Molecular Recognition Features Research Articles

Host-guest-induced electronic state triggers two-electron oxygen reduction electrocatalysis

Supramolecular polymers possess great potential in catalysis owing to their distinctive molecular recognition and dynamic crosslinking features. However, investigating supramolecular electrocatalysts with high efficiency in oxygen reduction reaction to hydrogen peroxide (ORHP) remains an unexplored frontier. Herein, we present organic polymers for ORHP by introducing cyclodextrin-containing noncovalent building blocks, affording these supramolecules with abundant dynamic bonds. The electronic states and reaction kinetics are further well-modulated via a host-guest strategy, resulting in appropriate regional electron binding force and controllable chemical activity. Notably, integrating supramolecular units into phenyl group-containing model covalent polymer achieves a production rate of 9.14 mol g−1cat h−1, with 98.01% Faraday efficiency, surpassing most reported metal-free electrocatalysts. Moreover, the dynamic bonds in supramolecular catalysts can effectively regulate the binding ability of oxygen intermediates, leading to high reactivity and selectivity for the 2e− pathway. Supported by theory calculation and in situ experiment, C atoms (site–1) adjacent to the –C = N (N) group are potential active sites. This work pioneers host-guest strategy and provides inspiring ideas for the ORHP process.

Protein embeddings predict binding residues in disordered regions

The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding residues often err for intrinsically disordered proteins or regions (IDPs/IDPRs), often also referred to as molecular recognition features (MoRFs). Here, we presented a novel machine learning (ML) model trained to specifically predict binding regions in IDPRs. The proposed model, IDBindT5, leveraged embeddings from the protein language model (pLM) ProtT5 to reach a balanced accuracy of 57.2 ± 3.6% (95% confidence interval). Assessed on the same data set, this did not differ at the 95% CI from the state-of-the-art (SOTA) methods ANCHOR2 and DeepDISOBind that rely on expert-crafted features and evolutionary information from multiple sequence alignments (MSAs). Assessed on other data, methods such as SPOT-MoRF reached higher MCCs. IDBindT5’s SOTA predictions are much faster than other methods, easily enabling full-proteome analyses. Our findings emphasize the potential of pLMs as a promising approach for exploring and predicting features of disordered proteins. The model and a comprehensive manual are publicly available at https://github.com/jahnl/binding_in_disorder.

The Rapid Evolution of De Novo Proteins in Structure and Complex.

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model.

Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction networks. As the association between disordered proteins and severe diseases continues to be discovered, identifying MoRFs has gained growing significance. Due to the limited number of experimentally validated MoRFs, the performance of existing MoRF's prediction algorithms is not good enough and still needs to be improved. In this research, we present a model named MoRF_ESM, which utilizes deep-learning protein representations to predict MoRFs in disordered proteins. This approach employs a pretrained ESM-2 protein language model to generate embedding representations of residues in the form of attention map matrices. These representations are combined with a self-learned TextCNN model for feature extraction and prediction. In addition, an averaging step was incorporated at the end of the MoRF_ESM model to refine the output and generate final prediction results. In comparison to other impressive methods on benchmark datasets, the MoRF_ESM approach demonstrates state-of-the-art performance, achieving [Formula: see text] higher AUC than other methods when tested on TEST1 and achieving [Formula: see text] higher AUC than other methods when tested on TEST2. These results imply that the combination of ESM-2 and TextCNN can effectively extract deep evolutionary features related to protein structure and function, along with capturing shallow pattern features located in protein sequences, and is well qualified for the prediction task of MoRFs. Given that ESM-2 is a highly versatile protein language model, the methodology proposed in this study can be readily applied to other tasks involving the classification of protein sequences.

Implications of intrinsic disorder and functional proteomics in the merkel cell polyomavirus life cycle.

Infection with merkel cell polyomavirus (MCPyV) is implicated in the development of merkel cell carcinoma (MCC), a rare but aggressive skin cancer. MCC has a mortality rate near 50%, and incidence has been rapidly increasing in recent decades, making development of improved treatment strategies critical to addressing its growing social burden. The parallel increasing necessity for novel research to better understand MCPyV pathogenesis has prompted numerous studies in recent years, yet the role of intrinsic disorder in MCPyV proteins remains unexplored. This study carries out computational characterization of intrinsic disorder within the MCPyV proteome and suggests mechanisms that may contribute to the oncogenicity of the virus to invade and hijack host immune systems. Our analysis finds that significant levels of intrinsic disorder are present in proteins LT, ALTO, 57kT, and VP1, and suggests that regions of sT may also contain large, disordered regions. The investigation further shows correlation of disorder propensity with the outputs for functional predictors of eukaryotic linear motifs (ELMs), molecular recognition features (MoRFs), and propensity for liquid-liquid phase separation (LLPS). Our findings indicate that MCPyV may use disorder and phase condensation to alter viral function that may accentuate or provide the basis for oncogenic activities. It is intended that this study will inform future experimental validation efforts around the phase separation capacity of MCPyV and its host protein-protein interactions. Furthermore, we hope to inform other investigators on the potential role of disorder in the MCPyV life cycle toward ultimately progressing the development of novel therapeutic agents.

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.

The Conserved Yeast Protein Knr4 Involved in Cell Wall Integrity Is a Multi-domain Intrinsically Disordered Protein

Knr4/Smi1 proteins are specific to the fungal kingdom and their deletion in the model yeast Saccharomyces cerevisiae and the human pathogen Candida albicans results in hypersensitivity to specific antifungal agents and a wide range of parietal stresses. In S. cerevisiae, Knr4 is located at the crossroads of several signalling pathways, including the conserved cell wall integrity and calcineurin pathways. Knr4 interacts genetically and physically with several protein members of those pathways. Its sequence suggests that it contains large intrinsically disordered regions. Here, a combination of small-angle X-ray scattering (SAXS) and crystallographic analysis led to a comprehensive structural view of Knr4. This experimental work unambiguously showed that Knr4 comprises two large intrinsically disordered regions flanking a central globular domain whose structure has been established. The structured domain is itself interrupted by a disordered loop. Using the CRISPR/Cas9 genome editing technique, strains expressing KNR4 genes deleted from different domains were constructed. The N-terminal domain and the loop are essential for optimal resistance to cell wall-binding stressors. The C-terminal disordered domain, on the other hand, acts as a negative regulator of this function of Knr4. The identification of molecular recognition features, the possible presence of secondary structure in these disordered domains and the functional importance of the disordered domains revealed here designate these domains as putative interacting spots with partners in either pathway. Targeting these interacting regions is a promising route to the discovery of inhibitory molecules that could increase the susceptibility of pathogens to the antifungals currently in clinical use.

Prediction of α-MORFS and folding free energies in intrinsically disordered peptides.

Intrinsically disordered proteins (IDPs) and disordered regions (IDRs) of folded proteins play significant roles in cell signaling, regulation, and recognition. However, the lack of well-defined structures challenges our ability to predict the biological role and working mechanisms of IDPs. Computer simulations provide a powerful tool to characterize the conformational ensembles explored by IDPs. Here, we use free energy calculations in atomistic simulations to explore the applicability of binding-induced folding mechanisms for the development of ligands that specifically bind to intrinsically disordered proteins. Molecular recognition features (MoRFs) are functional regions located within longer intrinsically disordered regions that undergo disorder-to-order transitions upon binding their specific interaction partners. A subgroup called α-MoRFs undergoes disordered to α-helix transitions upon binding. In principle, MoRFs can also be utilized as targets for the design of drug molecules that bind to such regions in disease-causing IDPs and render them inactive or modulate their properties. We quantify the free energy cost involved in transitions from intrinsically disordered peptide states into folded secondary structure motifs for variable peptide sequences. We compare α-MoRFs and fully disordered sequences from several known proteins and compare our results to state-of-the-art structure predictions. In contrast to structure prediction tools, our atomistic simulation protocol not only allows us to determine the minimum free energy structure but also quantifies the free energy difference between folded and disordered conformational states. The latter is critical for the development of drugs targeting a specific IDR in a folded conformation because the binding free energy needs to overcompensate the free energy cost of folding. Our general strategy aims to reduce the “undruggability” of IDPs and IDRs, which lack an inherent structure and represent a significant fraction of the proteome.

Structures of the Inhibitory Receptor Siglec-8 in Complex with a High-Affinity Sialoside Analogue and a Therapeutic Antibody.

Human sialic acid binding immunoglobulin-like lectin-8 (Siglec-8) is an inhibitory receptor that triggers eosinophil apoptosis and can inhibit mast cell degranulation when engaged by specific monoclonal antibodies (mAbs) or sialylated ligands. Thus, Siglec-8 has emerged as a critical negative regulator of inflammatory responses in diverse diseases, such as allergic airway inflammation. Herein, we have deciphered the molecular recognition features of the interaction of Siglec-8 with the mAb lirentelimab (2C4, under clinical development) and with a sialoside mimetic with the potential to suppress mast cell degranulation. The three-dimensional structure of Siglec-8 and the fragment antigen binding (Fab) portion of the anti-Siglec-8 mAb 2C4, solved by X-ray crystallography, reveal that 2C4 binds close to the carbohydrate recognition domain (V-type Ig domain) on Siglec-8. We have also deduced the binding mode of a high-affinity analogue of its sialic acid ligand (9-N-napthylsufonimide-Neu5Ac, NSANeuAc) using a combination of NMR spectroscopy and X-ray crystallography. Our results show that the sialoside ring of NSANeuAc binds to the canonical sialyl binding pocket of the Siglec receptor family and that the high affinity arises from the accommodation of the NSA aromatic group in a nearby hydrophobic patch formed by the N-terminal tail and the unique G-G' loop. The results reveal the basis for the observed high affinity of this ligand and provide clues for the rational design of the next generation of Siglec-8 inhibitors. Additionally, the specific interactions between Siglec-8 and the N-linked glycans present on the high-affinity receptor FcεRIα have also been explored by NMR.

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information.

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

Identification and characterization of epicuticular proteins of nematodes sharing motifs with cuticular proteins of arthropods.

Specific collagens and insoluble proteins called cuticlins are major constituents of the nematode cuticles. The epicuticle, which forms the outermost electron-dense layer of the cuticle, is composed of another category of insoluble proteins called epicuticlins. It is distinct from the insoluble cuticlins localized in the cortical layer and the fibrous ribbon underneath lateral alae. Our objective was to identify and characterize genes and their encoded proteins forming the epicuticle. The combination between previously obtained laboratory results and recently made available data through the whole-genome shotgun contigs (WGS) and the transcriptome Shotgun Assembly (TSA) sequencing projects of Ascaris suum allowed us to identify the first epicuticlin gene, Asu-epic-1, on the chromosome VI. This gene is formed of exon1 (55 bp) and exon2 (1067 bp), separated by an intron of 1593 bp. Exon 2 is formed of tandem repeats (TR) whose number varies in different cDNA and genomic clones of Asu-epic-1. These variations could be due to slippage of the polymerases during DNA replication and RNA transcription leading to insertions and deletions (Indels). The deduced protein, Asu-EPIC-1, consists of a signal peptide of 20 amino acids followed by 353 amino acids composed of seven TR of 49 or 51 amino acids each. Three highly conserved tyrosine motifs characterize each repeat. The GYR motif is the Pfam motif PF02756 present in several cuticular proteins of arthropods. Asu-EPIC-1 is an intrinsically disordered protein (IDP) containing seven predicted molecular recognition features (MoRFs). This type of protein undergoes a disorder-to-order transition upon binding protein partners. Three epicuticular sequences have been identified in A. suum, Ascaris lumbricoides, and Toxocara canis. Homologous epicuticular proteins were identified in over 50 other nematode species. The potential of this new category of proteins in forming the nematode cuticle through covalent interactions with other cuticular components, particularly with collagens, is discussed. Their localization in the outermost layer of the nematode body and their unique structure render them crucial candidates for biochemical and molecular interaction studies and targets for new biotechnological and biomedical applications.

Intrinsic Disorder in BAP1 and Its Association with Uveal Melanoma.

Background: Specific subvariants of uveal melanoma (UM) are associated with increased rates of metastasis compared to other subvariants. BRCA1 (BReast CAncer gene 1)-associated protein-1 (BAP1) is encoded by a gene that has been linked to aggressive behavior in UM. Methods: We evaluated BAP1 for the presence of intrinsically disordered protein regions (IDPRs) and its protein–protein interactions (PPI). We evaluated specific sequence-based features of the BAP1 protein using a set of bioinformatic databases, predictors, and algorithms. Results: We show that BAP1’s structure contains extensive IDPRs as it is highly enriched in proline residues (the most disordered amino acid; p-value < 0.05), the average percent of predicted disordered residues (PPDR) was 57.34%, and contains 9 disorder-based binding sites (ie. molecular recognition features (MoRFs)). BAP1’s intrinsic disorder allows it to engage in a complex PPI network with at least 49 partners (p-value < 1.0 × 10−16). Conclusion: These findings show that BAP1 contains IDPRs and an intricate PPI network. Mutations in UM that are associated with the BAP1 gene may alter the function of the IDPRs embedded into its structure. These findings develop the understanding of UM and may provide a target for potential novel therapies to treat this aggressive neoplasm.

Immobilization of Biantennary N-Glycans Leads to Branch Specific Epitope Recognition by LSECtin.

The molecular recognition features of LSECtin toward asymmetric N-glycans have been scrutinized by NMR and compared to those occurring in glycan microarrays. A pair of positional glycan isomers (LDN3 and LDN6), a nonelongated GlcNAc4Man3 N-glycan (G0), and the minimum binding epitope (the GlcNAcβ1-2Man disaccharide) have been used to shed light on the preferred binding modes under both experimental conditions. Strikingly, both asymmetric LDN3 and LDN6 N-glycans are recognized by LSECtin with similar affinities in solution, in sharp contrast to the results obtained when those glycans are presented on microarrays, where only LDN6 was efficiently recognized by the lectin. Thus, different results can be obtained using different experimental approaches, pointing out the tremendous difficulty of translating in vitro results to the in vivo environment.

Investigating the disordered regions (MoRFs, SLiMs and LCRs) and functions of mimicry proteins/peptides in silico

Microbial mimicry of the host proteins/peptides can elicit host auto-reactive T- or B-cells resulting in autoimmune disease(s). Since intrinsically disordered protein regions (IDPRs) are involved in several host cell signaling and PPI networks, molecular mimicry of the IDPRs can help the pathogens in substituting their own proteins in the host cell-signaling and PPI networks and, ultimately hijacking the host cellular machinery. Thus, the present study was conducted to discern the structural disorder and intrinsically disordered protein regions (IDPRs) like, molecular recognition features (MoRFs), short linear motifs (SLiMs), and low complexity regions (LCRs) in the experimentally verified mimicry proteins and peptides (mimitopes) of bacteria, viruses and host. Also, functional characteristics of the mimicry proteins were studied in silico. Our results indicated that 78% of the bacterial host mimicry proteins and 45% of the bacterial host mimitopes were moderately/highly disordered while, 73% of the viral host mimicry proteins and 31% of the viral host mimitopes were moderately/highly disordered. Among the pathogens, 27% of the bacterial mimicry proteins and 13% of the bacterial mimitopes were moderately/highly disordered while, 53% of the viral mimicry proteins and 21% of the viral mimitopes were moderately/highly disordered. Though IDPR were frequent in host, bacterial and viral mimicry proteins, only a few mimitopes overlapped with the IDPRs like, MoRFs, SLiMs and LCRs. This suggests that most of the microbes cannot use molecular mimicry to modulate the host PPIs and hijack the host cell machinery. Functional analyses indicated that most of the pathogens exhibited mimicry with the host proteins involved in ion binding and signaling pathways. This is the first report on the disordered regions and functional aspects of experimentally proven host and microbial mimicry proteins.

Intrinsic disorder in proteins associated with oxidative stress-induced JNK signaling.

The c-Jun N-terminal kinase (JNK) signaling cascade is a mitogen-activated protein kinase (MAPK) signaling pathway that can be activated in response to a wide range of environmental stimuli. Based on the type, degree, and duration of the stimulus, the JNK signaling cascade dictates the fate of the cell by influencing gene expression through its substrate transcription factors. Oxidative stress is a result of a disturbance in the pro-oxidant/antioxidant homeostasis of the cell and is associated with a large number of diseases, such as neurodegenerative disorders, cancer, diabetes, cardiovascular diseases, and disorders of the immune system, where it activates the JNK signaling pathway. Among different biological roles ascribed to the intrinsically disordered proteins (IDPs) and hybrid proteins containing ordered domains and intrinsically disordered protein regions (IDPRs) are signaling hub functions, as intrinsic disorder allows proteins to undertake multiple interactions, each with a different consequence. In order to ensure precise signaling, the cellular abundance of IDPs is highly regulated, and mutations or changes in abundance of IDPs/IDPRs are often associated with disease. In this study, we have used a combination of six disorder predictors to evaluate the presence of intrinsic disorder in proteins of the oxidative stress-induced JNK signaling cascade, and as per our findings, none of the 18 proteins involved in this pathway are ordered. The highest level of intrinsic disorder was observed in the scaffold proteins, JIP1, JIP2, JIP3; dual specificity phosphatases, MKP5, MKP7; 14-3-3ζ and transcription factor c-Jun. The MAP3Ks, MAP2Ks, MAPKs, TRAFs, and thioredoxin were the proteins that were predicted to be moderately disordered. Furthermore, to characterize the predicted IDPs/IDPRs in the proteins of the JNK signaling cascade, we identified the molecular recognition features (MoRFs), posttranslational modification (PTM) sites, and short linear motifs (SLiMs) associated with the disordered regions. These findings will serve as a foundation for experimental characterization of disordered regions in these proteins, which represents a crucial step for a better understanding of the roles of IDPRs in diseases associated with this important pathway.

MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.

Intrinsically disordered regions (IDRs) without stable structure are important for protein structures and functions. Some IDRs can be combined with molecular fragments to make itself completed the transition from disordered to ordered, which are called molecular recognition features (MoRFs). There are five main functions of MoRFs: molecular recognition assembler (MoR_assembler), molecular recognition chaperone (MoR_chaperone), molecular recognition display sites (MoR_display_sites), molecular recognition effector (MoR_effector), and molecular recognition scavenger (MoR_scavenger). Researches on functions of molecular recognition features are important for pharmaceutical and disease pathogenesis. However, the existing computational methods can only predict the MoRFs in proteins, failing to distinguish their different functions. In this paper, we treat MoRF function prediction as a multi-label learning task and solve it with the Binary Relevance (BR) strategy. Finally, we use Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as basic models to construct MoRF-FUNCpred through ensemble learning. Experimental results show that MoRF-FUNCpred performs well for MoRF function prediction. To the best knowledge of ours, MoRF-FUNCpred is the first predictor for predicting the functions of MoRFs. Availability and Implementation: The stand alone package of MoRF-FUNCpred can be accessed from https://github.com/LiangYu-Xidian/MoRF-FUNCpred.

Transactivation domain of Adenovirus Early Region 1A (E1A): Investigating folding dynamics and aggregation.

Transactivation domain of Adenovirus Early region 1A (E1A) oncoprotein is an intrinsically disordered molecular hub protein. It is involved in binding to different domains of human cell transcriptional co-activators such as retinoblastoma (pRb), CREB-binding protein (CBP), and its paralogue p300. The conserved region 1 (TAD) of E1A is known to undergo structural transitions and folds upon interaction with transcriptional adaptor zinc finger 2 (TAZ2). Previous reports on Taz2-E1A studies have suggested the formation of helical conformations of E1A-TAD. However, the folding behavior of the TAD region in isolation has not been studied in detail. Here, we have elucidated the folding behavior of E1A peptide at varied temperatures and solution conditions. Further, we have studied the effects of macromolecular crowding on E1A-TAD peptide. Additionally, we have also predicted the molecular recognition features of E1A using MoRF predictors. The predicted MoRFs are consistent with its structural transitions observed during TAZ2 interactions for transcriptional regulation in literature. Also, as a general rule of MoRFs, E1A undergoes helical transitions in alcohol and osmolyte solution. Finally, we studied the aggregation behavior of E1A, where we observed that the E1A could form amyloid-like aggregates that are cytotoxic to mammalian cells.

Role of “dual-personality” fragments in HEV adaptation—analysis of Y-domain region

BackgroundHepatitis E is a liver disease caused by the pathogen hepatitis E virus (HEV). The largest polyprotein open reading frame 1 (ORF1) contains a nonstructural Y-domain region (YDR) whose activity in HEV adaptation remains uncharted. The specific role of disordered regions in several nonstructural proteins has been demonstrated to participate in the multiplication and multiple regulatory functions of the viruses. Thus, intrinsic disorder of YDR including its structural and functional annotation was comprehensively studied by exploiting computational methodologies to delineate its role in viral adaptation. ResultsBased on our findings, it was evident that YDR contains significantly higher levels of ordered regions with less prevalence of disordered residues. Sequence-based analysis of YDR revealed it as a “dual personality” (DP) protein due to the presence of both structured and unstructured (intrinsically disordered) regions. The evolution of YDR was shaped by pressures that lead towards predominance of both disordered and regularly folded amino acids (Ala, Arg, Gly, Ile, Leu, Phe, Pro, Ser, Tyr, Val). Additionally, the predominance of characteristic DP residues (Thr, Arg, Gly, and Pro) further showed the order as well as disorder characteristic possessed by YDR. The intrinsic disorder propensity analysis of YDR revealed it as a moderately disordered protein. All the YDR sequences consisted of molecular recognition features (MoRFs), i.e., intrinsic disorder-based protein–protein interaction (PPI) sites, in addition to several nucleotide-binding sites. Thus, the presence of molecular recognition (PPI, RNA binding, and DNA binding) signifies the YDR’s interaction with specific partners, host membranes leading to further viral infection. The presence of various disordered-based phosphorylation sites further signifies the role of YDR in various biological processes. Furthermore, functional annotation of YDR revealed it as a multifunctional-associated protein, due to its susceptibility in binding to a wide range of ligands and involvement in various catalytic activities. ConclusionsAs DP are targets for regulation, thus, YDR contributes to cellular signaling processes through PPIs. As YDR is incompletely understood, therefore, our data on disorder-based function could help in better understanding its associated functions. Collectively, our novel data from this comprehensive investigation is the first attempt to delineate YDR role in the regulation and pathogenesis of HEV.

Subunit cooperation in the Get1/2 receptor promotes tail-anchored membrane protein insertion.

The guided entry of tail-anchored protein (GET) pathway, in which the Get3 ATPase delivers an essential class of tail-anchored membrane proteins (TAs) to the Get1/2 receptor at the endoplasmic reticulum, provides a conserved mechanism for TA biogenesis in eukaryotic cells. The membrane-associated events of this pathway remain poorly understood. Here we show that complex assembly between the cytosolic domains (CDs) of Get1 and Get2 strongly enhances the affinity of the individual subunits for Get3•TA, thus enabling efficient capture of the targeting complex. In addition to the known role of Get1CD in remodeling Get3 conformation, two molecular recognition features (MoRFs) in Get2CD induce Get3 opening, and both subunits are required for optimal TA release from Get3. Mutation of the MoRFs attenuates TA insertion into the ER in vivo. Our results demonstrate extensive cooperation between the Get1/2 receptor subunits in the capture and remodeling of the targeting complex, and emphasize the role of MoRFs in receptor function during membrane protein biogenesis.

Prediction of MoRFs based on sequence properties and convolutional neural networks

BackgroundIntrinsically disordered proteins possess flexible 3-D structures, which makes them play an important role in a variety of biological functions. Molecular recognition features (MoRFs) act as an important type of functional regions, which are located within longer intrinsically disordered regions and undergo disorder-to-order transitions upon binding their interaction partners.ResultsWe develop a method, MoRFCNN, to predict MoRFs based on sequence properties and convolutional neural networks (CNNs). The sequence properties contain structural and physicochemical properties which are used to describe the differences between MoRFs and non-MoRFs. Especially, to highlight the correlation between the target residue and adjacent residues, three windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict MoRFs through the constructed CNN. Comparing with other existing methods, MoRFCNN obtains better performance.ConclusionsMoRFCNN is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRFCNN is effective and competitive.