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Abstract Introduction Black pepper (Piper nigrum L.) is a non-model spice crop of significant agricultural and biological importance. The ‘quick wilt’ disease caused by the oomycete Phytophthora capsici is a major threat, leading to substantial crop loss. The molecular mechanisms governing the plant immune responses to this pathogen remain unclear. This study employs RNA sequencing and transcriptome analysis to explore the defense mechanisms of P. nigrum against P. capsici. Results Two-month-old P. nigrum plantlets were subjected to infection with P. capsici, and leaf samples were collected at 6- and 12-hours post-inoculation. RNA was extracted, sequenced, and the resulting data were processed and assembled. Differential gene expression analysis was conducted to identify genes responding to the infection. Additionally, the study investigated the involvement of Salicylic acid (SA), Jasmonic acid (JA), and Ethylene (ET) signalling pathways. Our transcriptome assembly comprised 64,667 transcripts with 96.7% completeness, providing valuable insights into the P. nigrum transcriptome. Annotation of these transcripts identified functional categories and domains, provided details on molecular processes. Gene expression analysis identified 4,714 transcripts at 6 h post-infection (hpi) and 9,416 at 12 hpi as differentially expressed, revealing dynamic regulation of immune-related genes. Furthermore, the study investigated key genes involved in biosynthesis pathways of Salicylic acid, Jasmonic acid, and Ethylene signalling. Notably, we found differential regulation of critical genes associated with these pathways while comparing data before and after infection, thereby shedding light on their roles in defense mechanism in P. nigrum defense. Conclusions This comprehensive transcriptome analysis of P. nigrum response to P. capsici attack provides valuable insights into the plant defense mechanisms. The dynamic regulation of innate immunity and the involvement of key signalling pathways highlight the complexity of the plant-pathogen interaction. This study contributes to our understanding of plant immunity and offers potential strategies for enhancing P. nigrum resistance to this harmful pathogen.

Abstract Celiac disease (CD) is a common autoimmune disorder in which the patients are unable to digest gluten, which is present in foods made up of wheat, barley and rye. Whilst diagnosis happens late in 80% of the cases, avoidance of such foods appears to be the common solution. Alternative management strategies are required for the patients and their families since CD is also genetically carried over. Probiotic therapeutics and the consumption of appropriate enzymes, such as prolyloligopeptidases (POPs), from gut-friendly bacteria could reduce the disease burden and provide a better lifestyle for CD patients. We have examined around 5000 gut bacterial genomes and identified nearly 4000 non-redundant putative POPs. A select set of 10 gut bacterial POP sequences were subject to three-dimensional modelling, ligand docking and molecular dynamics simulations where stable interactions were observed between the POPs and gluten peptides. Our study provides sequence and structural analysis of potential POP enzymes in gut bacterial genomes, which form a strong basis to offer probiotic solutions to CD patients. In particular, these enzymes could be lead future therapeutics for this disease.

Abstract Background Moringa concanensis Nimmo (MC), a plant that resembles Moringa oleifera Lam. (MO), has less scientific information but has traditionally been used as a medicinal plant. Moringa species have long been known for their medicinal qualities, which include antioxidant, anti-inflammatory, anticancer, and antihyperglycemic effects. We investigated the antidiabetic potential of MC and MO species in this study by using transcriptome profiling, metabolite analysis, and in vitro assay studies. Results Our transcriptome analysis revealed the expression of enzymes involved in the biosynthesis of quercetin, chlorogenic acid, and benzylamine, all of which have previously been shown to have antidiabetic activity. We compared the expression patterns of five different tissues from MC and MO and it was found that the key enzymes involved in the biosynthesis of these compounds were highly expressed in leaf tissue. The expression estimated by MC transcriptome data in different tissues was verified using RT-qPCR analysis. The amount of these compounds was further quantified in the crude leaf extract of both species and found that MC had a higher abundance of quercetin and chlorogenic acid than MO. The crude leaf extract from both MC and MO were further tested in vitro, and the results demonstrated strong inhibitory activity for α-glucosidase and DPP-IV enzymes. Our findings suggest that compounds in leaf tissue, such as quercetin, benzylamine, and chlorogenic acid, could play a significant role in this antidiabetic activity. In addition, when comparing MO plants, we found that MC had a slightly higher effect in expression, abundance, and inhibitory activity. Conclusions This study presents the first report of MC transcriptome data, as well as a comparison of its anti-diabetic activity to MO. Our analysis discussed the significance of leaf tissue in antidiabetic activity compared to other tissues of both species. Overall, this study not only provides transcriptome resources for Moringa species, but also sheds light on antidiabetic potential of both species.

Homologous proteins can display high structural variation due to evolutionary divergence at low sequence identity. This classical inverse relationship between sequence identity and structural similarity, established many years ago, has remained true between homologous proteins of known structure over time. However, a large number of heteromeric proteins also exist in the structural data bank, where the interacting subunits belong to the same fold and maintain low sequence identity between themselves. It is not clear if there is any selection pressure to deviate from the inverse sequence–structure relationship for such interacting distant homologs, in comparison to pairs of homologs which are not known to interact. We examined 12,824 fold pairs of interacting homologs of known structure, which includes both heteromers and multi‐domain proteins. These were compared with monomeric proteins, resulting in 26,082 fold pairs as a dataset of non‐interacting homologous systems. Interacting homologs were found to retain higher structural similarity than non‐interacting homologs at diminishing sequence identity in a statistically significant manner. Interacting homologs are more similar in their 3D structures than non‐interacting homologs and have a preference towards symmetric association. There appears to be a structural constraint between remote homologs due to this commitment.

Abstract Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy’s genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.

Abstract Toll/Interleukin-1 receptor (TIR) domains are cytoplasmic domain that mediates receptor signalling. These domains are present in proteins like Toll-like receptors (TLR), its signaling adaptors and Interleukins, that form a major part of the immune system. These TIR domain containing signaling adaptors binds to the TLRs and interacts with their TIR domains for downstream signaling. We have examined the evolutionary divergence across the tree of life of two of these TIR domain containing adaptor molecules (TICAM) i.e., TIR domain-containing adapter-inducing interferon-β (TRIF/TICAM1) and TIR domain containing adaptor molecule2 (TRAM/TICAM2), by using computational approaches. We studied their orthologs, domain architecture, conserved motifs, and amino acid variations. Our study also adds a timeframe to infer the duplication of TICAM protein from Leptocardii and later divergence into TICAM1/TRIF and TICAM2/TRAM. More evidence of TRIF proteins was seen, but the absence of conserved co-existing domains such as TRIF-NTD, TIR, and RHIM domains in distant relatives hints on diversification and adaptation to different biological functions. TRAM was lost in Actinopteri and has conserved domain architecture of TIR across species except in Aves. An additional isoform of TRAM, TAG (TRAM adaptor with the GOLD domain), could be identified in species in the Mesozoic era. Finally, the Hypothesis based Likelihood ratio test was applied to look for selection pressure amongst orthologues of TRIF and TRAM to search for positively selected sites. These residues were mostly seen in the non-structural region of the proteins. Overall, this study unravels evolutionary information on the adaptors TRAM and TRIF and how well they had duplicated to perform diverse functions by changes in their domain architecture across lineages.

Abstract Fenugreek (Trigonella foenum-graecum L.) is a self-pollinated leguminous crop belonging to the Fabaceae family. It is a multipurpose crop used as herb, spice, vegetable and forage. It is a traditional medicinal plant in India attributed with several nutritional and medicinal properties including antidiabetic and anticancer. We have performed a combined transcriptome assembly from RNA sequencing data derived from leaf, stem and root tissues. Around 209,831 transcripts were deciphered from the assembly of 92% completeness and an N50 of 1382 bases. Whilst secondary metabolites of medicinal value, such as trigonelline, diosgenin, 4-hydroxyisoleucine and quercetin, are distributed in several tissues, we report transcripts that bear sequence signatures of enzymes involved in the biosynthesis of such metabolites and are highly expressed in leaves, stem and roots. One of the antidiabetic alkaloid, trigonelline and its biosynthesising enzyme, is highly abundant in leaves. These findings are of value to nutritional and the pharmaceutical industry.

Abstract Background Plant species from Rosaceae family are economically important. One of the major environmental factors impacting those species is cold stress. Although several Rosaceae plant genomes have recently been sequenced, there have been very few research conducted on cold upregulated genes and their promoter binding sites. In this study, we used computational approaches to identify and analyse potential cold stress response genes across ten Rosaceae family members. Results Cold stress upregulated gene data from apple and strawberry were used to identify syntelogs in other Rosaceae species. Gene duplication analysis was carried out to better understand the distribution of these syntelog genes in different Rosaceae members. A total of 11,145 popular abiotic stress transcription factor-binding sites were identified in the upstream region of these potential cold-responsive genes, which were subsequently categorised into distinct transcription factor (TF) classes. MYB classes of transcription factor binding site (TFBS) were abundant, followed by bHLH, WRKY, and AP2/ERF. TFBS patterns in the promoter regions were compared among these species and gene families, found to be quite different even amongst functionally related syntelogs. A case study on important cold stress responsive transcription factor family, AP2/ERF showed less conservation in TFBS patterns in the promoter regions. This indicates that syntelogs from the same group may be comparable at the gene level but not at the level of cis-regulatory elements. Therefore, for such genes from the same family, different repertoire of TFs could be recruited for regulation and expression. Duplication events must have played a significant role in the similarity of TFBS patterns amongst few syntelogs of closely related species. Conclusions Our study overall suggests that, despite being from the same gene family, different combinations of TFs may play a role in their regulation and expression. The findings of this study will provide information about potential genes involved in the cold stress response, which will aid future functional research of these gene families involved in many important biological processes.

IntroductionCardiomyopathies are complex heart diseases with significant prevalence around the world. Among these, primary forms are the major contributors to heart failure and sudden cardiac death. As a high-energy demanding engine, the heart utilizes fatty acids, glucose, amino acid, lactate and ketone bodies for energy to meet its requirement. However, continuous myocardial stress and cardiomyopathies drive towards metabolic impairment that advances heart failure (HF) pathogenesis. So far, metabolic profile correlation across different cardiomyopathies remains poorly understood.MethodsIn this study, we systematically explore metabolic differences amongst primary cardiomyopathies. By assessing the metabolic gene expression of all primary cardiomyopathies, we highlight the significantly shared and distinct metabolic pathways that may represent specialized adaptations to unique cellular demands. We utilized publicly available RNA-seq datasets to profile global changes in the above diseases (|log2FC| ≥ 0.28 and BH adjusted p-val 0.1) and performed gene set analysis (GSA) using the PAGE statistics on KEGG pathways.ResultsOur analysis demonstrates that genes in arachidonic acid metabolism (AA) are significantly perturbed across cardiomyopathies. In particular, the arachidonic acid metabolism gene PLA2G2A interacts with fibroblast marker genes and can potentially influence fibrosis during cardiomyopathy.ConclusionThe profound significance of AA metabolism within the cardiovascular system renders it a key player in modulating the phenotypes of cardiomyopathies.

Actin filaments help in maintaining the cell structure and coordinating cellular movements and cargo transport within the cell. Actin participates in the interaction with several proteins and also with itself to form the helical filamentous actin (F-actin). Actin-binding proteins (ABPs) and actin-associated proteins (AAPs) coordinate the actin filament assembly and processing, regulate the flux between globular G-actin and F-actin in the cell, and help maintain the cellular structure and integrity. We have used protein–protein interaction data available through multiple sources (STRING, BioGRID, mentha, and a few others), functional annotation, and classical actin-binding domains to identify actin-binding and actin-associated proteins in the human proteome. Here, we report 2482 AAPs and present an analysis of their structural and sequential domains, functions, evolutionary conservation, cellular localization, abundance, and tissue-specific expression patterns. This analysis provides a base for the characterization of proteins involved in actin dynamics and turnover in the cell.

Myotubularin or MTM1 is a lipid phosphatase that regulates vesicular trafficking in the cell. The MTM1 gene is mutated in a severe form of muscular disease, X-linked myotubular myopathy or XLMTM, affecting 1 in 50,000 newborn males worldwide. There have been several studies on the disease pathology of XLMTM, but the structural effects of missense mutations of MTM1 are underexplored due to the unavailability of a crystal structure. MTM1 consists of three domains-a lipid-binding N-terminal GRAM domain, the phosphatase domain and a coiled-coil domain which aids dimerisation of Myotubularin homologs. While most mutations reported to date map to the phosphatase domain of MTM1, the other two domains on the sequence are also frequently mutated in XLMTM. To understand the overall structural and functional effects of missense mutations on MTM1, we curated several missense mutations and performed in silico and in vitro studies. Apart from significantly impaired binding to substrate, abrogation of phosphatase activity was observed for a few mutants. Possible long-range effects of mutations from non-catalytic domains on phosphatase activity were observed as well. Coiled-coil domain mutants have been characterised here for the first time in XLMTM literature.

Piper nigrum L. (black pepper), a woody perennial spice crop indigenous to India is positioned at the phylogenetically unique basal lineage of angiosperms. Cultivation of this major spice crop is constrained by rampant fungal and viral infections leading to a lack of disease-free planting material. The major disease that poses severe threat to P. nigrum plantations and nurseries is ‘quick wilt’ caused by the oomycete Phytophthora capsici, which affects the leaf, stem, spike, collar and root. In this paper, we report the consequence of priming in modulating Piper nigrum defense against Phytophthora capsici. Glycol Chitosan (GC) was used to infiltrate detached leaves of mature P. nigrum plants. It was observed that pre-treatment of GC for 24 hours resulted in significant reduction of disease symptoms in infected leaves, as evidenced by the marked decrease in the size of lesions, and also delayed the appearance of symptoms up to 72 hpi. Experiments repeated in P. nigrum seedlings under controlled growth conditions indicate that delayed disease symptoms of GC pre-treated leaves do not spread to healthy uninfiltrated leaves suggesting a priming-associated systemic defense response. An ROS-mediated manifestation of Hypersensitive Response (HR) induced by Chitosan was also evident in pre-treated leaves. A corresponding visual indication of increased lignification was observed, which correlated with an enhanced lignin content of GC-treated leaves. Enhanced callose deposition was also apparent in GC infiltrated leaves, establishing a stimulatory effect of GC in triggering HR through ROS production, enhanced lignification and callose deposition. Key genes of the core phenylpropanoid and isoprenoid pathways along with major defense signalling pathway genes of P. nigrum, including pathogenesis-related genes and hormone signalling genes showed significant transcript enrichment consequential to GC treatment. A significant quantitative enhancement in Piperine content was evident in GC-infiltrated leaves. The systemic nature of priming on disease protection was established through experiments conducted in rooted cuttings monitored for 30 days after disease infection. This is the first report that provides strong molecular evidence endorsing the twofold advantage of defense priming in P. nigrum by improving crop protection with a concomitant enhancement in Piperine biosynthesis.

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.

Aggregation of β-amyloid (Aβ) peptide is one of the hallmarks of Alzheimer’s disease (AD) which results in chronic and progressive neurodegeneration of the brain. A recent study by our group have shown the ability of cyclic disulfide-rich peptides (“cyclotides”) isolated from a medicinal plant, Clitoria ternatea, to inhibit the aggregation of Aβ peptides and reduce oxidative stress caused by reactive oxygen species using in vivo models of transgenic Caenorhabditis elegans. In the present study, through extensive computational docking and multi-ns molecular dynamics (MD) simulation, we evaluated if cyclotides can stably bind to Aβ molecules and/or destabilize the Aβ fibril by preventing conformational changes from α-helical to β-sheet rich structures. We demonstrate that cyclotides bind effectively and stably to different forms of Aβ structures via hydrogen bonding and hydrophobic interactions. One of the conserved hydrophobic interface residues, Tyr10 was mutated to Ala and the impact of this virtual mutation was estimated by additional MD simulations for the wild-type (WT) and mutant protein-peptide complexes. A detailed MD simulation analyses revealed that cyclotides form hydrogen bonds with the toxic amyloid assemblies thereby weakening the inter-strand hydrogen bonds between the Aβ peptide. The φ-ѱ distribution map of residues in the cyclotide binding pocket that ideally adopt β-sheet conformation show deviation towards right-handed ɑ-helical (ɑR) conformation. This effect was similar to that observed for the Tyr10Ala mutant and doubly so, for the cyclotide bound form. It is therefore possible to hypothesise that the opening up of amyloid β-sheet is due to an unfolding process occurring in the Aβ caused by cyclotide binding and inhibition. Our current findings provide novel structural insights on the mode of interaction between cyclotides and Aβ fibrils and describe their anti-amyloid aggregation potential. This sheds light on the future of cyclotide-based drug design against protein aggregation, a hallmark event in many neurodegenerative diseases.

Abstract Background Studying plasticity in gene expression in natural systems is crucial, for predicting and managing the effects of climate change on plant species. To understand the contribution of gene expression level variations to abiotic stress compensation in a Himalaya plant (Primula sikkimensis), we carried out a transplant experiment within (Ambient), and beyond (Below Ambient and Above Ambient) the altitudinal range limit of species. We sequenced nine transcriptomes (three each from each altitudinal range condition) using Illumina sequencing technology. We compared the fitness variation of transplants among three transplant conditions. Results A large number of significantly differentially expressed genes (DEGs) between below ambient versus ambient (109) and above ambient versus ambient (85) were identified. Transcripts involved in plant growth and development were mostly up-regulated in below ambient conditions. Transcripts involved in signalling, defence, and membrane transport were mostly up-regulated in above ambient condition. Pathway analysis revealed that most of the genes involved in metabolic processes, secondary metabolism, and flavonoid biosynthesis were differentially expressed in below ambient conditions, whereas most of the genes involved in photosynthesis and plant hormone signalling were differentially expressed in above ambient conditions. In addition, we observed higher reproductive fitness in transplant individuals at below ambient condition compared to above ambient conditions; contrary to what we expect from the cold adaptive P. sikkimensis plants. Conclusions We reveal P. sikkimensis’s capacity for rapid adaptation to climate change through transcriptome variation, which may facilitate the phenotypic plasticity observed in morphological and life history traits. The genes and pathways identified provide a genetic resource for understanding the temperature stress (both the hot and cold stress) tolerance mechanism of P. sikkimensis in their natural environment.

G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug Administration (FDA)-approved GPCR-targeting drugs, there is a continuing challenge of side effects owing to the nonspecific nature of drug binding. We have investigated the diversity of the ligand binding site for this class of proteins against their cognate ligands using computational docking, even if their structures are known already in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site with better score than the binding at the conservative site. Interestingly, amino acid residues at such allosteric binding site are not conserved across GPCR subfamilies. Such a computational approach can assist in the prediction of specific allosteric binders for GPCRs.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

BackgroundAfrica has one of the highest incidences of gonorrhea. Neisseria gonorrhoeae is gaining resistance to most of the available antibiotics, compromising treatment across the world. Whole-genome sequencing (WGS) is an efficient way of predicting AMR determinants and their spread in the population. Recent advances in next-generation sequencing technologies like Oxford Nanopore Technology (ONT) have helped in the generation of longer reads of DNA in a shorter duration with lower cost. Increasing accuracy of base-calling algorithms, high throughput, error-correction strategies, and ease of using the mobile sequencer MinION in remote areas lead to its adoption for routine microbial genome sequencing. To investigate whether MinION-only sequencing is sufficient for WGS and downstream analysis in resource-limited settings, we sequenced the genomes of 14 suspected N. gonorrhoeae isolates from Nairobi, Kenya.MethodsUsing WGS, the isolates were confirmed to be cases of N. gonorrhoeae (n = 9), and there were three co-occurrences of N. gonorrhoeae with Moraxella osloensis and N. meningitidis (n = 2). N. meningitidis has been implicated in sexually transmitted infections in recent years. The near-complete N. gonorrhoeae genomes (n = 10) were analyzed further for mutations/factors causing AMR using an in-house database of mutations curated from the literature.ResultsWe observe that ciprofloxacin resistance is associated with multiple mutations in both gyrA and parC. Mutations conferring tetracycline (rpsJ) and sulfonamide (folP) resistance and plasmids encoding beta-lactamase were seen in all the strains, and tet(M)-containing plasmids were identified in nine strains. Phylogenetic analysis clustered the 10 isolates into clades containing previously sequenced genomes from Kenya and countries across the world. Based on homology modeling of AMR targets, we see that the mutations in GyrA and ParC disrupt the hydrogen bonding with quinolone drugs and mutations in FolP may affect interaction with the antibiotic.ConclusionHere, we demonstrate the utility of mobile DNA sequencing technology in producing a consensus genome for sequence typing and detection of genetic determinants of AMR. The workflow followed in the study, including AMR mutation dataset creation and the genome identification, assembly, and analysis, can be used for any clinical isolate. Further studies are required to determine the utility of real-time sequencing in outbreak investigations, diagnosis, and management of infections, especially in resource-limited settings.

Homologous domains embedded in multidomain proteins of different domain architectures (DA) may exhibit subtle, but important, differences in their structure and function. Here, we consider two multidomain proteins, Arf nucleotide binding site opener (ARNO) and G protein‐coupled receptor kinase 2 (GRK2), which have very different DAs, but both contain pleckstrin homology (PH) domains. We analyzed the roles of residues selectively conserved in these subfamilies of PH domains from ARNO and GRK2 proteins. DA‐specific residues in PH domain are found to contribute to structural and functional specialization of ARNO and GRK2 in terms of (a) specific intra‐ and interprotein interactions; (b) specificity for phospholipids; and (c) participation in conformational excursions, leading to various functional forms. Our approach can also be applied to subfamilies of other protein families to identify subfamily‐specific residues and their specialized roles.

The bacterial enzyme PaaZ is involved in the breakdown of environmental pollutants via the aerobic-anaerobic hybrid pathway but its substrate transfer mechanism is not fully understood. Here, the authors present cryoEM structures of free and ligand-bound PaaZ that suggest a mechanism for internal substrate channeling.

Abstract Background Protein-protein interactions are crucial for normal biological processes and to regulate cellular reactions that affect gene expression and function. Several previous studies have emphasized the roles of residues at the interface of protein-protein complexes in conferring stability and specificity to the complex. Interface residues in a protein are well known for their interactions with sidechain and main chain atoms with the interacting protein. However, the extent of intra-protein interactions involving interface residues in a protein-protein complex and their relative contribution in comparison to inter-protein interactions are not clearly understood. This paper probes this feature using a dataset of protein-protein complexes of known 3-D structure. Results We have analysed a dataset of 45 transient protein-protein complex structures with at least one of the interacting proteins with a known structure available also in the unbound form. We observe that a large proportion of interface residues (1608 out of 2137 interface residues, 75%) are involved in intra and inter-protein interactions simultaneously. The amino acid propensities of such interfacial residues involved in bifurcated interactions are found to be highly similar to the general propensities to occur at protein-protein interfaces. Finally, we observe that a majority (83%) of intra-protein interactions of interface residues with bifurcated interactions, are also observed in the protein uncomplexed form. Conclusions We have shown, to the best of our knowledge for the first time, that a vast majority of the protein-protein interface residues are involved in extensive intra-protein interactions apart from inter-protein interactions. For a majority of such interface residues the microenvironment in the tertiary structure is pre-formed and retained upon complex formation with its cognate partner during transient interactions. Reviewers This article was reviewed by Arumay Pal and Mallur Madhusudhan.

In this paper, we present the data acquired during transcriptome analysis of the plant Moringa oleifera [1] from five different tissues (root, stem, leaf, flower and seed) by RNA sequencing. A total of 271 million reads were assembled with an N50 of 2094 bp. The combined transcriptome was assessed for transcript abundance across five tissues. The protein coding genes identified from the transcripts were annotated and used for orthology analysis. Further, enzymes involved in the biosynthesis of select medicinally important secondary metabolites, vitamins and ion transporters were identified and their expression levels across tissues were examined. The data generated by RNA sequencing has been deposited to NCBI public repository under the accession number PRJNA394193 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA394193).

The genome of Asian cultivated rice (Oryza sativa L.) shows the presence of six organelle-specific and one plasma membrane (OsNHX1-7) NHX-type cation proton antiporters. Of these, vacuolar-localized OsNHX1 is extensively characterized. The genus Oryza consists of 27 species and 11 genome-types, with cultivated rice, diploid O. sativa, having an AA-type genome. Oryza NHX1 orthologous regions (gene organization, 5′ upstream cis elements, amino acid residues/motifs) from closely related Oryza AA genomes cluster distinctly from NHX1 regions from more ancestral Oryza BB, FF and KKLL genomes. These sequence-specific distinctions also extend to two separate intron retention (IR) events involving Oryza NHX1 transcripts that occur at the 5′ and 3′ ends of the NHX1 transcripts. We demonstrate that the IR event involving the 5′ UTR is present only in more recently evolved Oryza AA genomes while the IR event governing retention of the 13th intron of Oryza NHX1 (terminal intron) is more ancient in origin, also occurring in halophytic wild rice, Oryza coarctata (KKLL). We also report presence of a retro-copy of the OcNHX1 cDNA in the genome of O. coarctata (rOcNHX1). Preferential species and tissue specific up- or down-regulation of the correctly spliced NHX1 transcript/5′ UTR/13th intron-retaining splice variants under salinity was observed. The implications of IR on NHX1 mRNA stability and ORF diversity in Oryza spp. is discussed.

This protocol describes a stepwise process to identify proteins of interest from a query proteome derived from NGS data. We implemented this protocol on Moringa oleifera transcriptome to identify proteins involved in secondary metabolite and vitamin biosynthesis and ion transport. This knowledge-driven protocol identifies proteins using an integrated approach involving sensitive sequence search and evolutionary relationships. We make use of functionally important residues (FIR) specific for the query protein family identified through its homologous sequences and literature. We screen protein hits based on the clustering with true homologues through phylogenetic tree reconstruction complemented with the FIR mapping. The protocol was validated for the protein hits through qRT-PCR and transcriptome quantification. Our protocol demonstrated a higher specificity as compared to other methods, particularly in distinguishing cross-family hits. This protocol was effective in transcriptome data analysis of M. oleifera as described in Pasha et al. • Knowledge-driven protocol to identify secondary metabolite synthesizing protein in a highly specific manner. • Use of functionally important residues for screening of true hits. • Beneficial for metabolite pathway reconstruction in any (species, metagenomics) NGS data.

Abstract Background Pathogenic bacteria have evolved various strategies to counteract host defences. They are also exposed to environments that are undergoing constant changes. Hence, in order to survive, bacteria must adapt themselves to the changing environmental conditions by performing regulations at the transcriptional and/or post-transcriptional levels. Roles of RNA-binding proteins (RBPs) as virulence factors have been very well studied. Here, we have used a sequence search-based method to compare and contrast the proteomes of 16 pathogenic and three non-pathogenic E. coli strains as well as to obtain a global picture of the RBP landscape (RBPome) in E. coli. Results Our results show that there are no significant differences in the percentage of RBPs encoded by the pathogenic and the non-pathogenic E. coli strains. The differences in the types of Pfam domains as well as Pfam RNA-binding domains, encoded by these two classes of E. coli strains, are also insignificant. The complete and distinct RBPome of E. coli has been established by studying all known E. coli strains till date. We have also identified RBPs that are exclusive to pathogenic strains, and most of them can be exploited as drug targets since they appear to be non-homologous to their human host proteins. Many of these pathogen-specific proteins were uncharacterised and their identities could be resolved on the basis of sequence homology searches with known proteins. Detailed structural modelling, molecular dynamics simulations and sequence comparisons have been pursued for selected examples to understand differences in stability and RNA-binding. Conclusions The approach used in this paper to cross-compare proteomes of pathogenic and non-pathogenic strains may also be extended to other bacterial or even eukaryotic proteomes to understand interesting differences in their RBPomes. The pathogen-specific RBPs reported in this study, may also be taken up further for clinical trials and/or experimental validations.

Abstract Background TRIF is a key protein in antiviral innate immunity, operating downstream of TLRs. TRIF activation leads to the production of interferon-β and pro-inflammatory cytokines. There is evidence from experiments to suggest that the N-terminal domain of TRIF binds to its TIR domain to avoid constitutive activation. However, no structure of a complex between the N-terminal domain and the TIR domain exists till date. The disordered nature of the region connecting the N-terminal domain and the TIR domain compounds the issue of elucidating the mechanism of autoinhibition of TRIF. In this study, we have employed an integrative approach consisting of mutual information analysis, docking, molecular dynamics simulations and residue network analysis, in combination with existing experimental data to provide a glimpse of TRIF in its autoinhibited state. Results Our extensive docking approach reveals that the N-terminal domain binds to the BB loop-B helix region of the TIR domain, consistent with experimental observations. Long length molecular dynamics simulations of 1 microsecond performed on the docked model highlights residues participating in hydrogen bonding and hydrophobic interactions at the interface. A pair of residues present in the vicinity of the interface is also predicted by mutual information analysis, to co-evolve. Residues mediating long-range interactions within the TIR domain of TRIF were identified using residue network analysis. Conclusions Based on the results of the modelling and residue network analysis, we propose that the N-terminal domain binds to the BB loop region of the TIR domain, thereby preventing its homodimersation. The binding of TRIF to TLR3 or TRAM could induce a slight conformational change, causing the interactions between the N-terminal domain and TIR domain to disrupt, thereby exposing the BB loop and rendering it amenable for higher-order oligomerisation. Reviewers This article was reviewed by Michael Gromiha, Srikrishna Subramaniam and Peter Bond (nominated by Chandra Verma).

Chemical signaling is ubiquitous and employs a variety of receptor types to detect the cacophony of molecules relevant for each living organism. Insects, our most diverse taxon, have evolved unique olfactory receptors with as little as 10% sequence identity between receptor types. We have identified a promiscuous volatile, 2-methyltetrahydro-3-furanone (coffee furanone), that elicits chemosensory and behavioral activity across multiple insect orders and receptors. In vivo and in vitro physiology showed that coffee furanone was detected by roughly 80% of the recorded neurons expressing the insect-specific olfactory receptor complex in the antenna of Drosophila melanogaster, at concentrations similar to other known, and less promiscuous, ligands. Neurons expressing specialized receptors, other chemoreceptor types, or mutants lacking the complex entirely did not respond to this compound. This indicates that coffee furanone is a promiscuous ligand for the insect olfactory receptor complex itself and did not induce non-specific cellular responses. In addition, we present homology modeling and docking studies with selected olfactory receptors that suggest conserved interaction regions for both coffee furanone and known ligands. Apart from its physiological activity, this known food additive elicits a behavioral response for several insects, including mosquitoes, flies, and cockroaches. A broad-scale behaviorally active molecule non-toxic to humans thus has significant implications for health and agriculture. Coffee furanone serves as a unique tool to unlock molecular, physiological, and behavioral relationships across this diverse receptor family and animal taxa.

Reversible phosphorylation maintained by protein kinases and phosphatases is an integral part of intracellular signalling, and phosphorylation on tyrosine is extensively utilised in higher eukaryotes. Tyrosine phosphatases are enzymes that not only scavenge phosphotyrosine but are also involved in wide range of signalling pathways. As a result, mutations in these enzymes have been implicated in the pathogenesis of several diseases like cancer, autoimmune disorders, and muscle-related diseases. The genes that harbour phosphatase domain also display diversity in co-existing domains suggesting the recruitment of the catalytic machinery in diverse pathways. We have examined the current draft of the human genome, using a combination of 3 sequence search methods and validations, and identified 101 genes encoding tyrosine phosphatase-containing gene products, agreeing with previous reports. Such gene products adopt 37 unique domain architectures (DAs), including few new ones and harbouring few co-existing domains that have not been reported before. This semi-automated computational approach for detection of gene products belonging to a particular superfamily can now be easily applied at whole genome level on other mammalian genomes and for other protein domains as well.

Computational approaches to high-throughput data are gaining importance because of explosion of sequences in the post-genomic era. This explosion of sequence data creates a huge gap among the domains of sequence structure and function, since the experimental techniques to determine the structure and function are very expensive, time taking, and laborious in nature. Therefore, there is an urgent need to emphasize on the development of computational approaches in the field of biological systems. Engagement of proteins in quaternary arrangements, such as domain swapping, might be relevant for higher compatibility of such genes at stress conditions. In this study, the capacity to engage in domain swapping was predicted from mere sequence information in the whole genome of holy Basil ( Ocimum tenuiflorum ), which is well known to be an anti-stress agent. Approximately, one-fourth of the proteins of O tenuiflorum are predicted to undergo three-dimensional (3D)-domain swapping. Furthermore, function annotation was carried out on all the predicted domain-swap sequences from the O tenuiflorum and Arabidopsis thaliana for their distribution in different Pfam protein families and gene ontology (GO) terms. These domain-swapped protein sequences are associated with many Pfam protein families with a wide range of GO annotation terms. A comparative analysis of domain-swap-predicted sequences in O tenuiflorum with gene products in A thaliana reveals that around 26% (2522 sequences) are close homologues across the 2 genomes. Functional annotation of predicted domain-swapped sequences infers that predicted domain-swap sequences are involved in diverse molecular functions, such as in gene regulation of abiotic stress conditions and adaptation to different environmental niches. Finally, the positively predicted sequences of A thaliana and O tenuiflorum were also examined for their presence in stress regulome, as recorded in our STIFDB database, to check the involvement of these proteins in different abiotic stresses.

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

Insertions/deletions are common evolutionary tools employed to alter the structural and functional repertoire of protein domains. An insert situated proximal to the active site or ligand binding site frequently impacts protein function; however, the effect of distal indels on protein activity and/or stability are often not studied. In this paper, we have investigated a distal insert, which influences the function and stability of a unique DNA polymerase, called terminal deoxynucleotidyl transferase (TdT). TdT (EC:2.7.7.31) is a monomeric 58 kDa protein belonging to family X of eukaryotic DNA polymerases and known for its role in V(D)J recombination as well as in non-homologous end-joining (NHEJ) pathways. Two murine isoforms of TdT, with a length difference of twenty residues and having different biochemical properties, have been studied. All-atom molecular dynamics simulations at different temperatures and interaction network analyses were performed on the short and long-length isoforms. We observed conformational changes in the regions distal to the insert position (thumb subdomain) in the longer isoform, which indirectly affects the activity and stability of the enzyme through a mediating loop (Loop1). A structural rationale could be provided to explain the reduced polymerization rate as well as increased thermosensitivity of the longer isoform caused by peripherally located length variations within a DNA polymerase. These observations increase our understanding of the roles of length variants in introducing functional diversity in protein families in general.

3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neuro-degenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping.

Olfactory receptors (ORs) belong to the class A G-Protein Coupled Receptor superfamily of proteins. Unlike G-Protein Coupled Receptors, ORs exhibit a combinatorial response to odors/ligands. ORs display an affinity towards a range of odor molecules rather than binding to a specific set of ligands and conversely a single odorant molecule may bind to a number of olfactory receptors with varying affinities. The diversity in odor recognition is linked to the highly variable transmembrane domains of these receptors. The purpose of this study is to decode the odor-olfactory receptor interactions using in silico docking studies. In this study, a ligand (odor molecules) dataset of 125 molecules was used to carry out in silico docking using the GLIDE docking tool (SCHRODINGER Inc Pvt LTD). Previous studies, with smaller datasets of ligands, have shown that orthologous olfactory receptors respond to similarly-tuned ligands, but are dramatically different in their efficacy and potency. Ligand docking results were applied on homologous pairs (with varying sequence identity) of ORs from human and mouse genomes and ligand binding residues and the ligand profile differed among such related olfactory receptor sequences. This study revealed that homologous sequences with high sequence identity need not bind to the same/ similar ligand with a given affinity. A ligand profile has been obtained for each of the 20 receptors in this analysis which will be useful for expression and mutation studies on these receptors.

Domain swapping mechanism is a specialised mode of oligomerization of proteins in which part of a protein is exchanged in a non-covalent manner between constituent subunits. This mechanism is highly affected by several physiological conditions. Here, we present a detailed analysis ofthe effect of pH on different regions of the domain swapped oligomer by considering examples which are known to be sensitive to pH in transiting from monomeric to domain-swapped dimeric form. The energetic calculations were performed using a specialized method which considers changes in pH and subsequent changes in the interactions between subunits. This analysis provides definitive hints about the pH-dependence switch from monomer to domain-swapped oligomer and the steps that may be involved in the swapping mechanism.

The protein sequence space is vast and diverse, spanning across different families. Biologically meaningful relationships exist between proteins at superfamily level. However, it is highly challenging to establish convincing relationships at the superfamily level by means of simple sequence searches. It is necessary to design a rigorous sequence search strategy to establish remote homology relationships and achieve high coverage. We have used iterative profile-based methods, along with constraints of sequence motifs, to specify search directions. We address the importance of multiple start points (queries) to achieve high coverage at protein superfamily level. We have devised strategies to employ a structural regime to search sequence space with good specificity and sensitivity. We employ two well-known sequence search methods, PSI-BLAST and PHI-BLAST, with multiple queries and multiple patterns to enhance homologue identification at the structural superfamily level. The study suggests that multiple queries improve sensitivity, while a pattern-constrained iterative sequence search becomes stringent at the initial stages, thereby driving the search in a specific direction and also achieves high coverage. This data mining approach has been applied to the entire structural superfamily database.

Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

BACKGROUND: Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. METHODOLOGY/PRINCIPAL FINDINGS: We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of ~100% and Mathew's correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. CONCLUSIONS/SIGNIFICANCE: Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the 'bridging' role of related families.

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion.