Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied to unequally sized protein sequences. PSSM summaries representing column averages and their amino-acid wise versions have been effectively used for the task, but it remains unclear if these features carry all the PSSM's predictive power, traditionally harnessed for binding site predictions. Here we evaluate if PSSMs scaled up to a fixed size by zero-vector padding (pPSSM) could perform better than the summary based features on similar models. Using multilayer perceptron (MLP) and deep convolutional neural network (CNN), we found that (a) Summary features work well for single-genome (human-only) data but are outperformed by pPSSM for diverse PDB-derived data sets, suggesting greater summary-level redundancy in the former, (b) even when summary features work comparably well with pPSSM, a consensus on the two outperforms both of them (c) CNN models comprehensively outperform their corresponding MLP models and (d) actual predicted scores from different models depend on the choice of input feature sets used whereas overall performance levels are model-dependent in which CNN leads the accuracy.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Intestinal Epithelium-Derived Luminally Released Extracellular Vesicles in Sepsis Exhibit the Ability to Suppress TNF-a and IL-17A Expression in Mucosal Inflammation.

Sepsis is a systemic inflammatory disorder induced by a dysregulated immune response to infection resulting in dysfunction of multiple critical organs, including the intestines. Previous studies have reported contrasting results regarding the abilities of exosomes circulating in the blood of sepsis mice and patients to either promote or suppress inflammation. Little is known about how the gut epithelial cell-derived exosomes released in the intestinal luminal space during sepsis affect mucosal inflammation. To study this question, we isolated extracellular vesicles (EVs) from intestinal lavage of septic mice. The EVs expressed typical exosomal (CD63 and CD9) and epithelial (EpCAM) markers, which were further increased by sepsis. Moreover, septic-EV injection into inflamed gut induced a significant reduction in the messaging of pro-inflammatory cytokines TNF-a and IL-17A. MicroRNA (miRNA) profiling and reverse transcription and quantitative polymerase chain reaction (RT-qPCR) revealed a sepsis-induced exosomal increase in multiple miRNAs, which putatively target TNF-a and IL-17A. These results imply that intestinal epithelial cell (IEC)-derived luminal EVs carry miRNAs that mitigate pro-inflammatory responses. Taken together, our study proposes a novel mechanism by which IEC EVs released during sepsis transfer regulatory miRNAs to cells, possibly contributing to the amelioration of gut inflammation.

APBioNet's annual International Conference on Bioinformatics (InCoB) returns to India in 2018.

InCoB, one of the largest annual bioinformatics conferences in the Asia-Pacific region since its launch in 2002, returned to New Delhi, India after 12 years, with a conference attendance of 314 delegates. The 2018 conference had sessions on Big Data and Algorithms, Next Generation Sequencing and Omics Science, Structure, Function and Interactions, Disease and Drug Discovery and Plant and Agricultural Bioinformatics. The conference also featured an industry track as well as panel discussions on Women in Bioinformatics and Democratization vs. Quality control in academic publishing. Asia Pacific Bioinformatics Interaction & Networking Society (APbians) was launched as an APBionet Special Interest Group. Of the 52 oral presentations made, 22 were accepted in supplemental issues of BMC Bioinformatics, BMC Genomics or BMC Medical Genomics and are briefly reviewed here. Next year's InCoB will be held in Jakarta, Indonesia from September 10-12, 2019.

Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins.

Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein⁻RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids⁻nucleotide pairs; which showed the preference of His⁻G; Asn⁻U and Ser⁻U at for the interface of DOT regions. This study provides insights to understand protein⁻RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins.

RNA polymerase III regulates cytosolic RNA:DNA hybrids and intracellular microRNA expression.

RNA:DNA hybrids form in the nuclei and mitochondria of cells as transcription-induced R-loops or G-quadruplexes, but exist only in the cytosol of virus-infected cells. Little is known about the existence of RNA:DNA hybrids in the cytosol of virus-free cells, in particular cancer or transformed cells. Here, we show that cytosolic RNA:DNA hybrids are present in various human cell lines, including transformed cells. Inhibition of RNA polymerase III (Pol III), but not DNA polymerase, abrogated cytosolic RNA:DNA hybrids. Cytosolic RNA:DNA hybrids bind to several components of the microRNA (miRNA) machinery-related proteins, including AGO2 and DDX17. Furthermore, we identified miRNAs that are specifically regulated by Pol III, providing a potential link between RNA:DNA hybrids and the miRNA machinery. One of the target genes, exportin-1, is shown to regulate cytosolic RNA:DNA hybrids. Taken together, we reveal previously unknown mechanism by which Pol III regulates the presence of cytosolic RNA:DNA hybrids and miRNA biogenesis in various human cells.

Improved pose and affinity predictions using different protocols tailored on the basis of data availability.

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins.

Protein-DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at http://www.biotech.iitm.ac.in/DNA-protein/. Further, we have grouped the methods based on their complexity and analyzed the performance. The information gained in this work could be effectively used to select the best method for designing experiments.

Distinct transcriptional regulatory modules underlie STAT3's cell type-independent and cell type-specific functions.

Transcription factors (TFs) regulate gene expression by binding to short DNA sequence motifs, yet their binding specificities alone cannot explain how certain TFs drive a diversity of biological processes. In order to investigate the factors that control the functions of the pleiotropic TF STAT3, we studied its genome-wide binding patterns in four different cell types: embryonic stem cells, CD4(+) T cells, macrophages and AtT-20 cells. We describe for the first time two distinct modes of STAT3 binding. First, a small cell type-independent mode represented by a set of 35 evolutionarily conserved STAT3-binding sites that collectively regulate STAT3's own functions and cell growth. We show that STAT3 is recruited to sites with E2F1 already pre-bound before STAT3 activation. Second, a series of different transcriptional regulatory modules (TRMs) assemble around STAT3 to drive distinct transcriptional programs in the four cell types. These modules recognize cell type-specific binding sites and are associated with factors particular to each cell type. Our study illustrates the versatility of STAT3 to regulate both universal- and cell type-specific functions by means of distinct TRMs, a mechanism that might be common to other pleiotropic TFs.

Prediction and experimental validation of a putative non-consensus binding site for transcription factor STAT3 in serum amyloid A gene promoter.

We previously demonstrated that though the human SAA1 gene shows no typical STAT3 response element (STAT3-RE) in its promoter region, STAT3 and the nuclear factor (NF-κB) p65 first form a complex following interleukin IL-1 and IL-6 (IL-1+6) stimulation, after which STAT3 interacts with a region downstream of the NF-κB RE in the SAA1 promoter. In this study, we employed a computational approach based on indirect read outs of protein-DNA contacts to identify a set of candidates for non-consensus STAT3 transcription factor binding sites (TFBSs). The binding of STAT3 to one of the predicted non-consensus TFBSs was experimentally confirmed through a dual luciferase assay and DNA affinity chromatography. The present study defines a novel STAT3 non-consensus TFBS at nt -75/-66 downstream of the NF-κB RE in the SAA1 promoter region that is required for NF-κB p65 and STAT3 to activate SAA1 transcription in human HepG2 liver cells. Our analysis builds upon the current understanding of STAT3 function, suggesting a wider array of mechanisms of STAT3 function in inflammatory response, and provides a useful framework for investigating novel TF-target associations with potential therapeutic implications.

Conformational changes in DNA-binding proteins: relationships with precomplex features and contributions to specificity and stability.

Both Proteins and DNA undergo conformational changes in order to form functional complexes and also to facilitate interactions with other molecules. These changes have direct implications for the stability and specificity of the complex, as well as the cooperativity of interactions between multiple entities. In this work, we have extensively analyzed conformational changes in DNA-binding proteins by superimposing DNA-bound and unbound pairs of protein structures in a curated database of 90 proteins. We manually examined each of these pairs, unified the authors' annotations, and summarized our observations by classifying conformational changes into six structural categories. We explored a relationship between conformational changes and functional classes, binding motifs, target specificity, biophysical features of unbound proteins, and stability of the complex. In addition, we have also investigated the degree to which the intrinsic flexibility can explain conformational changes in a subset of 52 proteins with high quality coordinate data. Our results indicate that conformational changes in DNA-binding proteins contribute significantly to both the stability of the complex and the specificity of targets recognized by them. We also conclude that most conformational changes occur in proteins interacting with specific DNA targets, even though unbound protein structures may have sufficient information to interact with DNA in a nonspecific manner.

Global and local genomic features together modulate the spontaneous single nucleotide mutation rate.

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems. We find that the spontaneous SNM rates in our data are correlated with many genomic features in prokaryotes and unicellular eukaryotes irrespective of their sample sizes. On the other hand, only the number of coding genes was correlated with the spontaneous SNM rates in multicellular eukaryotes primarily contributed by vertebrates data. Considering local features, we notice that local GC content and CpG content significantly were correlated with the spontaneous SNM rates in the unicellular eukaryotes, while local repeat fraction is an important feature in prokaryotes and certain specific uni- and multi-cellular eukaryotes. Such predictive features of the spontaneous SNM rates often support non-linear models as the best fit compared to the linear model. We also observe that the strand asymmetry in prokaryotes plays an important role in determining the spontaneous SNM rates but the SNM spectrum does not.

Deconstructing RNA: optical measurement of composition and structure.

RNA molecules are involved in many pathways within the cell and their sequence composition, structure, conformational transitions and interactions with other molecules are all important factors in determining RNA function. Here we present a method for systematically and quantitatively determining characteristics of RNA using Raman spectroscopy. This method can be used to assess the composition and structure of a given RNA molecule, including ribose-phosphate sugar-pucker conformation, face-to-face base stacking and hydrogen bonding interactions. Three RNA molecules with different sequence and structural features (the exon splicing silencer 3 from HIV-1, an RNA aptamer against Runt-related transcription factor, and the SARS coronaviral stem loop 2) are presented as examples where the structure is crucial to the function of the RNA. We carry out piecewise analysis of the RNA spectra and show that using a nucleotide spectra library helps to unlock the entire ensemble of vibrational information. This analysis demonstrates the extent to which RNA characteristics can be elucidated, using purely optical methods.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates.

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

CCRXP: exploring clusters of conserved residues in protein structures.

Conserved residues forming tightly packed clusters have been shown to be energy hot spots in both protein-protein and protein-DNA complexes. A number of analyses on these clusters of conserved residues (CCRs) have been reported, all pointing to a crucial role that these clusters play in protein function, especially protein-protein and protein-DNA interactions. However, currently there is no publicly available tool to automatically detect such clusters. Here, we present a web server that takes a coordinate file in PDB format as input and automatically executes all the steps to identify CCRs in protein structures. In addition, it calculates the structural properties of each residue and of the CCRs. We also present statistics to show that CCRs, determined by these procedures, are significantly enriched in 'hot spots' in protein-protein and protein-RNA complexes, which supplements our more detailed similar results on protein-DNA complexes. We expect that CCRXP web server will be useful in studies of protein structures and their interactions and selecting mutagenesis targets. The web server can be accessed at http://ccrxp.netasa.org.

Predictive modeling of moonlighting DNA-binding proteins.

Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY.

Understanding disorder-to-order transitions in protein-RNA complexes using molecular dynamics simulations.

Intrinsically disordered regions (IDRs) in proteins are characterized by their flexibilities and low complexity regions, which lack unique 3 D structures in solution. IDRs play a significant role in signaling, regulation, and binding multiple partners, including DNA, RNA, and proteins. Although various experiments have shown the role of disordered regions in binding with RNA, a detailed computational analysis is required to understand their binding and recognition mechanism. In this work, we performed molecular dynamics simulations of 10 protein-RNA complexes to understand the binding governed by intrinsically disordered regions. The simulation results show that most of the disordered regions are important for RNA-binding and have a transition from disordered-to-ordered conformation upon binding, which often contribute significantly towards the binding affinity. Interestingly, most of the disordered residues are present at the interface or located as a linker between two regions having similar movements. The DOT regions are overlaped or flanked with experimentally reported functionally important residues in the recognition of protein-RNA complexes. This study provides additional insights for understanding the role and recognition mechanism of disordered regions in protein-RNA complexes.Communicated by Ramaswamy H. Sarma.

Inadequacy of Evolutionary Profiles Vis-a-vis Single Sequences in Predicting Transient DNA-Binding Sites in Proteins.

Sequence-based prediction of DNA-binding residues in a protein is a widely studied problem for which machine learning methods with continuously improving predictive power have been developed. Concatenated rows within a sliding window of a Position Specific Substitution Matrix (PSSM) of the protein are currently used as the primary feature set in almost all the methods of predicting DNA-binding residues. Here we report that these evolutionary profiles are powerful, only for identifying conserved binding sites and fall short for the residue positions which undergo binding to non-binding transitions in closely related proteins. We created a database of highly similar protein pairs with known protein-DNA complexes and investigated differential predictability of conserved and transient binding residues within each pair. Retraining machine learning models uniformly, we compared the predictive powers of the models trained on PSSMs against similarly trained models on sparse-encoded single sequences. We found that the transient binding site predictions from evolutionary profiles are outperformed by single-sequence based models under controlled experiments by as much as 8 percentage points. Thus, we conclude that the PSSM-based models are inadequate to predict high-specificity DNA-binding residues. These findings are of critical significance for the design of mutant- and species-specific DNA ligands and for homology based modeling of protein-DNA complexes.

Host-pathogen protein-nucleic acid interactions: A comprehensive review.

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

Unraveling molecular mechanisms of head and neck cancer.

Malignancies that develop from mucosal epithelium of the upper aerodigestive tract are known as head and neck squamous cell carcinomas (HNSCC). Heterogeneity, late stage diagnosis and high recurrence rate are big hurdles in head and neck treatment regimen. Presently, the biomarkers available for diagnosis and prognosis of HNSCC are based on smoking as the major risk habit. This review shed light on the differential environment of HNSCC in smokeless tobacco consuming Indian patients. Frequent mutation in genes involved in DNA repair pathway (p53), cell proliferation (PIK3CA, HRAS) and cell death (CASP8, FADD) are common in western population. On the contrary, the genes involved in metastasis (MMPs, YAP1), lymphocyte proliferation (TNFRSF4, CD80), cell-cell adhesion (DCC, EDNRB), miRNA processing (DROSHA) and inflammatory responses (TLR9, IL-9) are mutated in Indian HNSCC patients. Gene ontology enrichment analysis highlighted that responses to chemical stimulus, immune pathways and stress pathways are highly enriched in Indian patients.