in ribosomes across all kingdoms, showing its ancient and fundamental role in ribosomal structure and function. Our study also explores early mechanisms of molecular convergence, where different protein folds bind equivalent ribosomal RNA structures in ribosomes across different kingdoms. This comparative analysis enhances our understanding of ribosomal evolution, particularly the distinct evolutionary paths of the large and small subunits, and underscores the complex interplay between RNA and protein components in the transition from the RNA world to modern cellular life. Transcending the concept of folds also makes it possible to group a large number of ribosomal proteins into five categories of urfolds or metafolds, which could attest to their ancestral character and common origins. This work also demonstrates that the gradual acquisition of extensions by simple but ordered folds constitutes an inexorable evolutionary mechanism. This observation supports the idea that simple but structured ribosomal proteins preceded the development of their disordered extensions.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

NMR-Based Characterization of the Interaction between Yeast Oxa1-CTD and Ribosomes

In mitochondria, the major subunits of oxidative phosphorylation complexes are translated by the mitochondrial ribosome (mito-ribosome). The correct insertion and assembly of these subunits into the inner mitochondrial membrane (IMM) are facilitated by mitochondrial oxidase assembly protein 1 (Oxa1) during the translation process. This co-translational insertion process involves an association between the mito-ribosome and the C-terminus of Oxa1 (Oxa1-CTD) Nuclear magnetic resonance (NMR) methods were mainly used to investigate the structural characterization of yeast Oxa1-CTD and its mode of interaction with the E. coli 70S ribosome. Oxa1-CTD forms a transient α-helical structure within the residues P342-Q385, which were reported to form an α-helix when combining with the ribosome. Two conserved contact sites that could interact with the ribosome were further identified. The first site was located on the very end of the N-terminus (V321-I327), and the second one encompassed a stretch of amino acid residues I348-Q370. Based on our discoveries and previous reports, a model has been proposed in which Oxa1-CTD interacts with ribosomes, accompanied by transient-to-stable transitions at the second contact site. These observations may enhance our understanding of the potential role of Oxa1-CTD in facilitating the assembly of oxidative phosphorylation complexes and provide insight into the structural characteristics of Oxa1-CTD.

Folding of heterologous proteins in bacterial cell factories: Cellular mechanisms and engineering strategies

The expression of correctly folded and functional heterologous proteins is important in many biotechnological production processes, whether it is enzymes, biopharmaceuticals or biosynthetic pathways for production of sustainable chemicals. For industrial applications, bacterial platform organisms, such as E. coli, are still broadly used due to the availability of tools and proven suitability at industrial scale. However, expression of heterologous proteins in these organisms can result in protein aggregation and low amounts of functional protein. This review provides an overview of the cellular mechanisms that can influence protein folding and expression, such as co-translational folding and assembly, chaperone binding, as well as protein quality control, across different model organisms. The knowledge of these mechanisms is then linked to different experimental methods that have been applied in order to improve functional heterologous protein folding, such as codon optimization, fusion tagging, chaperone co-production, as well as strain and protein engineering strategies.

Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious global challenge requiring urgent and permanent therapeutic solutions. These solutions can only be engineered if the patterns and rate of mutations of the virus can be elucidated. Predicting mutations and the structure of proteins based on these mutations have become necessary for early drug and vaccine design purposes in anticipation of future viral mutations. The amino acid composition (AAC) of proteomes and individual viral proteins provide avenues for exploitation since AACs have been previously used to predict structure, shape and evolutionary rates. Herein, the frequency of amino acid residues found in 1637 complete proteomes belonging to 11 SARS-CoV-2 variants/lineages were analyzed. Leucine is the most abundant amino acid residue in the SARS-CoV-2 with an average AAC of 9.658% while tryptophan had the least abundance of 1.11%. The AAC and ranking of lysine and glycine varied in the proteome. For some variants, glycine had higher frequency and AAC than lysine and vice versa in other variants. Tryptophan was also observed to be the most intolerant to mutation in the various proteomes for the variants used. A correlogram revealed a very strong correlation of 0.999992 between B.1.525 (Eta) and B.1.526 (Iota) variants. Furthermore, isoleucine and threonine were observed to have a very strong negative correlation of -0.912, while cysteine and isoleucine had a very strong positive correlation of 0.835 at p < 0.001. Shapiro-Wilk normality test revealed that AAC values for all the amino acid residues except methionine showed no evidence of non-normality at p < 0.05. Thus, AACs of SARS-CoV-2 variants can be predicted using probability and z-scores. AACs may be beneficial in classifying viral strains, predicting viral disease types, members of protein families, protein interactions and for diagnostic purposes. They may also be used as a feature along with other crucial factors in machine-learning based algorithms to predict viral mutations. These mutation-predicting algorithms may help in developing effective therapeutics and vaccines for SARS-CoV-2.

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.

New insights into disordered proteins and regions according to the FOD-M model

A collection of intrinsically disordered proteins (IDPs) having regions with the status of intrinsically disordered (IDR) according to the Disprot database was analyzed from the point of view of the structure of hydrophobic core in the structural unit (chain / domain). The analysis includes all the Homo Sapiens as well as Mus Musculus proteins present in the DisProt database for which the structure is available. In the analysis, the fuzzy oil drop modified model (FOD-M) was used, taking into account the external force field, modified by the presence of other factors apart from polar water, influencing protein structuring. The paper presents an alternative to secondary-structure-based classification of intrinsically disordered regions (IDR). The basis of our classification is the ordering of hydrophobic core as calculated by the FOD-M model resulting in FOD-ordered or FOD-unordered IDRs.

The Quest for Anti-α-Synuclein Antibody Specificity-Lessons Learnt From Flow Cytometry Analysis

The accumulation of alpha-synuclein (aSyn) is the hallmark of a group of neurodegenerative conditions termed synucleopathies. Physiological functions of aSyn, including those outside of the CNS, remain elusive. However, a reliable and reproducible evaluation of aSyn protein expression in different cell types and especially in low-expressing cells is impeded by the existence of a huge variety of poorly characterized anti-aSyn antibodies and a lack of a routinely used sensitive detection methods. Here, we developed a robust flow cytometry-based workflow for aSyn detection and antibody validation. We test our workflow using three commercially available antibodies (MJFR1, LB509, and 2A7) in a variety of human cell types, including induced pluripotent stem cells, T lymphocytes, and fibroblasts, and provide a cell- and antibody-specific map for aSyn expression. Strikingly, we demonstrate a previously unobserved unspecificity of the LB509 antibody, while the MJFR1 clone revealed specific aSyn binding however with low sensitivity. On the other hand, we identified an aSyn-specific antibody clone 2A7 with an optimal sensitivity for detecting aSyn in a range of cell types, including those with low aSyn expression. We further utilize our workflow to demonstrate the ability of the 2A7 antibody to distinguish between physiological differences in aSyn expression in neuronal and non-neuronal cells from the cortical organoids, and in neural progenitors and midbrain dopaminergic neurons from healthy controls and in patients with Parkinson's disease who have aSyn gene locus duplication. Our results provide a proof of principle for the use of high-throughput flow cytometry-based analysis of aSyn and highlight the necessity of rigorous aSyn antibody validation to facilitate the research of aSyn physiology and pathology.

Intrinsically Disordered Proteins: Critical Components of the Wetware

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

Microsecond Dynamics During the Binding-induced Folding of an Intrinsically Disordered Protein

Tau is an intrinsically disordered protein implicated in many neurodegenerative diseases. The repeat domain fragment of tau, tau-K18, is known to undergo a disorder to order transition in the presence of lipid micelles and vesicles, in which helices form in each of the repeat domains. Here, the mechanism of helical structure formation, induced by a phospholipid mimetic, sodium dodecyl sulfate (SDS) at sub-micellar concentrations, has been studied using multiple biophysical probes. A study of the conformational dynamics of the disordered state, using photoinduced electron transfer coupled to fluorescence correlation spectroscopy (PET-FCS) has indicated the presence of an intermediate state, I, in equilibrium with the unfolded state, U. The cooperative binding of the ligand (L), SDS, to I has been shown to induce the formation of a compact, helical intermediate (IL₅) within the dead time (∼37 µs) of a continuous flow mixer. Quantitative analysis of the PET-FCS data and the ensemble microsecond kinetic data, suggests that the mechanism of induction of helical structure can be described by a U ↔ I ↔ IL₅ ↔ FL₅ mechanism, in which the final helical state, FL₅, forms from IL₅ with a time constant of 50-200 µs. Finally, it has been shown that the helical conformation is an aggregation-competent state that can directly form amyloid fibrils.

Function, Regulation, and Dysfunction of Intrinsically Disordered Proteins

The discovery that a considerable fraction of the eukaryotic proteins lacks a well-defined three-dimensional structure in their native state has revolutionised our general understanding of proteins [...].