Functional Segments on Intrinsically Disordered Regions in Disease-Related Proteins

Spatially resolved transcriptomic profiling of placental development in dairy cow

The placenta plays a crucial role in successful mammalian reproduction. Ruminant animals possess a semi-invasive placenta characterized by a highly vascularized structure formed by maternal endometrial caruncles and fetal placental cotyledons, essential for full-term fetal development. The cow placenta harbors at least two trophoblast cell populations: uninucleate (UNC) and binucleate (BNC) cells. However, the limited capacity to elucidate the transcriptomic dynamics of the placental natural environment has resulted in a poor understanding of both the molecular and cellular interactions between trophoblast cells and niches, and the molecular mechanisms governing trophoblast differentiation and functionalization. To fill this knowledge gap, we employed Stereo-seq to map spatial gene expression patterns at near single-cell resolution in the cow placenta at 90 and 130 days of gestation, attaining high-resolution, spatially resolved gene expression profiles. Based on clustering and cell marker gene expression analyses, key transcription factors, including YBX1 and NPAS2, were shown to regulate the heterogeneity of trophoblast cell subpopulations. Cell communication and trajectory analysis provided a framework for understanding cell-cell interactions and the differentiation of trophoblasts into BNCs in the placental microenvironment. Differential analysis of cell trajectories identified a set of genes involved in regulation of trophoblast differentiation. Additionally, spatial modules and co-variant genes that help shape specific tissue structures were identified. Together, these findings provide foundational insights into important biological pathways critical to the placental development and function in cows.

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.

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.

Enrichment patterns of intrinsic disorder in proteins

Intrinsically disordered regions in proteins have been shown to be important in protein function. However, not all proteins contain the same amount of intrinsic disorder. The variation in the levels of intrinsic disorder in different types of proteins has been extensively studied over the last two decades. It is now known that the levels of intrinsic disorder vary in proteins across organisms, functions, diseases, and cellular locations. This review consolidates the known trends in the abundance of intrinsic disorder identified in groups of proteins across varying conditions and functions. It also presents new data towards the understanding of intrinsic disorder in cell type-specific proteins.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01016-7.

Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information

The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions.

Cdk8 Kinase Module: A Mediator of Life and Death Decisions in Times of Stress

The Cdk8 kinase module (CKM) of the multi-subunit mediator complex plays an essential role in cell fate decisions in response to different environmental cues. In the budding yeast S. cerevisiae, the CKM consists of four conserved subunits (cyclin C and its cognate cyclin-dependent kinase Cdk8, Med13, and Med12) and predominantly negatively regulates a subset of stress responsive genes (SRG’s). Derepression of these SRG’s is accomplished by disassociating the CKM from the mediator, thus allowing RNA polymerase II-directed transcription. In response to cell death stimuli, cyclin C translocates to the mitochondria where it induces mitochondrial hyper-fission and promotes regulated cell death (RCD). The nuclear release of cyclin C requires Med13 destruction by the ubiquitin-proteasome system (UPS). In contrast, to protect the cell from RCD following SRG induction induced by nutrient deprivation, cyclin C is rapidly destroyed by the UPS before it reaches the cytoplasm. This enables a survival response by two mechanisms: increased ATP production by retaining reticular mitochondrial morphology and relieving CKM-mediated repression on autophagy genes. Intriguingly, nitrogen starvation also stimulates Med13 destruction but through a different mechanism. Rather than destruction via the UPS, Med13 proteolysis occurs in the vacuole (yeast lysosome) via a newly identified Snx4-assisted autophagy pathway. Taken together, these findings reveal that the CKM regulates cell fate decisions by both transcriptional and non-transcriptional mechanisms, placing it at a convergence point between cell death and cell survival pathways.

A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation

R-loops are non-canonical, three-stranded nucleic acid structures composed of a DNA:RNA hybrid, a displaced single-stranded (ss)DNA, and a trailing ssRNA overhang. R-loops perform critical biological functions under both normal and disease conditions. To elucidate their cellular functions, we need to understand the mechanisms underlying R-loop formation, recognition, signaling, and resolution. Previous high-throughput screens identified multiple proteins that bind R-loops, with many of these proteins containing folded nucleic acid processing and binding domains that prevent (e.g., topoisomerases), resolve (e.g., helicases, nucleases), or recognize (e.g., KH, RRMs) R-loops. However, a significant number of these R-loop interacting Enzyme and Reader proteins also contain long stretches of intrinsically disordered regions (IDRs). The precise molecular and structural mechanisms by which the folded domains and IDRs synergize to recognize and process R-loops or modulate R-loop-mediated signaling have not been fully explored. While studying one such modular R-loop Reader, the Fragile X Protein (FMRP), we unexpectedly discovered that the C-terminal IDR (C-IDR) of FMRP is the predominant R-loop binding site, with the three N-terminal KH domains recognizing the trailing ssRNA overhang. Interestingly, the C-IDR of FMRP has recently been shown to undergo spontaneous Liquid-Liquid Phase Separation (LLPS) assembly by itself or in complex with another non-canonical nucleic acid structure, RNA G-quadruplex. Furthermore, we have recently shown that FMRP can suppress persistent R-loops that form during transcription, a process that is also enhanced by LLPS via the assembly of membraneless transcription factories. These exciting findings prompted us to explore the role of IDRs in R-loop processing and signaling proteins through a comprehensive bioinformatics and computational biology study. Here, we evaluated IDR prevalence, sequence composition and LLPS propensity for the known R-loop interactome. We observed that, like FMRP, the majority of the R-loop interactome, especially Readers, contains long IDRs that are highly enriched in low complexity sequences with biased amino acid composition, suggesting that these IDRs could directly interact with R-loops, rather than being “mere flexible linkers” connecting the “functional folded enzyme or binding domains”. Furthermore, our analysis shows that several proteins in the R-loop interactome are either predicted to or have been experimentally demonstrated to undergo LLPS or are known to be associated with phase separated membraneless organelles. Thus, our overall results present a thought-provoking hypothesis that IDRs in the R-loop interactome can provide a functional link between R-loop recognition via direct binding and downstream signaling through the assembly of LLPS-mediated membrane-less R-loop foci. The absence or dysregulation of the function of IDR-enriched R-loop interactors can potentially lead to severe genomic defects, such as the widespread R-loop-mediated DNA double strand breaks that we recently observed in Fragile X patient-derived cells.

Budding yeast Rad51: a paradigm for how phosphorylation and intrinsic structural disorder regulate homologous recombination and protein homeostasis

The RecA-family recombinase Rad51 is the central player in homologous recombination (HR), the faithful pathway for repairing DNA double-strand breaks (DSBs) during both mitosis and meiosis. The behavior of Rad51 protein in vivo is fine-tuned via posttranslational modifications conducted by multiple protein kinases in response to cell cycle cues and DNA lesions. Unrepaired DSBs and ssDNA also activate Mec1^ATR and Tel1^ATM family kinases to initiate the DNA damage response (DDR) that safeguards genomic integrity. Defects in HR and DDR trigger genome instability and result in cancer predisposition, infertility, developmental defects, neurological diseases or premature aging. Intriguingly, yeast Mec1^ATR- and Tel1^ATM-dependent phosphorylation promotes Rad51 protein stability during DDR, revealing how Mec1^ATR can alleviate proteotoxic stress. Moreover, Mec1^ATR- and Tel1^ATM-dependent phosphorylation also occurs on DDR-unrelated proteins, suggesting that Mec1^ATR and Tel1^ATM have a DDR-independent function in protein homeostasis. In this minireview, we first describe how human and budding yeast Rad51 are phosphorylated by multiple protein kinases at different positions to promote homology-directed DNA repair and recombination (HDRR). Then, we discuss recent findings showing that intrinsic structural disorder and Mec1^ATR/Tel1^ATM-dependent phosphorylation are coordinated in yeast Rad51 to regulate both HR and protein homeostasis.

NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences

Intrinsically disordered proteins are those proteins with intrinsically disordered regions. One of the unique characteristics of intrinsically disordered proteins is the existence of functional segments in intrinsically dis-ordered regions. These segments are involved in binding to partner molecules, such as protein and DNA, and play important roles in signaling pathways and/or transcriptional regulation. Although there are databases that gather information on such disordered binding regions, data remain limited. Therefore, it is desirable to develop programs to predict the disordered binding regions without using data for the binding regions. We developed a program, NeProc, to predict the disordered binding regions, which can be regarded as intrinsically disordered regions with a structural propensity. We only used data for the structural domains and intrinsically disordered regions to detect such regions. NeProc accepts a query amino acid sequence converted into a position specific score matrix, and uses two neural networks that employ different window sizes, a neural network of short windows, and a neural network of long windows. The performance of NeProc was comparable to that of existing programs of the disordered binding region prediction. This result presents the possibility to overcome the shortage of the disordered binding region data in the development of the prediction programs for these binding regions. NeProc is available at http://flab.neproc.org/neproc/index.html.

Short Disordered Epitope of CRTAM Ig-Like V Domain as a Potential Target for Blocking Antibodies

Class-I Restricted T Cell-Associated Molecule (CRTAM) is a protein that is expressed after T cell activation. The interaction of CRTAM with its ligand, nectin-like 2 (Necl2), is required for the efficient production of IL-17, IL-22, and IFNγ by murine CD4 T cells, and it plays a role in optimal CD8 T and NK cell cytotoxicity. CRTAM promotes the pro-inflammatory cytokine profile; therefore, it may take part in the immunopathology of autoimmune diseases such as diabetes type 1 or colitis. Thus, antibodies that block the interaction between CRTAM and Necl2 would be useful for controlling the production of these inflammatory cytokines. In this work, using bioinformatics predictions, we identified three short disordered epitopes (sDE1-3) that are located in the Ig-like domains of murine CRTAM and are conserved in mammalian species. We performed a structural analysis by molecular dynamics simulations of sDE1 (QHPALKSSKY, Ig-like V), sDE2 (QRNGEKSVVK, Ig-like C1), and sDE3 (CSTERSKKPPPQI, Ig-like C1). sDE1, which is located within a loop of the contact interface of the heterotypic interaction with Nectl2, undergoes an order–disorder transition. On the contrary, even though sDE2 and sDE3 are flexible and also located within loops, they do not undergo order–disorder transitions. We evaluated the immunogenicity of sDE1 and sDE3 through the expression of these epitopes in chimeric L1 virus-like particles. We confirmed that sDE1 induces polyclonal antibodies that recognize the native folding of CRTAM expressed in activated murine CD4 T cells. In contrast, sDE3 induces polyclonal antibodies that recognize the recombinant protein hCRTAM-Fc, but not the native CRTAM. Thus, in this study, an exposed disordered epitope in the Ig-like V domain of CRTAM was identified as a potential site for therapeutic antibodies.

Identification of Small Molecules that Modulate Mutant p53 Condensation

Structural mutants of p53 induce global p53 protein destabilization and misfolding, followed by p53 protein aggregation. First evidence indicates that p53 can be part of protein condensates and that p53 aggregation potentially transitions through a condensate-like state. We show condensate-like states of fluorescently labeled structural mutant p53 in the nucleus of living cancer cells. We furthermore identified small molecule compounds that interact with the p53 protein and lead to dissolution of p53 structural mutant condensates. The same compounds lead to condensation of a fluorescently tagged p53 DNA-binding mutant, indicating that the identified compounds differentially alter p53 condensation behavior depending on the type of p53 mutation. In contrast to p53 aggregation inhibitors, these compounds are active on p53 condensates and do not lead to mutant p53 reactivation. Taken together our study provides evidence for structural mutant p53 condensation in living cells and tools to modulate this process.

ODiNPred: comprehensive prediction of protein order and disorder

Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations

Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein–protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the prevalent class of protein interactions mediated by intrinsically disordered regions (IDRs). Therefore, we investigated the enrichment patterns of missense mutation-causing SNVs that are associated with disease and cancer, as well as those present in the healthy population, in structures of IDR-mediated interactions with comparisons to classical globular interactions. When comparing the different categories of interaction interfaces, division of the interface regions into solvent-exposed rim residues and buried core residues reveal distinctive enrichment patterns for the various types of missense mutations. Most notably, we demonstrate a strong enrichment at the interface core of interacting IDRs in disease mutations and its depletion in neutral ones, which supports the view that the disruption of IDR interactions is a mechanism underlying many diseases. Intriguingly, we also found an asymmetry across the IDR interaction interface in the enrichment of certain missense mutation types, which may hint at an increased variant tolerance and urges further investigations of IDR interactions.

The Biochemistry of Survival Motor Neuron Protein Is Paving the Way to Novel Therapies for Spinal Muscle Atrophy

Spinal muscle atrophy (SMA) is the leading genetic cause of infant mortality. SMA originates from the loss of functional survival motor neuron (SMN) protein. In most SMA cases, the SMN1 gene is deleted. However, in some cases, SMN is mutated, impairing its biological functions. SMN mutants could provide clues about the biological functions of SMN and the specific impact on SMA, potentially leading to the identification of new pathways and thus providing novel treatment alternatives, and even personalized care. Here, we discuss the biochemistry of SMN and the most recent SMA treatment strategies.