MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously adapt upon binding with specific interaction partners and/or certain alterations in environmental conditions. This malleability is foundational for their role as tunable interaction hubs in core cellular processes such as signaling, transcription, and translation. Tracing the conformational ensemble of an IDP/IDR and its perturbation in response to regulatory cues is thus paramount for illuminating its function. However, the conformational heterogeneity of IDP/IDRs poses several challenges. Here, we review experimental and computational methods devised to disentangle the conformational landscape of IDP/IDRs, highlighting recent computational advances that permit proteome-wide scans of IDP/IDRs conformations. We briefly evaluate selected computational methods using the disordered N-terminal of the human copper transporter 1 as a test case and outline further challenges in IDP/IDRs ensemble prediction.

Intrinsic disorder and salt-dependent conformational changes of the N-terminal region of TFIP11 splicing factor

Tuftelin Interacting Protein 11 (TFIP11) was identified as a critical human spliceosome assembly regulator, interacting with multiple proteins and localising in membrane-less organelles. However, a lack of structural information on TFIP11 limits the rationalisation of its biological role. TFIP11 is predicted as an intrinsically disordered protein (IDP), and more specifically concerning its N-terminal (N-TER) region. IDPs lack a defined tertiary structure, existing as a dynamic conformational ensemble, favouring protein-protein and protein-RNA interactions. IDPs are involved in liquid-liquid phase separation (LLPS), driving the formation of subnuclear compartments. Combining disorder prediction, molecular dynamics, and spectroscopy methods, this contribution shows the first evidence TFIP11 N-TER is a polyampholytic IDP, exhibiting a structural duality with the coexistence of ordered and disordered assemblies, depending on the ionic strength. Increasing the salt concentration enhances the protein conformational flexibility, presenting a more globule-like shape, and a fuzzier unstructured arrangement that could favour LLPS and protein-RNA interaction. The most charged and hydrophilic regions are the most impacted, including the G-Patch domain essential to TFIP11 function. This study gives a better understanding of the salt-dependent conformational behaviour of the N-TER TFIP11, supporting the hypothesis of the formation of different types of protein assembly, in line with its multiple biological roles.

MYC phase separation selectively modulates the transcriptome

Dysregulation and enhanced expression of MYC transcription factors (TFs) including MYC and MYCN contribute to the majority of human cancers. For example, MYCN is amplified up to several hundredfold in high-risk neuroblastoma. The resulting overexpression of N-myc aberrantly activates genes that are not activated at low N-myc levels and drives cell proliferation. Whether increasing N-myc levels simply mediates binding to lower-affinity binding sites in the genome or fundamentally changes the activation process remains unclear. One such activation mechanism that could become important above threshold levels of N-myc is the formation of aberrant transcriptional condensates through phase separation. Phase separation has recently been linked to transcriptional regulation, but the extent to which it contributes to gene activation remains an open question. Here we characterized the phase behavior of N-myc and showed that it can form dynamic condensates that have transcriptional hallmarks. We tested the role of phase separation in N-myc-regulated transcription by using a chemogenetic tool that allowed us to compare non-phase-separated and phase-separated conditions at equivalent N-myc levels, both of which showed a strong impact on gene expression compared to no N-myc expression. Interestingly, we discovered that only a small percentage (<3%) of N-myc-regulated genes is further modulated by phase separation but that these events include the activation of key oncogenes and the repression of tumor suppressors. Indeed, phase separation increases cell proliferation, corroborating the biological effects of the transcriptional changes. However, our results also show that >97% of N-myc-regulated genes are not affected by N-myc phase separation, demonstrating that soluble complexes of TFs with the transcriptional machinery are sufficient to activate transcription.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Cell-specific polymerization-driven biomolecular condensate formation fine-tunes root tissue morphogenesis

Formation of biomolecular condensates can be driven by weak multivalent interactions and emergent polymerization. However, the mechanism of polymerization-mediated condensate formation is less studied. We found lateral root cap cell (LRC)-specific SUPPRESSOR OF RPS4-RLD1 (SRFR1) condensates fine-tune primary root development. Polymerization of the SRFR1 N-terminal domain is required for both LRC condensate formation and optimal root growth. Surprisingly, the first intrinsically disordered region (IDR1) of SRFR1 can be functionally substituted by a specific group of intrinsically disordered proteins known as dehydrins. This finding facilitated the identification of functional segments in the IDR1 of SRFR1, a generalizable strategy to decode unknown IDRs. With this functional information we further improved root growth by modifying the SRFR1 condensation module, providing a strategy to improve plant growth and resilience.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Experimental methods to study the structure and dynamics of intrinsically disordered regions in proteins

Eukaryotic proteins often feature long stretches of amino acids that lack a well-defined three-dimensional structure and are referred to as intrinsically disordered proteins (IDPs) or regions (IDRs). Although these proteins challenge conventional structure-function paradigms, they play vital roles in cellular processes. Recent progress in experimental techniques, such as NMR spectroscopy, single molecule FRET, high speed AFM and SAXS, have provided valuable insights into the biophysical basis of IDP function. This review discusses the advancements made in these techniques particularly for the study of disordered regions in proteins. In NMR spectroscopy new strategies such as 13C detection, non-uniform sampling, segmental isotope labeling, and rapid data acquisition methods address the challenges posed by spectral overcrowding and low stability of IDPs. The importance of various NMR parameters, including chemical shifts, hydrogen exchange rates, and relaxation measurements, to reveal transient secondary structures within IDRs and IDPs are presented. Given the high flexibility of IDPs, the review outlines NMR methods for assessing their dynamics at both fast (ps-ns) and slow (μs-ms) timescales. IDPs exert their functions through interactions with other molecules such as proteins, DNA, or RNA. NMR-based titration experiments yield insights into the thermodynamics and kinetics of these interactions. Detailed study of IDPs requires multiple experimental techniques, and thus, several methods are described for studying disordered proteins, highlighting their respective advantages and limitations. The potential for integrating these complementary techniques, each offering unique perspectives, is explored to achieve a comprehensive understanding of IDPs.

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2

Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges.

Shift of the insoluble content of the proteome in the aging mouse brain

During aging, the cellular response to unfolded proteins is believed to decline, resulting in diminished proteostasis. In model organisms, such as Caenorhabditis elegans, proteostatic decline with age has been linked to proteome solubility shifts and the onset of protein aggregation. However, this correlation has not been extensively characterized in aging mammals. To uncover age-dependent changes in the insoluble portion of a mammalian proteome, we analyzed the detergent-insoluble fraction of mouse brain tissue by mass spectrometry. We identified a group of 171 proteins, including the small heat shock protein α-crystallin, that become enriched in the detergent-insoluble fraction obtained from old mice. To enhance our ability to detect features associated with proteins in that fraction, we complemented our data with a meta-analysis of studies reporting the detergent-insoluble proteins in various mouse models of aging and neurodegeneration. Strikingly, insoluble proteins from young and old mice are distinct in several features in our study and across the collected literature data. In younger mice, proteins are more likely to be disordered, part of membraneless organelles, and involved in RNA binding. These traits become less prominent with age, as an increased number of structured proteins enter the pellet fraction. This analysis suggests that age-related changes to proteome organization lead a group of proteins with specific features to become detergent-insoluble. Importantly, these features are not consistent with those associated with proteins driving membraneless organelle formation. We see no evidence in our system of a general increase of condensate proteins in the detergent-insoluble fraction with age.

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.

Comprehensive analysis of liquid-liquid phase separation propensities of HSV-1 proteins and their interaction with host factors

In recent years, it has been shown that the liquid-liquid phase separation (LLPS) of virus proteins plays a crucial role in their life cycle. It promotes the formation of viral replication organelles, concentrating viral components for efficient replication and facilitates the assembly of viral particles. LLPS has emerged as a crucial process in the replication and assembly of herpes simplex virus-1 (HSV-1). Recent studies have identified several HSV-1 proteins involved in LLPS, including the myristylated tegument protein UL11 and infected cell protein 4; however, a complete proteome-level understanding of the LLPS-prone HSV-1 proteins is not available. We provide a comprehensive analysis of the HSV-1 proteome and explore the potential of its proteins to undergo LLPS. By integrating sequence analysis, prediction algorithms and an array of tools and servers, we identified 10 HSV-1 proteins that exhibit high LLPS potential. By analysing the amino acid sequences of the LLPS-prone proteins, we identified specific sequence motifs and enriched amino acid residues commonly found in LLPS-prone regions. Our findings reveal a diverse range of LLPS-prone proteins within the HSV-1, which are involved in critical viral processes such as replication, transcriptional regulation and assembly of viral particles. This suggests that LLPS might play a crucial role in facilitating the formation of specialized viral replication compartments and the assembly of HSV-1 virion. The identification of LLPS-prone proteins in HSV-1 opens up new avenues for understanding the molecular mechanisms underlying viral pathogenesis. Our work provides valuable insights into the LLPS landscape of HSV-1, highlighting potential targets for further experimental validation and enhancing our understanding of viral replication and pathogenesis.

Mechanosensitive ion channels MSL8, MSL9, and MSL10 have environmentally sensitive intrinsically disordered regions with distinct biophysical characteristics in vitro

Intrinsically disordered protein regions (IDRs) are highly dynamic sequences that rapidly sample a collection of conformations over time. In the past several decades, IDRs have emerged as a major component of many proteomes, comprising ~30% of all eukaryotic protein sequences. Proteins with IDRs function in a wide range of biological pathways and are notably enriched in signaling cascades that respond to environmental stresses. Here, we identify and characterize intrinsic disorder in the soluble cytoplasmic N-terminal domains of MSL8, MSL9, and MSL10, three members of the MscS-like (MSL) family of mechanosensitive ion channels. In plants, MSL channels are proposed to mediate cell and organelle osmotic homeostasis. Bioinformatic tools unanimously predicted that the cytosolic N-termini of MSL channels are intrinsically disordered. We examined the N-terminus of MSL10 (MSL10N) as an exemplar of these IDRs and circular dichroism spectroscopy confirms its disorder. MSL10N adopted a predominately helical structure when exposed to the helix-inducing compound trifluoroethanol (TFE). Furthermore, in the presence of molecular crowding agents, MSL10N underwent structural changes and exhibited alterations to its homotypic interaction favorability. Lastly, interrogations of collective behavior via in vitro imaging of condensates indicated that MSL8N, MSL9N, and MSL10N have sharply differing propensities for self-assembly into condensates, both inherently and in response to salt, temperature, and molecular crowding. Taken together, these data establish the N-termini of MSL channels as intrinsically disordered regions with distinct biophysical properties and the potential to respond uniquely to changes in their physiochemical environment.

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.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

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.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

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/.

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

DMFpred: Predicting protein disorder molecular functions based on protein cubic language model

Intrinsically disordered proteins and regions (IDP/IDRs) are widespread in living organisms and perform various essential molecular functions. These functions are summarized as six general categories, including entropic chain, assembler, scavenger, effector, display site, and chaperone. The alteration of IDP functions is responsible for many human diseases. Therefore, identifying the function of disordered proteins is helpful for the studies of drug target discovery and rational drug design. Experimental identification of the molecular functions of IDP in the wet lab is an expensive and laborious procedure that is not applicable on a large scale. Some computational methods have been proposed and mainly focus on predicting the entropic chain function of IDRs, while the computational predictive methods for the remaining five important categories of disordered molecular functions are desired. Motivated by the growing numbers of experimental annotated functional sequences and the need to expand the coverage of disordered protein function predictors, we proposed DMFpred for disordered molecular functions prediction, covering disordered assembler, scavenger, effector, display site and chaperone. DMFpred employs the Protein Cubic Language Model (PCLM), which incorporates three protein language models for characterizing sequences, structural and functional features of proteins, and attention-based alignment for understanding the relationship among three captured features and generating a joint representation of proteins. The PCLM was pre-trained with large-scaled IDR sequences and fine-tuned with functional annotation sequences for molecular function prediction. The predictive performance evaluation on five categories of functional and multi-functional residues suggested that DMFpred provides high-quality predictions. The web-server of DMFpred can be freely accessed from http://bliulab.net/DMFpred/.

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.

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.

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.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions

Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non-cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition reduces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N- and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered regions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Structural characterisation of amyloidogenic intrinsically disordered zinc finger protein isoforms DPF3b and DPF3a

Double PHD fingers 3 (DPF3) is a zinc finger protein, found in the BAF chromatin remodelling complex, and is involved in the regulation of gene expression. Two DPF3 isoforms have been identified, respectively named DPF3b and DPF3a. Very limited structural information is available for these isoforms, and their specific functionality still remains poorly studied. In a previous work, we have demonstrated the first evidence of DPF3a being a disordered protein sensitive to amyloid fibrillation. Intrinsically disordered proteins (IDPs) lack a defined tertiary structure, existing as a dynamic conformational ensemble, allowing them to act as hubs in protein-protein interaction networks. In the present study, we have more thoroughly characterised DPF3a in vitro behaviour, as well as unravelled and compared the structural properties of the DPF3b isoform, using an array of predictors and biophysical techniques. Predictions, spectroscopy, and dynamic light scattering have revealed a high content in disorder: prevalence of random coil, aromatic residues partially to fully exposed to the solvent, and large hydrodynamic diameters. DPF3a appears to be more disordered than DPF3b, and exhibits more expanded conformations. Furthermore, we have shown that they both time-dependently aggregate into amyloid fibrils, as revealed by typical circular dichroism, deep-blue autofluorescence, and amyloid-dye binding assay fingerprints. Although spectroscopic and microscopic analyses have unveiled that they share a similar aggregation pathway, DPF3a fibrillates at a faster rate, likely through reordering of its C-terminal domain.

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

BACKGROUND

Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences.

RESULTS

We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA.

CONCLUSIONS

Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

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.

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.

Intrinsically disordered proteins play diverse roles in cell signaling

Signaling pathways allow cells to detect and respond to a wide variety of chemical (e.g. Ca2+ or chemokine proteins) and physical stimuli (e.g., sheer stress, light). Together, these pathways form an extensive communication network that regulates basic cell activities and coordinates the function of multiple cells or tissues. The process of cell signaling imposes many demands on the proteins that comprise these pathways, including the abilities to form active and inactive states, and to engage in multiple protein interactions. Furthermore, successful signaling often requires amplifying the signal, regulating or tuning the response to the signal, combining information sourced from multiple pathways, all while ensuring fidelity of the process. This sensitivity, adaptability, and tunability are possible, in part, due to the inclusion of intrinsically disordered regions in many proteins involved in cell signaling. The goal of this collection is to highlight the many roles of intrinsic disorder in cell signaling. Following an overview of resources that can be used to study intrinsically disordered proteins, this review highlights the critical role of intrinsically disordered proteins for signaling in widely diverse organisms (animals, plants, bacteria, fungi), in every category of cell signaling pathway (autocrine, juxtacrine, intracrine, paracrine, and endocrine) and at each stage (ligand, receptor, transducer, effector, terminator) in the cell signaling process. Thus, a cell signaling pathway cannot be fully described without understanding how intrinsically disordered protein regions contribute to its function. The ubiquitous presence of intrinsic disorder in different stages of diverse cell signaling pathways suggest that more mechanisms by which disorder modulates intra- and inter-cell signals remain to be discovered.

Experimental Evidence of Intrinsic Disorder and Amyloid Formation by the Henipavirus W Proteins

Henipaviruses are severe human pathogens within the Paramyxoviridae family. Beyond the P protein, the Henipavirus P gene also encodes the V and W proteins which share with P their N-terminal, intrinsically disordered domain (NTD) and possess a unique C-terminal domain. Henipavirus W proteins antagonize interferon (IFN) signaling through NTD-mediated binding to STAT1 and STAT4, and prevent type I IFN expression and production of chemokines. Structural and molecular information on Henipavirus W proteins is lacking. By combining various bioinformatic approaches, we herein show that the Henipaviruses W proteins are predicted to be prevalently disordered and yet to contain short order-prone segments. Using limited proteolysis, differential scanning fluorimetry, analytical size exclusion chromatography, far-UV circular dichroism and small-angle X-ray scattering, we experimentally confirmed their overall disordered nature. In addition, using Congo red and Thioflavin T binding assays and negative-staining transmission electron microscopy, we show that the W proteins phase separate to form amyloid-like fibrils. The present study provides an additional example, among the few reported so far, of a viral protein forming amyloid-like fibrils, therefore significantly contributing to enlarge our currently limited knowledge of viral amyloids. In light of the critical role of the Henipavirus W proteins in evading the host innate immune response and of the functional role of phase separation in biology, these studies provide a conceptual asset to further investigate the functional impact of the phase separation abilities of the W proteins.

Predicting Protein Conformational Disorder and Disordered Binding Sites

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

DisoLipPred: accurate prediction of disordered lipid-binding residues in protein sequences with deep recurrent networks and transfer learning

MOTIVATION

Intrinsically disordered protein regions interact with proteins, nucleic acids and lipids. Regions that bind lipids are implicated in a wide spectrum of cellular functions and several human diseases. Motivated by the growing amount of experimental data for these interactions and lack of tools that can predict them from the protein sequence, we develop DisoLipPred, the first predictor of the disordered lipid-binding residues (DLBRs).

RESULTS

DisoLipPred relies on a deep bidirectional recurrent network that implements three innovative features: transfer learning, bypass module that sidesteps predictions for putative structured residues, and expanded inputs that cover physiochemical properties associated with the protein-lipid interactions. Ablation analysis shows that these features drive predictive quality of DisoLipPred. Tests on an independent test dataset and the yeast proteome reveal that DisoLipPred generates accurate results and that none of the related existing tools can be used to indirectly identify DLBR. We also show that DisoLipPred's predictions complement the results generated by predictors of the transmembrane regions. Altogether, we conclude that DisoLipPred provides high-quality predictions of DLBRs that complement the currently available methods.

AVAILABILITY AND IMPLEMENTATION

DisoLipPred's webserver is available at http://biomine.cs.vcu.edu/servers/DisoLipPred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.

Evolutionary Dynamics of Indels in SARS-CoV-2 Spike Glycoprotein

SARS-CoV-2, responsible for the current COVID-19 pandemic that claimed over 5.0 million lives, belongs to a class of enveloped viruses that undergo quick evolutionary adjustments under selection pressure. Numerous variants have emerged in SARS-CoV-2, posing a serious challenge to the global vaccination effort and COVID-19 management. The evolutionary dynamics of this virus are only beginning to be explored. In this work, we have analysed 1.79 million spike glycoprotein sequences of SARS-CoV-2 and found that the virus is fine-tuning the spike with numerous amino acid insertions and deletions (indels). Indels seem to have a selective advantage as the proportions of sequences with indels steadily increased over time, currently at over 89%, with similar trends across countries/variants. There were as many as 420 unique indel positions and 447 unique combinations of indels. Despite their high frequency, indels resulted in only minimal alteration of N-glycosylation sites, including both gain and loss. As indels and point mutations are positively correlated and sequences with indels have significantly more point mutations, they have implications in the evolutionary dynamics of the SARS-CoV-2 spike glycoprotein.

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.

MedProDB: A database of Mediator proteins

•

Mediator complex is a key component of transcriptional regulation in eukaryotes.

•

Identification of Mediator subunits was done by using computational approaches.

•

Different physicochemical properties, and functions of Mediators were discussed.

•

We have developed first database of Mediator proteins e.g. MedProDB.

•

MedProDB contains different types of search and browse options, and various tools.

In the last three decades, the multi-subunit Mediator complex has emerged as the key component of transcriptional regulation of eukaryotic gene expression. Although there were initial hiccups, recent advancements in bioinformatics tools contributed significantly to in-silico prediction and characterization of Mediator subunits from several organisms belonging to different eukaryotic kingdoms. In this study, we have developed the first database of Mediator proteins named MedProDB with 33,971 Mediator protein entries. Out of those, 12531, 11545, and 9895 sequences belong to metazoans, plants, and fungi, respectively. Apart from the core information consisting of sequence, length, position, organism, molecular weight, and taxonomic lineage, additional information of each Mediator sequence like aromaticity, hydropathy, instability index, isoelectric point, functions, interactions, repeat regions, diseases, sequence alignment to Mediator subunit family, Intrinsically Disordered Regions (IDRs), Post-translation modifications (PTMs), and Molecular Recognition Features (MoRFs) may be of high utility to the users. Furthermore, different types of search and browse options with four different tools namely BLAST, Smith-Waterman Align, IUPred, and MoRF-Chibi_Light are provided at MedProDB to perform different types of analysis. Being a critical component of the transcriptional machinery and regulating almost all the aspects of transcription, it generated lots of interest in structural and functional studies of Mediator functioning. So, we think that the MedProDB database will be very useful for researchers studying the process of transcription. This database is freely available at www.nipgr.ac.in/MedProDB.

Prediction of MoRFs based on sequence properties and convolutional neural networks

Background

Intrinsically 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.

Results

We develop a method, MoRF_CNN, 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, MoRF_CNN obtains better performance.

Conclusions

MoRF_CNN is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRF_CNN is effective and competitive.

flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.

PRISMA and BioID disclose a motifs-based interactome of the intrinsically disordered transcription factor C/EBPα

C/EBPα represents a paradigm intrinsically disordered transcription factor containing short linear motifs and post-translational modifications (PTM). Unraveling C/EBPα protein interaction networks is a prerequisite for understanding the multi-modal functions of C/EBPα in hematopoiesis and leukemia. Here, we combined arrayed peptide matrix screening (PRISMA) with BioID to generate an in vivo validated and isoform specific interaction map of C/EBPα. The myeloid C/EBPα interactome comprises promiscuous and PTM-regulated interactions with protein machineries involved in gene expression, epigenetics, genome organization, DNA replication, RNA processing, and nuclear transport. C/EBPα interaction hotspots coincide with homologous conserved regions of the C/EBP family that also score as molecular recognition features. PTMs alter the interaction spectrum of C/EBP-motifs to configure a multi-valent transcription factor hub that interacts with multiple co-regulatory components, including BAF/SWI-SNF or Mediator complexes. Combining PRISMA and BioID is a powerful strategy to systematically explore the PTM-regulated interactomes of intrinsically disordered transcription factors.

Analysis of the dark proteome of Chandipura virus reveals maximum propensity for intrinsic disorder in phosphoprotein

Chandipura virus (CHPV, a member of the Rhabdoviridae family) is an emerging pathogen that causes rapidly progressing influenza-like illness and acute encephalitis often leading to coma and death of the human host. Given several CHPV outbreaks in Indian sub-continent, recurring sporadic cases, neurological manifestation, and high mortality rate of this infection, CHPV is gaining global attention. The 'dark proteome' includes the whole proteome with special emphasis on intrinsically disordered proteins (IDP) and IDP regions (IDPR), which are proteins or protein regions that lack unique (or ordered) three-dimensional structures within the cellular milieu. These proteins/regions, however, play a number of vital roles in various biological processes, such as cell cycle regulation, control of signaling pathways, etc. and, therefore, are implicated in many human diseases. IDPs and IPPRs are also abundantly found in many viral proteins enabling their multifunctional roles in the viral life cycles and their capability to highjack various host systems. The unknown abundance of IDP and IDPR in CHPV, therefore, prompted us to analyze the dark proteome of this virus. Our analysis revealed a varying degree of disorder in all five CHPV proteins, with the maximum level of intrinsic disorder propensity being found in Phosphoprotein (P). We have also shown the flexibility of P protein using extensive molecular dynamics simulations up to 500 ns (ns). Furthermore, our analysis also showed the abundant presence of the disorder-based binding regions (also known as molecular recognition features, MoRFs) in CHPV proteins. The identification of IDPs/IDPRs in CHPV proteins suggests that their disordered regions may function as potential interacting domains and may also serve as novel targets for disorder-based drug designs.

Notable sequence homology of the ORF10 protein introspects the architecture of SARS-CoV-2

The current Coronavirus Disease 19 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) shows similar pathology to MERS and SARS-CoV, with a current estimated fatality rate of 1.4%. Open reading frame 10 (ORF10) is a unique SARS-CoV-2 accessory protein, which contains eleven cytotoxic T lymphocyte (CTL) epitopes each of nine amino acids in length. Twenty-two unique SARS-CoV-2 ORF10 variants have been identified based on missense mutations found in sequence databases. Some of these mutations are predicted to decrease the stability of ORF10 in silico physicochemical and structural comparative analyses were carried out on SARS-CoV-2 and Pangolin-CoV ORF10 proteins, which share 97.37% amino acid (aa) homology. Though there is a high degree of ORF10 protein similarity of SARS-CoV-2 and Pangolin-CoV, there are differences of these two ORF10 proteins related to their sub-structure (loop/coil region), solubility, antigenicity and shift from strand to coil at aa position 26 (tyrosine). SARS-CoV-2 ORF10, which is apparently expressed in vivo since reactive T cell clones are found in convalescent patients should be monitored for changes which could correlate with the pathogenesis of COVID-19.

Computational and experimental characterization of the novel ECM glycoprotein SNED1 and prediction of its interactome

The extracellular matrix (ECM) is a complex meshwork of proteins and an essential component of multicellular life. We have recently reported the characterization of a novel ECM protein, SNED1, and showed that it promotes breast cancer metastasis and regulates craniofacial development. However, the mechanisms by which it does so remain unknown. ECM proteins exert their functions by binding to cell surface receptors and interacting with other ECM proteins, actions that we can predict using knowledge of protein's sequence, structure, and post-translational modifications. Here, we combined in-silico and in-vitro approaches to characterize the physico-chemical properties of SNED1 and infer its putative functions. To do so, we established a mammalian cell system to produce and purify SNED1 and its N-terminal fragment, which contains a NIDO domain, and demonstrated experimentally SNED1's potential to be glycosylated, phosphorylated, and incorporated into an insoluble ECM. We also determined the secondary and tertiary structures of SNED1 and its N-terminal fragment and obtained a model for its NIDO domain. Using computational predictions, we identified 114 proteins as putative SNED1 interactors, including the ECM protein fibronectin. Pathway analysis of the predicted SNED1 interactome further revealed that it may contribute to signaling through cell surface receptors, such as integrins, and participate in the regulation of ECM organization and developmental processes. Last, using fluorescence microscopy, we showed that SNED1 forms microfibrils within the ECM and partially colocalizes with fibronectin. Altogether, we provide a wealth of information on an understudied yet important ECM protein with the potential to decipher its pathophysiological functions.

Identification of a Putative DNA-Binding Protein in Arabidopsis That Acts as a Susceptibility Hub and Interacts With Multiple Pseudomonas syringae Effectors

Phytopathogens use secreted effector proteins to suppress host immunity and promote pathogen virulence, and there is increasing evidence that the host-pathogen interactome comprises a complex network. To identify novel interactors of the Pseudomonas syringae effector HopZ1a, we performed a yeast two-hybrid screen that identified a previously uncharacterized Arabidopsis protein that we designate HopZ1a interactor 1 (ZIN1). Additional analyses in yeast and in planta revealed that ZIN1 also interacts with several other P. syringae effectors. We show that an Arabidopsis loss-of-function zin1 mutant is less susceptible to infection by certain strains of P. syringae, while overexpression of ZIN1 results in enhanced susceptibility. Functionally, ZIN1 exhibits topoisomerase-like activity in vitro. Transcriptional profiling of wild-type and zin1 Arabidopsis plants inoculated with P. syringae indicated that while ZIN1 regulates a wide range of pathogen-responsive biological processes, the list of genes more highly expressed in zin1 versus wild-type plants is particularly enriched for ribosomal protein genes. Altogether, these data illuminate ZIN1 as a potential susceptibility hub that interacts with multiple effectors to influence the outcome of plant-microbe interactions.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Protein feature analysis of heat shock induced ubiquitination sites reveals preferential modification site localization

Protein aggregation is indicative of failing protein quality control systems. These systems are responsible for the refolding or degradation of aberrant and misfolded proteins. Heat stress can cause proteins to misfold, triggering cellular responses including a marked increase in the ubiquitination of proteins. This response has been characterized in yeast, however more studies are needed within mammalian cells. Herein, we examine proteins that become ubiquitinated during heat shock in human tissue culture cells using diGly enrichment coupled with mass spectrometry. A majority of these proteins are localized in the nucleus or cytosol. Proteins which are conjugated under stress display longer sequence lengths, more interaction partners, and more hydrophobic patches than controls but do not show lower melting temperatures. Furthermore, heat-induced conjugation sites occur less frequently in disordered regions and are closer to hydrophobic patches than other ubiquitination sites; perhaps providing novel insight into the molecular mechanism mediating this response. Nuclear and cytosolic pools of modified proteins appear to have different protein features. Using a pulse-SILAC approach, we found that both long-lived and newly-synthesized proteins are conjugated under stress. Modified long-lived proteins are predominately nuclear and were distinct from newly-synthesized proteins, indicating that different pathways may mediate the heat-induced increase of polyubiquitination. SIGNIFICANCE: The maintenance of protein homeostasis requires a balance of protein synthesis, folding, and degradation. Under stress conditions, the cell must rapidly adapt by increasing its folding capacity to eliminate aberrant proteins. A major pathway for proteolysis is mediated by the ubiquitin proteasome system. While increased ubiquitination after heat stress was observed over 30 years ago, it remains unclear which proteins are conjugated during heat shock in mammalian cells and by what means this conjugation occurs. In this study, we combined SILAC-based mass spectrometry with computational analyses to reveal features associated to proteins ubiquitinated while under heat shock. Interestingly, we found that conjugation sites induced by the stress are less often located within disordered regions and more often located near hydrophobic patches. Our study showcases how proteomics can reveal distinct feature associated to a cohort of proteins that are modified post translationally and how the ubiquitin conjugation sites are preferably selected in these conditions. Our work opens a new path for delineating the molecular mechanisms leading to the heat stress response and the regulation of protein homeostasis.