Structural insights into the role of deleterious mutations at the dimeric interface of Toll-like receptor interferon-ß related adaptor protein.

Toll-like receptors (TLRs) are major players in the innate immune system-recognizing pathogens and differentiating self/non-self components of immunity. These proteins are present either on the plasma membrane or endosome and recognize pathogens at their extracellular domains. They are characterized by a single transmembrane helix and an intracellular toll-interleukin-1 receptor (TIR) domain. Few TIRs directly invoke downstream signaling, while others require other TIR domains of adaptors like TIR domain-containing adaptor-inducing interferon-ß (TRIF) and TRIF-related adaptor molecule (TRAM). On recognizing pathogenic lipopolysaccharides, TLR4 dimerises and interacts with the intracellular TRAM dimer through the TIR domain to recruit a downstream signaling adaptor (TRIF). We have performed an in-depth study of the structural effect of two mutations (P116H and C117H) at the dimeric interface of the adaptor TRAM, which are known to abrogate downstream signaling. We modeled the structure and performed molecular dynamics studies in order to decipher the structural basis of this effect. We observed that these mutations led to an increased radius of gyration of the complex and resulted in several changes to the interaction energy values when compared against the wild type (WT) and positive control mutants. We identified highly interacting residues as hubs in the WT dimer, and a few such hubs that were lost in the mutant dimers. Changes in the protein residue path, hampering the information flow between the crucial A86/E87/D88/D89 and T155/S156 sites, were observed for the mutants. Overall, we show that such residue changes can have subtle but long-distance effects, impacting the signaling path allosterically.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites.

Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

DDX24 is required for muscle fiber organization and the suppression of wound-induced Wnt activity necessary for pole re-establishment during planarian regeneration.

Planarians have a remarkable ability to undergo whole-body regeneration. Successful regeneration outcome is determined by processes like polarity establishment at the wound site, which is followed by pole (organizer) specification. Interestingly, these determinants are almost exclusively expressed by muscles in these animals. However, the molecular toolkit that enables the functional versatility of planarian muscles remains poorly understood. Here we report that SMED_DDX24, a D-E-A-D Box RNA helicase, is necessary for planarian survival and regeneration. We found that DDX24 is enriched in muscles and its knockdown disrupts muscle fiber organization. This leads to defective pole specification, which in turn results in misregulation of many positional control genes specifically during regeneration. ddx24 RNAi also upregulates wound-induced Wnt signalling. Suppressing this ectopic Wnt activity rescues the knockdown phenotype by enabling better anterior pole regeneration. To summarize, our work highlights the role of an RNA helicase in muscle fiber organization, and modulating amputation-induced wnt levels, both of which seem critical for pole re-organization, thereby regulating whole-body regeneration.

Comparative analysis of permanent and transient domain-domain interactions in multi-domain proteins.

Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co-existing domains and provide stability. Hence, it is important to study intra-protein inter-domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein-protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain-domain interactions. In this work, we studied 263 two-domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two-domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure-function and folding principles of multi-domain proteins.

The alteration of structural network upon transient association between proteins studied using graph theory.

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

Immune profile and responses of a novel dengue DNA vaccine encoding an EDIII-NS1 consensus design based on Indo-African sequences.

The ongoing COVID-19 pandemic highlights the need to tackle viral variants, expand the number of antigens, and assess diverse delivery systems for vaccines against emerging viruses. In the present study, a DNA vaccine candidate was generated by combining in tandem envelope protein domain III (EDIII) of dengue virus serotypes 1-4 and a dengue virus (DENV)-2 non-structural protein 1 (NS1) protein-coding region. Each domain was designed as a serotype-specific consensus coding sequence derived from different genotypes based on the whole genome sequencing of clinical isolates in India and complemented with data from Africa. This sequence was further optimized for protein expression. In silico structural analysis of the EDIII consensus sequence revealed that epitopes are structurally conserved and immunogenic. The vaccination of mice with this construct induced pan-serotype neutralizing antibodies and antigen-specific T cell responses. Assaying intracellular interferon (IFN)-γ staining, immunoglobulin IgG2(a/c)/IgG1 ratios, and immune gene profiling suggests a strong Th1-dominant immune response. Finally, the passive transfer of immune sera protected AG129 mice challenged with a virulent, non-mouse-adapted DENV-2 strain. Our findings collectively suggest an alternative strategy for dengue vaccine design by offering a novel vaccine candidate with a possible broad-spectrum protection and a successful clinical translation either as a stand alone or in a mix and match strategy.

Computational analysis of potential candidate genes involved in the cold stress response of ten Rosaceae members.

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.

Transcriptome profiling of two Moringa species and insights into their antihyperglycemic activity.

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.

The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

Molecular mechanisms and structural features of cardiomyopathy-causing troponin T mutants in the tropomyosin overlap region.

Point mutations in genes encoding sarcomeric proteins are the leading cause of inherited primary cardiomyopathies. Among them are mutations in the TNNT2 gene that encodes cardiac troponin T (TnT). These mutations are clustered in the tropomyosin (Tm) binding region of TnT, TNT1 (residues 80-180). To understand the mechanistic changes caused by pathogenic mutations in the TNT1 region, six hypertrophic cardiomyopathy (HCM) and two dilated cardiomyopathy (DCM) mutants were studied by biochemical approaches. Binding assays in the absence and presence of actin revealed changes in the affinity of some, but not all, TnT mutants for Tm relative to WT TnT. HCM mutants were hypersensitive and DCM mutants were hyposensitive to Ca2+ in regulated actomyosin ATPase activities. To gain better insight into the disease mechanism, we modeled the structure of TNT1 and its interactions with Tm. The stability predictions made by the model correlated well with the affinity changes observed in vitro of TnT mutants for Tm. The changes in Ca2+ sensitivity showed a strong correlation with the changes in binding affinity. We suggest the primary reason by which these TNNT2 mutations between residues 92 and 144 cause cardiomyopathy is by changing the affinity of TnT for Tm within the TNT1 region.

Transcriptome analysis reveals plasticity in gene regulation due to environmental cues in Primula sikkimensis, a high altitude plant species.

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.

Investigating the effect of key mutations on the conformational dynamics of toll-like receptor dimers through molecular dynamics simulations and protein structure networks.

The Toll-like receptors (TLRs) are critical components of the innate immune system due to their ability to detect conserved pathogen-associated molecular patterns, present in bacteria, viruses, and other microorganisms. Ligand detection by TLRs leads to a signaling cascade, mediated by interactions among TIR domains present in the receptors, the bridging adaptors and sorting adaptors. The BB loop is a highly conserved region present in the TIR domain and is crucial for mediating interactions among TIR domain-containing proteins. Mutations in the BB loop of the Toll-like receptors, such as the A795P mutation in TLR3 and the P712H mutation (Lpsd mutation) in TLR4, have been reported to disrupt or alter downstream signaling. While the phenotypic effect of these mutations is known, the underlying effect of these mutations on the structure, dynamics and interactions with other TIR domain-containing proteins is not well understood. Here, we have attempted to investigate the effect of the BB loop mutations on the dimer form of TLRs, using TLR2 and TLR3 as case studies. Our results based on molecular dynamics simulations, protein-protein interaction analyses and protein structure network analyses highlight significant differences between the dimer interfaces of the wild-type and mutant forms and provide a logical reasoning for the effect of these mutations on adaptor binding to TLRs. Furthermore, it also leads us to propose a hypothesis for the differential requirement of signaling and bridging adaptors by TLRs. This could aid in further understanding of the mechanisms governing such signaling pathways.

Probing subtle conformational changes induced by phosphorylation and point mutations in the TIR domains of TLR2 and TLR3.

Extensive research performed on Toll-like receptor (TLR) signaling has identified residues in the Toll/interleukin-1 receptor (TIR) domains that are essential for its proper functioning. Among these residues, those in BB loop are particularly significant as single amino acid mutations in this region can cause drastic changes in downstream signaling. However, while the effect of these mutations on the function is well studied (like the P681H mutation in TLR2, the A795P mutation in TLR3, and the P714H mutation in TLR4), their influence on the dynamics and inter-residue networks is not well understood. The effects of local perturbations induced by these mutations could propagate throughout the TIR domain, influencing interactions with other TIR domain-containing proteins. The identification of these subtle changes in inter-residue interactions can provide new insights and structural rationale for how single-point mutations cause drastic changes in TIR-TIR interactions. We employed molecular dynamics simulations and protein structure network (PSN) analyses to investigate the structural transitions with special emphasis on TLR2 and TLR3. Our results reveal that phosphorylation of the Tyr 759 residue in the TIR domain of TLR3 introduces rigidity to its BB loop. Subtle differences in the intra BB loop hydrogen bonding network between TLR3 and TLR2 are also observed. The PSN analyses indicate that the TIR domain is highly connected and pinpoints key differences in the inter-residue interactions between the wild-type and mutant TIR domains, suggesting that TIR domain structure is prone to allosteric effects, consistent with the current view of the influence of allostery on TLR signaling.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment.

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses.

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Bioinformatics comparisons of RNA-binding proteins of pathogenic and non-pathogenic Escherichia coli strains reveal novel virulence factors.

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.

Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space.

MOTIVATION: In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the available methods. RESULTS: We have developed a method to identify remote homologues more effectively from any protein sequence database by using several cascading events with Hidden Markov Models (C-HMM). We have implemented clustering of hits and profile generation of hit clusters to effectively reduce the computational timings of the cascaded sequence searches. Our C-HMM approach could cover 94, 83 and 40% coverage at family, superfamily and fold levels, respectively, when applied on diverse protein folds. We have compared C-HMM with various remote homology detection methods and discuss the trade-offs between coverage and false positives. AVAILABILITY AND IMPLEMENTATION: A standalone package implemented in Java along with a detailed documentation can be downloaded from https://github.com/RSLabNCBS/C-HMM SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: mini@ncbs.res.in.

NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection.

NrichD (http://proline.biochem.iisc.ernet.in/NRICHD/) is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural 'intermediately related sequences' is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been 'enriched' with these artificial intermediary sequences. NrichD database currently contains 3,611,010 artificial sequences that have been generated between 27,882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.