DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein

DNA-protein interactions exert the fundamental structure of many pivotal biological processes, such as DNA replication, transcription, and gene regulation. However, accurate and efficient computational methods for identifying these interactions are still lacking. In this study, we propose a method ESM-DBP through refining the DNA-binding protein sequence repertory and domain-adaptive pretraining based the general protein language model. Our method considers the lacking exploration of general language model for DNA-binding protein domain-specific knowledge, so we screen out 170,264 DNA-binding protein sequences to construct the domain-adaptive language model. Experimental results on four downstream tasks show that ESM-DBP provides a better feature representation of DNA-binding protein compared to the original language model, resulting in improved prediction performance and outperforming the state-of-the-art methods. Moreover, ESM-DBP can still perform well even for those sequences with only a few homologous sequences. ChIP-seq on two predicted cases further support the validity of the proposed method.

Unraveling the assembly mechanism of SADS-CoV virus nucleocapsid protein: insights from RNA binding, dimerization, and epitope diversity profiling

The swine acute diarrhea syndrome coronavirus (SADS-CoV) has caused significant disruptions in porcine breeding and raised concerns about potential human infection. The nucleocapsid (N) protein of SADS-CoV plays a vital role in viral assembly and replication, but its structure and functions remain poorly understood. This study utilized biochemistry, X-ray crystallography, and immunization techniques to investigate the N protein's structure and function in SADS-CoV. Our findings revealed distinct domains within the N protein, including an RNA-binding domain, two disordered domains, and a dimerization domain. Through biochemical assays, we confirmed that the N-terminal domain functions as an RNA-binding domain, and the C-terminal domain is involved in dimerization, with the crystal structure analysis providing visual evidence of dimer formation. Immunization experiments demonstrated that the disordered domain 2 elicited a significant antibody response. These identified domains and their interactions are crucial for viral assembly. This comprehensive understanding of the N protein in SADS-CoV enhances our knowledge of its assembly and replication mechanisms, enabling the development of targeted interventions and therapeutic strategies.

IMPORTANCE

SADS-CoV is a porcine coronavirus that originated from a bat HKU2-related coronavirus. It causes devastating swine diseases and poses a high risk of spillover to humans. The coronavirus N protein, as the most abundant viral protein in infected cells, likely plays a key role in viral assembly and replication. However, the structure and function of this protein remain unclear. Therefore, this study employed a combination of biochemistry and X-ray crystallography to uncover distinct structural domains in the N protein, including RNA-binding domains, two disordered domains, and dimerization domains. Additionally, we made the novel discovery that the disordered domain elicited a significant antibody response. These findings provide new insights into the structure and functions of the SADS-CoV N protein, which have important implications for future studies on SADS-CoV diagnosis, as well as the development of vaccines and anti-viral drugs.

In situ analysis of osmolyte mechanisms of proteome thermal stabilization

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

Mycobacteriophage Alexphander Gene 94 Encodes an Essential dsDNA-Binding Protein during Lytic Infection

Mycobacteriophages are viruses that specifically infect bacterial species within the genera Mycobacterium and Mycolicibacterium. Over 2400 mycobacteriophages have been isolated on the host Mycolicibacterium smegmatis and sequenced. This wealth of genomic data indicates that mycobacteriophage genomes are diverse, mosaic, and contain numerous (35-60%) genes for which there is no predicted function based on sequence similarity to characterized orthologs, many of which are essential to lytic growth. To fully understand the molecular aspects of mycobacteriophage-host interactions, it is paramount to investigate the function of these genes and gene products. Here we show that the temperate mycobacteriophage, Alexphander, makes stable lysogens with a frequency of 2.8%. Alexphander gene 94 is essential for lytic infection and encodes a protein predicted to contain a C-terminal MerR family helix-turn-helix DNA-binding motif (HTH) and an N-terminal DinB/YfiT motif, a putative metal-binding motif found in stress-inducible gene products. Full-length and C-terminal gp94 constructs form high-order nucleoprotein complexes on 100-500 base pair double-stranded DNA fragments and full-length phage genomic DNA with little sequence discrimination for the DNA fragments tested. Maximum gene 94 mRNA levels are observed late in the lytic growth cycle, and gene 94 is transcribed in a message with neighboring genes 92 through 96. We hypothesize that gp94 is an essential DNA-binding protein for Alexphander during lytic growth. We proposed that gp94 forms multiprotein complexes on DNA through cooperative interactions involving its HTH DNA-binding motif at sites throughout the phage chromosome, facilitating essential DNA transactions required for lytic propagation.

Structure-guided discovery of highly efficient cytidine deaminases with sequence-context independence

The applicability of cytosine base editors is hindered by their dependence on sequence context and by off-target effects. Here, by using AlphaFold2 to predict the three-dimensional structure of 1,483 cytidine deaminases and by experimentally characterizing representative deaminases (selected from each structural cluster after categorizing them via partitional clustering), we report the discovery of a few deaminases with high editing efficiencies, diverse editing windows and increased ratios of on-target to off-target effects. Specifically, several deaminases induced C-to-T conversions with comparable efficiency at AC/TC/CC/GC sites, the deaminases could introduce stop codons in single-copy and multi-copy genes in mammalian cells without double-strand breaks, and some residue conversions at predicted DNA-interacting sites reduced off-target effects. Structure-based generative machine learning could be further leveraged to expand the applicability of base editors in gene therapies.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins

Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .

Genome-scale annotation of protein binding sites via language model and geometric deep learning

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Systematic identification of 20S proteasome substrates

For years, proteasomal degradation was predominantly attributed to the ubiquitin-26S proteasome pathway. However, it is now evident that the core 20S proteasome can independently target proteins for degradation. With approximately half of the cellular proteasomes comprising free 20S complexes, this degradation mechanism is not rare. Identifying 20S-specific substrates is challenging due to the dual-targeting of some proteins to either 20S or 26S proteasomes and the non-specificity of proteasome inhibitors. Consequently, knowledge of 20S proteasome substrates relies on limited hypothesis-driven studies. To comprehensively explore 20S proteasome substrates, we employed advanced mass spectrometry, along with biochemical and cellular analyses. This systematic approach revealed hundreds of 20S proteasome substrates, including proteins undergoing specific N- or C-terminal cleavage, possibly for regulation. Notably, these substrates were enriched in RNA- and DNA-binding proteins with intrinsically disordered regions, often found in the nucleus and stress granules. Under cellular stress, we observed reduced proteolytic activity in oxidized proteasomes, with oxidized protein substrates exhibiting higher structural disorder compared to unmodified proteins. Overall, our study illuminates the nature of 20S substrates, offering crucial insights into 20S proteasome biology.

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.

Computational studies on rep and capsid proteins of CRESS DNA viruses

The circular rep-encoding single-stranded DNA viruses (CRESS DNA viruses) are among the smallest, with 2-6 kb ssDNA genomes that encode for a coat protein (C) and a replication protein (R). To comprehend the complexity and divergence of the C and R proteins, we have created predictive structural models of representative viruses infecting unique hosts from each family using the neural network-based method AlphaFold2 and carried out molecular dynamic simulations to assess their stability. The structural characteristics indicate that differences in loops and amino-terminus may play a significant role in facilitating adaptations to multiple hosts and vectors. In comparison to the C, the Rs show a high degree of conservation and structural mimicry of the nuclease-helicase domains of plasmids. A phylogenetic analysis based on the structures and sequences of the C and R proteins reveals evolutionary variances. Our study also highlights the conservation of structural components involved in the interaction of R with the conserved intergenic region of the genome. Further, we envisage that the adaptability of R's central linker may be crucial for establishing interactions with multiple protein partners, including C.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00858-x.

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.

Regulation of micro- and small-exon retention and other splicing processes by GRP20 for flower development

Pre-mRNA splicing is crucial for gene expression and depends on the spliceosome and splicing factors. Plant exons have an average size of ~180 nucleotides and typically contain motifs for interactions with spliceosome and splicing factors. Micro exons (<51 nucleotides) are found widely in eukaryotes and in genes for plant development and environmental responses. However, little is known about transcript-specific regulation of splicing in plants and about the regulators for micro exon splicing. Here we report that glycine-rich protein 20 (GRP20) is an RNA-binding protein and required for splicing of ~2,100 genes including those functioning in flower development and/or environmental responses. Specifically, GRP20 is required for micro-exon retention in transcripts of floral homeotic genes; these micro exons are conserved across angiosperms. GRP20 is also important for small-exon (51-100 nucleotides) splicing. In addition, GRP20 is required for flower development. Furthermore, GRP20 binds to poly-purine motifs in micro and small exons and a spliceosome component; both RNA binding and spliceosome interaction are important for flower development and micro-exon retention. Our results provide new insights into the mechanisms of micro-exon retention in flower development.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

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.

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures

The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacking known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breakthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA- and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The network was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common network to acquire common binding characteristics. Finally, these characteristics were input into two fully connected layers to predict binding sites with DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmark datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for inferring nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, and trained models are available at https://github.com/biomed-AI/nucleic-acid-binding.

Dynamically regulated two-site interaction of viral RNA to capture host translation initiation factor

Many RNA viruses employ internal ribosome entry sites (IRESs) in their genomic RNA to commandeer the host's translational machinery for replication. The IRES from encephalomyocarditis virus (EMCV) interacts with eukaryotic translation initiation factor 4 G (eIF4G), recruiting the ribosomal subunit for translation. Here, we analyze the three-dimensional structure of the complex composed of EMCV IRES, the HEAT1 domain fragment of eIF4G, and eIF4A, by cryo-electron microscopy. Two distinct eIF4G-interacting domains on the IRES are identified, and complex formation changes the angle therebetween. Further, we explore the dynamics of these domains by using solution NMR spectroscopy, revealing conformational equilibria in the microsecond to millisecond timescale. In the lowly-populated conformations, the base-pairing register of one domain is shifted with the structural transition of the three-way junction, as in the complex structure. Our study provides insights into the viral RNA's sophisticated strategy for optimal docking to hijack the host protein.

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning

Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.

Staphylococcus aureus Prophage-Encoded Protein Causes Abortive Infection and Provides Population Immunity against Kayviruses

Both temperate and obligately lytic phages have crucial roles in the biology of staphylococci. While superinfection exclusion among closely related temperate phages is a well-characterized phenomenon, the interactions between temperate and lytic phages in staphylococci are not understood. Here, we present a resistance mechanism toward lytic phages of the genus Kayvirus, mediated by the membrane-anchored protein designated Pdp_Sau encoded by Staphylococcus aureus prophages, mostly of the Sa2 integrase type. The prophage accessory gene pdp_Sau is strongly linked to the lytic genes for holin and ami2-type amidase and typically replaces genes for the toxin Panton-Valentine leukocidin (PVL). The predicted Pdp_Sau protein structure shows the presence of a membrane-binding α-helix in its N-terminal part and a cytoplasmic positively charged C terminus. We demonstrated that the mechanism of action of Pdp_Sau does not prevent the infecting kayvirus from adsorbing onto the host cell and delivering its genome into the cell, but phage DNA replication is halted. Changes in the cell membrane polarity and permeability were observed from 10 min after the infection, which led to prophage-activated cell death. Furthermore, we describe a mechanism of overcoming this resistance in a host-range Kayvirus mutant, which was selected on an S. aureus strain harboring prophage 53 encoding Pdp_Sau, and in which a chimeric gene product emerged via adaptive laboratory evolution. This first case of staphylococcal interfamily phage-phage competition is analogous to some other abortive infection defense systems and to systems based on membrane-destructive proteins. IMPORTANCE Prophages play an important role in virulence, pathogenesis, and host preference, as well as in horizontal gene transfer in staphylococci. In contrast, broad-host-range lytic staphylococcal kayviruses lyse most S. aureus strains, and scientists worldwide have come to believe that the use of such phages will be successful for treating and preventing bacterial diseases. The effectiveness of phage therapy is complicated by bacterial resistance, whose mechanisms related to therapeutic staphylococcal phages are not understood in detail. In this work, we describe a resistance mechanism targeting kayviruses that is encoded by a prophage. We conclude that the defense mechanism belongs to a broader group of abortive infections, which is characterized by suicidal behavior of infected cells that are unable to produce phage progeny, thus ensuring the survival of the host population. Since the majority of staphylococcal strains are lysogenic, our findings are relevant for the advancement of phage therapy.

iDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning

Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficiently identify DBPs or RBPs. However, the information used in these methods is slightly unitary, and most of them only can predict DBPs or RBPs. In this study, we proposed a computational predictor iDRBP-EL to identify DNA- and RNA- binding proteins, and introduced hierarchical ensemble learning to integrate three level information. The method can integrate the information of different features, machine learning algorithms and data into one multi-label model. The ablation experiment showed that the fusion of different information can improve the prediction performance and overcome the cross-prediction problem. Experimental results on the independent datasets showed that iDRBP-EL outperformed all the other competing methods. Moreover, we established a user-friendly webserver iDRBP-EL (http://bliulab.net/iDRBP-EL), which can predict both DBPs and RBPs only based on protein sequences.

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures

Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains challenging. In this study, we introduce an easy-to-use web server BindWeb for ligand-specific and ligand-general binding residue and pocket prediction from protein structures. BindWeb integrates a graph neural network GraphBind with a hybrid convolutional neural network and bidirectional long short-term memory network DELIA to identify binding residues. Furthermore, BindWeb clusters the predicted binding residues to binding pockets with mean shift clustering. The experimental results and case study demonstrate that BindWeb benefits from the complementarity of two base methods. BindWeb is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/BindWeb/.

To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks

MOTIVATION

The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes. The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules. However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases. As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecule types.

RESULTS

In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite. By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods. To my best knowledge, this is the first method using multi-task framework to predict multiple molecular binding sites simultaneously.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Genome Sequence of a Microvirus Recovered from Wastewater in Arizona, USA, in October 2020, Encodes a Previously Undescribed DNA-Binding Protein

We describe the genome of Microvirus-AZ-2020, which was identified from wastewater in Arizona, USA, in October 2020. Microvirus-AZ-2020 belongs to subfamily Gokushovirinae and contains six (five known and one hypothetical) open reading frames (ORFs), each with >40 codons. HHPred analysis and Colabfold structure prediction suggest that the hypothetical ORF encodes a previously undescribed putative DNA-binding protein.

New Insights into Phase Separation Processes and Membraneless Condensates of EIN2

Recent technological advances allow us to resolve molecular processes in living cells with high spatial and temporal resolution. Based on these technological advances, membraneless intracellular condensates formed by reversible functional aggregation and phase separation have been identified as important regulatory modules in diverse biological processes. Here, we present bioinformatic and cellular studies highlighting the possibility of the involvement of the central activator of ethylene responses EIN2 in such cellular condensates and phase separation processes. Our work provides insight into the molecular type (identity) of the observed EIN2 condensates and on potential intrinsic elements and sequence motifs in EIN2-C that may regulate condensate formation and dynamics.

DNA- and RNA-Binding Proteins Linked Transcriptional Control and Alternative Splicing Together in a Two-Layer Regulatory Network System of Chronic Myeloid Leukemia

DNA- and RNA-binding proteins (DRBPs) typically possess multiple functions to bind both DNA and RNA and regulate gene expression from more than one level. They are controllers for post-transcriptional processes, such as splicing, polyadenylation, transportation, translation, and degradation of RNA transcripts in eukaryotic organisms, as well as regulators on the transcriptional level. Although DRBPs are reported to play critical roles in various developmental processes and diseases, it is still unclear how they work with DNAs and RNAs simultaneously and regulate genes at the transcriptional and post-transcriptional levels. To investigate the functional mechanism of DRBPs, we collected data from a variety of databases and literature and identified 118 DRBPs, which function as both transcription factors (TFs) and splicing factors (SFs), thus called DRBP-SF. Extensive investigations were conducted on four DRBP-SFs that were highly expressed in chronic myeloid leukemia (CML), heterogeneous nuclear ribonucleoprotein K (HNRNPK), heterogeneous nuclear ribonucleoprotein L (HNRNPL), non-POU domain–containing octamer–binding protein (NONO), and TAR DNA-binding protein 43 (TARDBP). By integrating and analyzing ChIP-seq, CLIP-seq, RNA-seq, and shRNA-seq data in K562 using binding and expression target analysis and Statistical Utility for RBP Functions, we discovered a two-layer regulatory network system centered on these four DRBP-SFs and proposed three possible regulatory models where DRBP-SFs can connect transcriptional and alternative splicing regulatory networks cooperatively in CML. The exploration of the identified DRBP-SFs provides new ideas for studying DRBP and regulatory networks, holding promise for further mechanistic discoveries of the two-layer gene regulatory system that may play critical roles in the occurrence and development of CML.

IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation

DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some proteins (DRBPs) bind to both DNA and RNA, also play a key role in gene expression. Identification of DBPs, RBPs and DRBPs is important to study protein-nucleic acid interactions. Computational methods are increasingly being proposed to automatically identify DNA- or RNA-binding proteins based only on protein sequences. One challenge is to design an effective protein representation method to convert protein sequences into fixed-dimension feature vectors. In this study, we proposed a novel protein representation method called Position-Specific Scoring Matrix (PSSM) and Position-Specific Frequency Matrix (PSFM) Cross Transformation (PPCT) to represent protein sequences. This method contains the evolutionary information in PSSM and PSFM, and their correlations. A new computational predictor called IDRBP-PPCT was proposed by combining PPCT and the two-layer framework based on the random forest algorithm to identify DBPs, RBPs and DRBPs. The experimental results on the independent dataset and the tomato genome proved the effectiveness of the proposed method. A user-friendly web-server of IDRBP-PPCT was constructed, which is freely available at http://bliulab.net/IDRBP-PPCT.

Multi-task learning to leverage partially annotated data for PPI interface prediction

Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations—with data extension—reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein’s functional properties of interest are only partially annotated.

Light Regulates Acinetobacter baumannii Chromosomal and pAB3 Plasmid Genes at 37°C

The opportunistic pathogen A. baumannii has a remarkable capacity to persist in the hospital environment and cause devastating human infections. This capacity can be attributed partly to the sensing and regulatory systems that enable this pathogen to modify its physiology based on environmental cues. One of the signals that A. baumannii senses and responds to is light through the sensing and regulatory roles of the BlsA photoreceptor protein in cells cultured at temperatures below 30°C. This report presents evidence that a light stimulon is operational at 37°C, a condition at which the BlsA production and activity are drastically impaired. Global transcriptional analysis showed that the 37°C light stimulon includes the differential expression of chromosomal genes encoding a wide range of functions that are known to be involved in the adaptation to different metabolic conditions, as well as virulence and persistence in the host and the medical environment. Unexpectedly, the 37°C light stimulon also includes the differential expression of conjugation functions encoded by pAB3 plasmid genes. Our work further demonstrates that the TetR1 and H-NS regulators encoded by this conjugative plasmid control the expression of H₂O₂ resistance and surface motility, respectively. Furthermore, our data showed that pAB3 has an overall negative effect on the expression of these phenotypes and plays no significant virulence role. Although the nature of the bacterial factors and the mechanisms by which the regulation is attained at 37°C remain unknown, taken together, our work expands the current knowledge about light sensing and gene regulation in A. baumannii. IMPORTANCE As a facultative pathogen, Acinetobacter baumannii persists in various environments by sensing different environmental cues, including light. This report provides evidence of light-dependent regulation at 37°C of the expression of genes coding for a wide range of functions, including those involved in the conjugation of the pAB3 plasmid. Although this plasmid affects the expression of virulence traits when tested under laboratory conditions, it does not have a significant impact when tested using ex vivo and in vivo experimental models. These findings provide a better understanding of the interplay between light regulation and plasmid persistence in the pathobiology of A. baumannii.

PreRBP-TL: prediction of species-specific RNA-binding proteins based on transfer learning

MOTIVATION

RNA-binding proteins (RBPs) play crucial roles in post-transcriptional regulation. Accurate identification of RBPs helps to understand gene expression, regulation, etc. In recent years, some computational methods were proposed to identify RBPs. However, these methods fail to accurately identify RBPs from some specific species with limited data, such as bacteria.

RESULTS

In this study, we introduce a computational method called PreRBP-TL for identifying species-specific RBPs based on transfer learning. The weights of the prediction model were initialized by pretraining with the large general RBP dataset and then fine-tuned with the small species-specific RPB dataset by using transfer learning. The experimental results show that the PreRBP-TL achieves better performance for identifying the species-specific RBPs from Human, Arabidopsis, Escherichia coli and Salmonella, outperforming eight state-of-the-art computational methods. It is anticipated PreRBP-TL will become a useful method for identifying RBPs.

AVAILABILITY AND IMPLEMENTATION

For the convenience of researchers to identify RBPs, the web server of PreRBP-TL was established, freely available at http://bliulab.net/PreRBP-TL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nuclear translocation of vitellogenin in the honey bee (Apis mellifera)

Vitellogenin (Vg) is a conserved protein used by nearly all oviparous animals to produce eggs. It is also pleiotropic and performs functions in oxidative stress resistance, immunity, and, in honey bees, behavioral development of the worker caste. It has remained enigmatic how Vg affects multiple traits. Here, we asked whether Vg enters the nucleus and acts via DNA-binding. We used cell fractionation, immunohistology, and cell culture to show that a structural subunit of honey bee Vg translocates into cell nuclei. We then demonstrated Vg-DNA binding theoretically and empirically with prediction software and chromatin immunoprecipitation with sequencing (ChIP-seq), finding binding sites at genes influencing immunity and behavior. Finally, we investigated the immunological and enzymatic conditions affecting Vg cleavage and nuclear translocation and constructed a 3D structural model. Our data are the first to show Vg in the nucleus and suggest a new fundamental regulatory role for this ubiquitous protein.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13592-022-00914-9.

Kinesin Motors in the Filamentous Basidiomycetes in Light of the Schizophyllum commune Genome

Kinesins are essential motor molecules of the microtubule cytoskeleton. All eukaryotic organisms have several genes encoding kinesin proteins, which are necessary for various cell biological functions. During the vegetative growth of filamentous basidiomycetes, the apical cells of long leading hyphae have microtubules extending toward the tip. The reciprocal exchange and migration of nuclei between haploid hyphae at mating is also dependent on cytoskeletal structures, including the microtubules and their motor molecules. In dikaryotic hyphae, resulting from a compatible mating, the nuclear location, synchronous nuclear division, and extensive nuclear separation at telophase are microtubule-dependent processes that involve unidentified molecular motors. The genome of Schizophyllum commune is analyzed as an example of a species belonging to the Basidiomycota subclass, Agaricomycetes. In this subclass, the investigation of cell biology is restricted to a few species. Instead, the whole genome sequences of several species are now available. The analyses of the mating type genes and the genes necessary for fruiting body formation or wood degrading enzymes in several genomes of Agaricomycetes have shown that they are controlled by comparable systems. This supports the idea that the genes regulating the cell biological process in a model fungus, such as the genes encoding kinesin motor molecules, are also functional in other filamentous Agaricomycetes.

A Hypothesis on How the Azolla Symbiosis Mitigates Nitrous Oxide Based on In Silico Analyses

Nitrous oxide is a long-lived greenhouse gas that exists for 114 years in the atmosphere and is 298-fold more potent than carbon dioxide in its global warming potential. Two recent studies showcased the utility of Azolla plants for a lesser footprint in nitrous oxide production from urea and other supplements to the irrigated ecosystem, which mandates exploration since there is still no clear solution to nitrous oxide in paddy fields or in other ecosystems. Here, we propose a solution based on the evolution of a single cytochrome oxidase subunit II protein (WP_013192178.1) from the cyanobiont Trichormus azollae that we hypothesize to be able to quench nitrous oxide. First, we draw attention to a domain in the candidate protein that is emerging as a sensory periplasmic Y_Y_Y domain that is inferred to bind nitrous oxide. Secondly, we draw the phylogeny of the candidate protein showcasing the poor bootstrap support of its position in the wider clade showcasing its deviation from the core function. Thirdly, we show that the NtcA protein, the apical N-effecting transcription factor, can putatively bind to a promoter sequence of the gene coding for the candidate protein (WP_013192178.1), suggesting a function associated with heterocysts and N-metabolism. Our fourth point involves a string of histidines at the C-terminal extremity of the WP_013192178.1 protein that is missing on all other T. azollae cytochrome oxidase subunit II counterparts, suggesting that such histidines are perhaps involved in forming a Cu center. As the fifth point, we showcase a unique glycine-183 in a lengthy linker region containing multiple glycines that is absent in all proximal Nostocales cyanobacteria, which we predict to be a DNA binding residue. We propose a mechanism of action for the WP_013192178.1 protein based on our in silico analyses. In total, we hypothesize the incomplete and rapid conversion of a likely heterocystous cytochrome oxidase subunit II protein to an emerging nitrous oxide sensing/quenching subunit based on bioinformatics analyses and past literature, which can have repercussions to climate change and consequently, future human life.

Domesticated LTR-Retrotransposon gag-Related Gene (Gagr) as a Member of the Stress Response Network in Drosophila

The most important sources of new components of genomes are transposable elements, which can occupy more than half of the nucleotide sequence of the genome in higher eukaryotes. Among the mobile components of a genome, a special place is occupied by retroelements, which are similar to retroviruses in terms of their mechanisms of integration into a host genome. The process of positive selection of certain sequences of transposable elements and retroviruses in a host genome is commonly called molecular domestication. There are many examples of evolutionary adaptations of gag (retroviral capsid) sequences as new regulatory sequences of different genes in mammals, where domesticated gag genes take part in placenta functioning and embryogenesis, regulation of apoptosis, hematopoiesis, and metabolism. The only gag-related gene has been found in the Drosophila genome—Gagr. According to the large-scale transcriptomic and proteomic analysis data, the Gagr gene in D. melanogaster is a component of the protein complex involved in the stress response. In this work, we consider the evolutionary processes that led to the formation of a new function of the domesticated gag gene and its adaptation to participation in the stress response. We discuss the possible functional role of the Gagr as part of the complex with its partners in Drosophila, and the pathway of evolution of proteins of the complex in eukaryotes to determine the benefit of the domesticated retroelement gag gene.

Intermolecular interactions drive protein adaptive and co-adaptive evolution at both species and population levels

Proteins are the building blocks for almost all the functions in cells. Understanding the molecular evolution of proteins and the forces that shape protein evolution is essential in understanding the basis of function and evolution. Previous studies have shown that adaptation frequently occurs at the protein surface, such as in genes involved in host–pathogen interactions. However, it remains unclear whether adaptive sites are distributed randomly or at regions associated with particular structural or functional characteristics across the genome, since many proteins lack structural or functional annotations. Here, we seek to tackle this question by combining large-scale bioinformatic prediction, structural analysis, phylogenetic inference, and population genomic analysis of Drosophila protein-coding genes. We found that protein sequence adaptation is more relevant to function-related rather than structure-related properties. Interestingly, intermolecular interactions contribute significantly to protein adaptation. We further showed that intermolecular interactions, such as physical interactions, may play a role in the coadaptation of fast-adaptive proteins. We found that strongly differentiated amino acids across geographic regions in protein-coding genes are mostly adaptive, which may contribute to the long-term adaptive evolution. This strongly indicates that a number of adaptive sites tend to be repeatedly mutated and selected throughout evolution in the past, present, and maybe future. Our results highlight the important roles of intermolecular interactions and coadaptation in the adaptive evolution of proteins both at the species and population levels.

Role of "dual-personality" fragments in HEV adaptation-analysis of Y-domain region

BACKGROUND

Hepatitis 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.

RESULTS

Based 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.

CONCLUSIONS

As 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.

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains

Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT's predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http://protdomain.princeton.edu/dsprint.

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory

DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical research and real world application. Existing sequence-based DBP predictors can accurately identify DBPs but incorrectly predict many RBPs as DBPs, and vice versa, resulting in low prediction precision. Moreover, some proteins (DRBPs) interacting with both DNA and RNA play important roles in gene expression and cannot be identified by existing computational methods. In this study, a two-level predictor named DeepDRBP-2L was proposed by combining Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM). It is the first computational method that is able to identify DBPs, RBPs and DRBPs. Rigorous cross-validations and independent tests showed that DeepDRBP-2L is able to overcome the shortcoming of the existing methods and can go one further step to identify DRBPs. Application of DeepDRBP-2L to tomato genome further demonstrated its performance. The webserver of DeepDRBP-2L is freely available at http://bliulab.net/DeepDRBP-2L.

Norovirus VPg Binds RNA through a Conserved N-Terminal K/R Basic Patch

The viral protein genome-linked (VPg) of noroviruses is a multi-functional protein that participates in essential roles during the viral replication cycle. Predictive analyses indicate that murine norovirus (MNV) VPg contains a disordered N-terminal region with RNA binding potential. VPg proteins were expressed with an N-terminal spidroin fusion protein in insect cells and the interaction with RNA investigated by electrophoretic mobility shift assays (EMSA) against a series of RNA probes (pentaprobes) representing all possible five nucleotide combinations. MNV VPg and human norovirus (HuNV) VPg proteins were directly bound to RNA in a non-specific manner. To identify amino acids involved in binding to RNA, all basic (K/R) residues in the first 12 amino acids of MNV VPg were mutated to alanine. Removal of the K/R amino acids eliminated RNA binding and is consistent with a K/R basic patch RNA binding motif within the disordered N-terminal region of norovirus VPgs. Finally, we show that mutation of the K/R basic patch required for RNA binding eliminates the ability of MNV VPg to induce a G0/G1 cell cycle arrest.

YAP, CTGF and Cyr61 are overexpressed in tamoxifen-resistant breast cancer and induce transcriptional repression of ERα

About 70% of breast cancers overexpress estrogen receptor α (ERα, encoded by ESR1). Tamoxifen, a competitive inhibitor of estrogen that binds to ER, has been widely used as a treatment for ER-positive breast cancer. However, 20-30% of breast cancer is resistant to tamoxifen treatment. The mechanisms underlying tamoxifen resistance remain elusive. We found that Yes-associated protein (YAP; also known as YAP1), connective tissue growth factor (CTGF; also known as CCN2) and cysteine-rich angiogenic inducer 61 (Cyr61; also known as CCN1) are overexpressed, while ERα is downregulated in tamoxifen-resistant breast cancer. Inhibition of YAP, CTGF and Cyr61 restored ERα expression and increased sensitivity to tamoxifen. Overexpression of YAP, CTGF, and Cyr61 led to downregulation of ERα and conferred resistance to tamoxifen in ER-positive breast cancer cells. Mechanistically, CTGF and Cyr61 downregulated ERα expression at the transcriptional level by directly binding to the regulatory regions of the ERα-encoding gene, leading to increased tamoxifen resistance. Also, CTGF induced Glut3 (also known as SLC2A3) expression, leading to increased glycolysis, which enhanced cell proliferation and migration in tamoxifen-resistant cells. Together, these results demonstrate a novel role of YAP, CTGF and Cyr61 in tamoxifen resistance and provide a molecular basis for their function in tamoxifen-resistant breast cancer.

A localized adaptor protein performs distinct functions at the Caulobacter cell poles

Asymmetric cell division yields two distinct daughter cells by mechanisms that underlie stem cell behavior and cellular diversity in all organisms. The bacterium Caulobacter crescentus is able to orchestrate this complex process with less than 4,000 genes. This article describes a strategy deployed by Caulobacter where a regulatory protein, PopA, is programed to perform distinct roles based on its subcellular address. We demonstrate that, depending on the availability of a second messenger molecule, PopA adopts either a monomer or dimer form. The two oligomeric forms interact with different partners at the two cell poles, playing a critical role in the degradation of a master transcription factor at one pole and flagellar assembly at the other pole.

Asymmetric cell division generates two daughter cells with distinct characteristics and fates. Positioning different regulatory and signaling proteins at the opposing ends of the predivisional cell produces molecularly distinct daughter cells. Here, we report a strategy deployed by the asymmetrically dividing bacterium Caulobacter crescentus where a regulatory protein is programmed to perform distinct functions at the opposing cell poles. We find that the CtrA proteolysis adaptor protein PopA assumes distinct oligomeric states at the two cell poles through asymmetrically distributed c-di-GMP: dimeric at the stalked pole and monomeric at the swarmer pole. Different polar organizing proteins at each cell pole recruit PopA where it interacts with and mediates the function of two molecular machines: the ClpXP degradation machinery at the stalked pole and the flagellar basal body at the swarmer pole. We discovered a binding partner of PopA at the swarmer cell pole that together with PopA regulates the length of the flagella filament. Our work demonstrates how a second messenger provides spatiotemporal cues to change the physical behavior of an effector protein, thereby facilitating asymmetry.

Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses

The recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 1.21 million cases of SARS-CoV-2 infection and more than 67,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. The World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted COVID-19 as a pandemic now. Multiple sequence alignment data correlated with the already published reports on SARS-CoV-2 evolution indicated that this virus is closely related to the bat severe acute respiratory syndrome-like coronavirus (bat SARS-like CoV) and the well-studied human SARS coronavirus (SARS-CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins contains molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of SARS-CoV-2 proteome will have important implications in understanding the structural and non-structural biology of SARS or SARS-like coronaviruses.

DescribePROT: database of amino acid-level protein structure and function predictions

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.