Aedes aegypti and A. atropalpus are related mosquitoes that differ reproductively. Aedes aegypti must blood-feed to produce eggs (anautogenous) while A. atropalpus always produces a first clutch of eggs without blood-feeding (facultatively autogenous). We recently characterized the gut microbiota of A. aegypti and A. atropalpus that were reared identically in the laboratory. Here, we assessed the effects of specific members of the gut microbiota in A. aegypti and A. atropalpus on female fitness including egg production.A. aegypti and A. atropalpus larvae were colonized by specific members of the gut microbiota. Survival, development time, size and egg production for each treatment was then compared to axenic and conventionally reared larvae.Gnotobiotic A. aegypti but only one betaproteobacterium, a Comamonas, supported development and egg production by A. atropalpus to equivalent levels as conventionally reared females. Aedes atropalpus females colonized by Comamonas contained similar stores of glycogen and protein as conventionally reared females, whereas females colonized by Aquitalea did not. Small differences in bacterial loads were detected between gnotobiotic and conventionally reared A. aegypti and A. atropalpus, but this variation did not correlate with the beneficial effects of Comamonas in A. atropalpus.Most species of bacteria we tested supported normal development and egg production by A. atropalpus than A. aegypti.Specific members of the gut microbiota more strongly affected survival, size and egg production by The online version of this article (doi:10.1186/s13071-016-1660-9) contains supplementary material, which is available to authorized users. Most mosquito species are anautogenous, which means that after mating with males, adult females must consume at least one blood meal from a vertebrate host for each clutch of eggs they produce and lay . RepeateAedes aegypti, which vectors several human pathogens including the viruses that cause yellow fever, dengue fever and Zika virus disease. In brief, the fat body of newly emerged A. aegypti females becomes competent to produce yolk proteins through the activity of juvenile hormone [Regulation of egg formation is best understood in the anautogenous mosquito  hormone , 3. Howe hormone , 5. Ecdy hormone –8. Nutri hormone –11.Aedes atropalpus, is closely related to A. aegypti [Aedes atropalpus females produce similar numbers of eggs in the first gonadotropic cycle as A. aegypti, but fully rely on nutrient reserves acquired from feeding during the larval stage to do so [A. atropalpus to produce ecdysone and the fat body to synthesize yolk proteins as determined for A. aegypti [A. atropalpus females release these neurohormones shortly after emerging as adults rather than in response to blood-feeding [The rockpool mosquito,  aegypti  but is fto do so , 13–15.  aegypti . What di-feeding . The mec-feeding , 15, 16.Microorganisms in the digestive tract can also influence nutrient acquisition by animals including insects . MosquitA. aegypti and A. atropalpus larvae reared under identical environmental conditions in the laboratory. Results showed that similar communities of bacteria were present in the aquatic habitat of both species during larval development. However, Bacteroidetes (Flavobacteriaceae) and Actinobacteria (Microbacteriaceae) dominated the gut community in A. aegypti larvae, whereas Proteobacteria (Class Betaproteobacteria) dominated the community in A. atropalpus [A. aegypti larvae, and that the resulting gnotobiotic larvae also develop normally into adults [A. aegypti or any fitness traits in A. atropalpus.We recently used 16S rDNA sequencing to characterize the gut microbiota of ropalpus . We deveo adults . HoweverA. aegypti and A. atropalpus larvae that were singly colonized by abundant members of the larval gut microbiota and compared their effects on female-related fitness traits including egg production in the first ovarian cycle. Our results showed that gnotobiotic A. aegypti singly colonized by several members of the gut microbiota developed and produced eggs similarly to conventionally reared A. aegypti females with a mixed community of bacteria. In contrast, only one community member we tested rescued development and egg production to equivalent levels as conventionally reared A. atropalpus females.In this study, we produced gnotobiotic A. aegypti and Bass Rock strain of A. atropalpus were conventionally reared in the same insectary at 27 °C, 60 % relative humidity, and 16 h light: 8 h dark photoperiod [A. aegypti and A. atropalpus that were similar in size. Adults were fed 10 % sucrose in water (wt/vol) ad libitum. Adult female A. aegypti were blood-fed two days post-emergence on an anesthetized rat until engorged. Aedes aegypti females laid a clutch of eggs approximately 36 h after blood-feeding on damp filter paper. Aedes atropalpus females in contrast laid clutches of eggs on damp filter paper 3–5 days post-emergence.The UGAL strain of toperiod . Pupae wA. aegypti and A. atropalpus larvae by collecting and surface sterilizing fourth instars followed by homogenization in 1.6 ml centrifuge tubes in sterile phosphate buffered saline (PBS) using a plastic pestle [Bacterial isolates were maintained as summarized in Additional file c pestle . Homogenc pestle .A. aegypti and A. atropalpus colonies followed by hatching of first instars in sterile water per Coon et al. [8 cells of a given bacterial isolate suspended in sterile water. Sterilized diet was thereafter added to dishes every other day until death or pupation. Pupae were surface-sterilized by placing in 2 % (vol/vol) bleach for 2 min and rinsing 3 times in sterile water. Pupae were then placed in sterile water in an autoclaved polypropylene plastic chamber (Olcott Plastics) for adult emergence. The axenic status of larvae and food were confirmed by culture-based methods and PCR analysis using universal 16S rRNA primers  lined with moist filter paper and containing a cotton wick soaked with water. Filter paper, wicks, water and cages were autoclaved before use. The number of eggs laid by each female was counted by visually inspecting the filter paper using a dissecting microscope. Each female was then dissected in PBS and the number of mature eggs remaining in the ovaries was also determined by visual inspection using previously established criteria . These c2SO4 with 200 μl of methanol  followed by storage at −80 °C before use [g, 4 °C), supernatants were used to determine total protein, glycogen and lipid amounts as previously described [Conventionally reared and gnotobiotic adult females were collected 12 h post-emergence followed by dissection in PBS and removal of the digestive tract. The remaining body wall, which contains the fat body, from two individuals was transferred to a microfuge tube and homogenized in 100 μl of water (protein assay) or 100 μl of Nafore use . After cescribed . A totalA. aegypti) or 72 h post-emergence . At least 4 larvae or adults were analyzed per treatment with each sample internally replicated 4 times.The number of bacteria in gnotobiotic larvae and adults was assessed by standard plate counts. Individual larvae were collected after molting to the fourth instar from replicate dishes, surface-sterilized, homogenized in 100 μl of PBS as described above, serially diluted and plated on LB agar. Colony forming units (CFUs) were then determined after incubation of plates at 27 °C for 24 h. Homogenates were also plated from adult females collected 6–12 h and either 24 h post-blood meal (http://www.r-project.org/). Development time and total nutrient amounts were tested for normality and equality of variances before analysis by one-way analysis of variance (ANOVA), followed by post-hoc comparisons using Dunnett’s tests. Survival data were analyzed by Chi-square or Fisher’s exact tests followed by post-hoc Bonferroni-corrected pairwise tests to compare treatments. Bacterial counts were analyzed by ANOVA followed by either post-hoc comparison to the conventional positive control using Dunnett’s test or Tukey-Kramer Honest Significant Difference (HSD) tests. The number of mature eggs females produced was analyzed by Fisher’s exact test to compare the proportion of females that produced one or more mature eggs in each bacterial treatment while the effects of size were analyzed by logistic regression. These data were also examined by an analysis of covariance (ANCOVA) followed by post-hoc comparisons using a Dunnett’s test.All analyses were performed using R (Paenibacillus (Firmicutes: Paenibacillaceae), Chryseobacterium (Bacteroidetes: Flavobacteriaceae), Sphingobacterium (Bacteroidetes: Sphingobacteriaceae), Microbacterium (Actinobacteria: Microbacteriaceae), Leucobacter (Actinobacteria: Microbacteriaceae), Aquitalea (Proteobacteria: Neisseriaceae) and Comamonas (Proteobacteria: Comamonadaceae). Two of these genera, Aquitalea and Comamonas, belong to the Betaproteobacteria and were previously identified as dominant members of the A. atropalpus larval gut community by pyrosequencing. All other genera were previously isolated from A. aegypti  first instars, which were either maintained in sterile water with sterile food or placed into water inoculated with one of the above isolates plus food. PCR screening of bacterial 16S rDNA using universal primers 6 h after hatching confirmed the presence of bacteria in conventionally reared first instars and the absence of bacteria in axenic larvae  developed and emerged as adults at 8 days post-egg hatching (positive control), while as previously reported no axenic A. aegypti or A. atropalpus larvae developed beyond the first instar (negative control)  rather than a smaller proportion of pupae failing to emerge as adults .Results showed that 87 % of ol) Fig. . All axears Fig. . All of vae Fig. , b. In cpus Fig. , b. Survpus Fig. , b. The A. aegypti and A. atropalpus to estimate body size [A. aegypti adult females and adults that emerged from larvae colonized by Paenibacillus, Chryseobacterium, Sphingobacterium, Aquitalea or Comamonas  the proportion of females for each treatment that laid or contained at least one mature egg; (ii) the total number of mature eggs per female (laid plus present in the ovaries); and (iii) the total number of laid eggs.We next focused on those species of bacteria that supported development of at least some A. aegypti, all females in each treatment produced mature eggs  = 2.26, P > 0.05) and laid the same number of eggs  = 2.19, P > 0.05) as conventionally reared females  . For the A. atropalpus females in each treatment that produced mature eggs, we compared clutch sizes using an ANCOVA where wing length served as the covariate. We detected no significant interaction between wing length and treatment  = 0.9, P = 0.49), indicating equivalent regression slopes for each treatment, while comparison of the adjusted treatment means indicated that clutch sizes significantly differed among treatments  = 13.0, P < 0.0001). The total number of mature eggs produced by gnotobiotic females colonized by Comamonas did not significantly differ from the number of mature eggs produced by conventional females, whereas females colonized by Aquitalea, Sphingobacterium, Chryseobacterium and Paenibacillus all produced fewer mature eggs  Fig. . Almost ggs Fig. . We consggs Fig. . Across ced Fig. . For botA. aegypti and A. atropalpus females at adult emergence [A. aegypti for this study because no differences in egg production were detected between gnotobiotic and conventionally reared females. However, we did compare nutrient reserves between gnotobiotic A. atropalpus colonized by Aquitalea or Comamonas and conventionally reared females. We also focused on these treatments because survival and adult female sizes were similar but egg production was lower in Aquitalea-colonized females. Results showed that stored lipid was higher in newly emerged conventional females than gnotobiotic females colonized by Comamonas, but protein and glycogen were equivalent  in conventionally reared mergence , 15. We ent Fig. . In contlea Fig. .Fig. 3ToA. aegypti versus A. atropalpus is that bacterial abundance differed. We therefore assessed this by plating homogenates of larvae or adults after surface sterilization, which yielded CFUs per individual. Since larvae inoculated with Microbacterium or Leucobacter all died as first instars, we assayed first instars for these treatments at three days post-inoculation. Plating assays yielded no colonies from any larva, which in light of the data in Additional file A. aegypti and A. atropalpus first instars ingested these species but they failed to colonize. Since inoculation with the other isolates resulted in most larvae molting but varying numbers of A. atropalpus developing into adults, we first compared bacterial load in larvae that had just molted to the final (fourth) instar (0–6 h). PCR assays as in Additional file A. aegypti and A. atropalpus fourth instars both contained the bacterium they were inoculated with. Colony counts indicated that each gnotobiotic treatment except Chryseobacterium contained fewer viable bacteria per larva than the conventionally reared (non-sterile) positive control for both mosquito species  and 72 h old adults. For ant Fig. . Merginglts Fig. . In A. alts Fig. .Fig. 5CFAedes aegypti and A. atropalpus females are similar in size when reared as larvae under the same conditions, but A. aegypti emerges with lower nutrient reserves as measured by stored glycogen and protein [A. aegypti females emerge with insufficient nutrient reserves and enter an arrested previtellogenic state that blood-feeding overcomes by providing additional nutrients. Aedes atropalpus females in contrast emerge with sufficient nutrients to initiate egg formation without blood-feeding. Enhanced nutrient acquisition along with genetic factors has also been implicated in anautogenous versus autogenous reproduction in other species [ protein , 15. For protein , 30. Thi protein , 15, 16. species , 31–35.A. aegypti affect blood meal digestion [Enterobacter and Serratia species with hemolytic activity further supported a role for the gut microbiota in blood meal processing. Outside of this work, however, what role if any the gut microbiota plays in female-associated fitness including egg production is unknown in mosquitoes [An earlier study used antibiotic treatment to determine whether gut bacteria in adult igestion . Treatmesquitoes , which iMicrobacterium and Leucobacter), Bacteroidetes (Chryseobacterium and Sphingobacterium), Betaproteobacteria  and Firmicutes (Paenibacillus) are present in the digestive tracts of both A. aegypti and A. atropalpus larvae when reared under identical conditions in the laboratory [A. aegypti and Betaproteobacteria predominating in A. atropalpus. These differences guided our choice of the gut bacteria used in this study to compare the effects individual members have on fitness relative to conventionally reared individuals with a mixed community of bacteria or axenic larvae with no gut bacteria.Our previous results showed that certain genera of Actinobacteria  tested in A. atropalpus supported survival, development times and adult body sizes that were similar to those of conventionally reared larvae. Only A. atropalpus larvae colonized by Comamonas produced the same number of eggs per first clutch as conventionally reared females. The three other community members we tested  are more abundant in conventionally reared A. aegypti than A. atropalpus. Each of these Paenibacillus, Chryseobacterium and Sphingobacterium species colonized A. atropalpus to produce gnotobiotic larvae and increased survival relative to axenic larvae. However, survival and other fitness measures were also much lower than those of conventionally reared A. atropalpus.The first part of our study corroborates prior findings by showing that axenic A. aegypti larvae support development and egg production to levels that are comparable to conventionally reared individuals with a mixed bacterial community. They also suggest the differential abundance of these bacteria in conventionally reared A. aegypti larvae does not reflect the ability of these community members to rescue larval development or egg production by adult females after a blood meal. This is not the case though for A. atropalpus, where the two Betaproteobacteria we tested are more abundant in conventionally reared larvae and also had a more positive effect on survival, size and egg production in gnotobiotic larvae. Thus, one important element of autogeny in A. atropalpus could be greater dependence on the composition of the gut microbiota for development and reproduction. In addition, the impacts of gut bacteria on nutrient reserves in A. aegypti are potentially compensated for by the extra nutrients females obtain from a blood meal.Taken together, these results indicate that several members of the gut community in A. atropalpus colonized by Comamonas emerged with similar stores of glycogen and protein but significantly lower total lipid than conventionally reared females. In contrast, gnotobiotic females colonized by Aquitalea emerged with lower glycogen, protein, and lipid stores. Reduced nutrient uptake and metabolism in axenic or gnotobiotic backgrounds have been reported for mice, zebrafish, Daphnia and Drosophila [Drosophila is also known to promote growth by modulating nutrient sensing through the target of rapamycin and insulin signaling pathways, which intersect to regulate metabolism [Aquitalea or Comamonas are required for normal lipid storage. However, lower lipid levels in Comamonas-colonized females did not correlate with reduced first clutch sizes, which suggests stored protein is the more important determinant for production of a first clutch. The importance of stored protein may also underlie why gnotobiotic females colonized by Aquitalea produced smaller clutches.Gnotobiotic osophila –42. The tabolism , 44. ThuA. aegypti, digestion of a blood meal primarily provides amino acids, which suggests TAG packaged into eggs during the first gonadotropic cycle derives primarily from stores in the fat body while yolk protein is produced from a combination of nutrient stores and blood meal derived amino acids [A. atropalpus, however, TAG and yolk protein must both come from teneral reserves in the fat body. The mobilization of nutrient stores in A. atropalpus colonized by Comamonas is of interest because these females have lower stored lipid relative to females of conventionally reared larvae, but the number of mature eggs produced by both sets of females was similar, as were their protein and carbohydrate stores. How these gnotobiotic females accomplish this is unclear. One possibility is their lower lipid reserves are sufficient to mobilize as TAG and produce a comparable number of mature eggs as conventionally reared females. Alternatively, excess carbohydrate or protein stores may be catabolized to acetyl-CoA, which is the precursor for fatty acid synthesis and thus TAG importation, and would allow females to produce a similar number of mature eggs as conventionally reared females with larger lipid reserves.Most insects including mosquitoes lay eggs that by dry weight consist primarily of protein but also 30–40 % lipid that is predominantly triacylglycerol (TAG) , 45. TAGno acids . In the Microbacterium and Leucobacter, our measures of bacterial abundance in gnotobiotic A. aegypti and A. atropalpus showed that the average number of living bacteria per individual was either similar or slightly lower in most gnotobiotic treatments when compared to conventionally reared controls. This finding indicates each of the community members we tested proliferated in the absence of other community members, but usually did not exceed the total abundance of bacteria in conventional larvae that host a mixed bacterial community. These results indicate that the higher survival and egg production of gnotobiotic A. atropalpus colonized by Comamonas was not due to this bacterium being more abundant in the guts of larvae or adults than the other community members we tested. Thus, other features of this bacterium likely underlie its positive effects for A. atropalpus. These data also indicate that bacterial abundance in gnotobiotic and conventionally reared individuals is lower in adults than fourth instar larvae, which is a feature previously noted for A. aegypti [Excluding  aegypti .A. aegypti and A. atropalpus larvae both require gut bacteria to develop into adults, but individual gut bacteria differentially affect development and reproduction by both species when reared as gnotobiotic larvae. In particular, autogenous egg production by A. atropalpus has a greater dependence on the specific members of the gut microbiota, while the added nutrients females obtain from blood-feeding potentially make A. aegypti females less dependent on the composition of the gut microbiota.Results of this study show that CFU, Colony forming unit; ILPs, Insulin-like peptides; OEH, Ovary ecdysteroidogenic hormone


Several regulators of programmed cell death (PCD) in plants encode proteins with putative lipid-binding domains. Among them, VAD1 is a regulator of PCD propagation harboring a GRAM putative lipid-binding domain. However the function of VAD1 at the subcellular level is unknown and the domain architecture of VAD1 has not been analyzed in details.We analyzed sequence conservation across the plant kingdom in the VAD1 protein and identified an uncharacterized VASt  domain. Using profile hidden Markov models (profile HMMs) and phylogenetic analysis we found that this domain is conserved among eukaryotes and generally associates with various lipid-binding domains. Proteins containing both a GRAM and a VASt domain include notably the yeast Ysp2 cell death regulator and numerous uncharacterized proteins. Using structure-based phylogeny, we found that the VASt domain is structurally related to Bet v1-like domains.We identified a novel protein domain ubiquitous in Eukaryotic genomes and belonging to the Bet v1-like superfamily. Our findings open perspectives for the functional analysis of VASt-containing proteins and the characterization of novel mechanisms regulating PCD. Protein domain predictions are a starting point for a range of functional analyses and can either newly predict or further refine functional predictions . Indeed,Arabidopsis thaliana vad1 (vascular associated death1) mutant is a LMM altered in a negative regulator of PCD and defense responses harboring a GRAM domain predicted to bind lipids . Close analogs also included human CERT ceramide trafficking protein [PDB:2E3M] and Arabidopsis thaliana PYL2 ABA receptor [PDB:3KDI]. Gene Ontology terms associated with AtVAD1 VASt domain based on the 3D model included hormone binding [GO:0042562], isoprenoid binding [GO:0019840] and monocarboxylic acid binding [GO:0033293]. To build a structure-based phylogenetic tree, we modeled the three-dimensional structure of 15 VASt domains using AtVAD1 VASt as a template, and searched for AtVAD1 VASt closest structural analogs in the medium redundancy subset of the Molecular Modeling database  receptor, belonging to the Pyrabactin resistance 1 (PYR1)/PYR1-like (PYL)/Regulatory components of ABA receptors (RCAR) family and uncharacterized bacterial proteins. Group III contained mammalian START proteins binding sterols and sphingolipids, and Streptomyces aromatase-cyclases. Group IV contained VASt-domains. Finally Group V contained mammalian phosphatidylinositol transfer protein (PITP), bacterial oxygenase and hydrolases, and other Bet v1-like domains. With the exception of the N-terminal VASt domain of Phytophthora infestans predicted protein PITG_12663 [Uniprot:D0NKW7], all VASt domains clustered together into structural group IV. A separate sub-group within structural group IV was formed by VASt domains from sequence-based phylogenetic group 8, supporting a possible functional divergence. This analysis revealed that VASt domains are structurally related to Bet v1-like domains [Pfam:CL0209] known to bind bulky hydrophobic ligands such as phytohormones, lipids and polyketides. Association with lipid-binding domains in large multidomain proteins is typical for START and VASt domains.To further test whether AtVAD1 VASt domain and the well-characterized START domain of the CERT protein could be evolutionary related, we closely examined the ligand-binding pockets of these two proteins. For this, we performed a structural alignment of AtVAD1 VASt and CERT START domain models. The structural alignment of AtVAD1 VASt domain and CERT START domain highlighted a good conservation of secondary structure elements lining the substrate binding pocket  STAR-related lipid transport domain [PDB:1EM2] and g GO:001940 and moWe report the identification of the VASt protein domain in the VAD1 plant cell death regulator. This domain is conserved across eukaryotes and is structurally related to Bet v1-like domains, including START lipid-binding domains. The predicted structure of VAD1 VASt domain is consistent with a function in binding large hydrophobic ligands. Our findings open new perspectives for the analysis of functions of the VASt domain associated with the GRAM, C2 and PH lipid-binding domains and the characterization of novel mechanisms regulating PCD in plants.A. thaliana VAD1 is the only exception in plants. In the context of pathogen attack, controlled programmed cell death (PCD) is one of the prevailing plant defense responses, allowing confinement of the pathogen locally in dead cells. The vad1 mutant exhibits spontaneous PCD lesions initiated in cells surrounding vascular tissue progressively expand to the whole leaf, hence its classification as “propagation lesion mimic mutant”  was used as a query for a profile Hidden Markov Model (HMM) search with phmmer  against ersion 7  with defersion 7 , was plo-10. The final list of proteins containing VASt domains was obtained after 3 iterations of jackhammer search using all hits from previous iteration as a seed. Sequence logo of the 452 VASt domains alignment was done with LOGOMAT-M [Homo sapiens and Mus musculus , Caenorhabditis elegans and Drosophila melanogaster (Other Metazoans), Arabidopsis thaliana and Oryza sativa (Tracheophyta), Physcomitrella patens (Briophyta), Saccharomyces cerevisiae, Aspergillus nidulans, Neurospora crassa and Ustilago maydis (Fungi), Phytophthora infestans and Thalassiosira pseudonana (Stramenopiles), Entamoeba histolytica and Dictyostelium discoideum (Amoebozoa), Leishmania major (Kinetoplasts), Tetrahymena thermophila (Ciliates). After removing incomplete sequences and redundant sequences with CD-HIT [The precise boundaries of the newly identified VASt domain were set based on a conservation score >4 among plant AtVAD1 homologs and to include well-conserved N-terminal residues F1, D7 and P11, delimiting a 193 amino acids domain. The ungapped alignment of the 193 amino acids domain of VAD1 VASt domain with its 13 homologs was used as entry for HMM searches with jackhmmer  using BlOGOMAT-M . To builh CD-HIT , 85 sequh CD-HIT . The alih CD-HIT  to keep h CD-HIT . A phyloh CD-HIT  and visuh CD-HIT .http://zhanglab.ccmb.med.umich.edu/I-TASSER/). For structural tree inference, we first searched for AtVAD1 VASt domain structural analogs using the Vector Alignment Search Tool server [The 3D structure of AtVAD1 (At1g02120) VASt domain (residues 257 to 449) was predicted with I-TASSER web server (l server . Forty sl server  using Atl server . Pairwisl server  with powl server .http://itol.embl.de/shared/lipm_bioinfo under the project ‘VAST’. All other supporting data associated with this manuscript are included as additional files.The original version of the tree shown in Figure The authors declare no conflict of interestConceived and designed the analyses: MK, LC, SR. Performed and interpreted the analyses: MK, LC, CB, SR. Wrote the paper: MK, CB, SR. All authors read and approved the manuscript.Multiple sequence alignment of VASt domains from AtVAD1 and its 12 closets plant homologs. Positions showing > 70% identity are highlighted in blue.Click here for fileMultiple alignment file in .FASTA format of AtVAD1 and its 12 closest homologs.Click here for fileTable of the 452 proteins retrieved from Uniprot after HMM search using the model built from 14 VAD1 homologs as bait.Click here for fileMultiple alignment file in .FASTA format of 85 VAST domains.Click here for fileMultiple alignment file in .FASTA format of 85 VAST domains after clean-up by trimAI, used to generate the tree shown in Figure Click here for filePhylogenetic tree of 85 VASt-containing proteins from 17 fully-sequenced genomes in newick format.Click here for fileAtomic coordinates of AtVAD1 VASt domain best model obtained via modeling by I-TASSER in .pdb format.Click here for fileList of AtVAD1 VASt domain closest structural analogs identified in the medium redundancy subset of the Molecular Modeling database using NCBI Vector Alignment Search Tool. Only structures that aligned over 100 residues or more were included in further analyses.Click here for filePairwise RMSD matrix used for the generation of the structure-based neighbor-joining tree of VASt domain analogs in phylip format.Click here for fileStructure based neighbor-joining tree of VASt domain analogs in newick format.Click here for file


The discoidin domain receptors, DDR1 and DDR2, are two closely related receptor tyrosine kinases that are activated by triple-helical collagen in a slow and sustained manner. The DDRs have important roles in embryo development and their dysregulation is associated with human diseases, such as fibrosis, arthritis and cancer. The extracellular region of DDRs consists of a collagen-binding discoidin (DS) domain and a DS-like domain. The transmembrane region mediates the ligand-independent dimerisation of DDRs and is connected to the tyrosine kinase domain by an unusually long juxtamembrane domain. The major DDR binding site in fibrillar collagens is a GVMGFO motif (O is hydroxyproline), which is recognised by an amphiphilic trench at the top of the DS domain. How collagen binding leads to DDR activation is not understood. GVMGFO-containing triple-helical peptides activate DDRs with the characteristic slow kinetics, suggesting that the supramolecular structure of collagen is not required. Activation can be blocked allosterically by monoclonal antibodies that bind to the DS-like domain. Thus, collagen most likely causes a conformational change within the DDR dimer, which may lead to the formation of larger DDR clusters. This article is part of a Special Issue entitled: Emerging recognition and activation mechanisms of receptor tyrosine kinases. ► DDR1 and DDR2 are receptor tyrosine kinases that are activated by collagen. ► DDR activation by collagen is slow and sustained. ► The principles of collagen recognition are understood from structural studies. ► The mechanism of transmembrane signalling is currently not understood. ► Possible activation mechanisms are discussed. Signalling by RTKs critically controls many fundamental cellular processes, such as proliferation, differentiation, survival and migration Dictyostelium discoideumThe DDRs were discovered by homology cloning in searches for novel RTKs Ligand binding to prototypical RTKs results in the autophosphorylation of specific cytosolic tyrosine residues within seconds to minutes of stimulation, followed by the rapid down-regulation of the signal Ddr2−/− mice is due to reduction in chondrocyte proliferation DDR2 gene, termed spondylo-meta-epiphyseal dysplasia short limb-hand type Ddr1−/− mice includes female infertility, as well as defects in the mammary gland Ddr2 gene (slie mice) are infertile due to a gonadal dysregulation The biological outcomes of DDR activation and the role of DDRs in human diseases are only partly understood. The DDRs are widely expressed in mammalian tissues and regulate cell adhesion, migration, proliferation and differentiation 2N-linked glycosylation sites are present in the DS-like domain and the extracellular JM region, which additionally contains several predicted O-linked glycosylation sites. Following the TM region, there is a large cytosolic JM region  and, finally, the tyrosine kinase domain. The cytoplasmic tails of DDRs are unusually short and devoid of tyrosine residues.Invertebrates have DDR genes, but whether the encoded proteins function as collagen receptors is not known Crystal structures have been determined of the DS/DS-like tandem of DDR1 in complex with an antibody Fab fragment 3Mutagenesis experiments based on homology models of the DDR DS domain established that several predicted loop regions are essential for collagen binding and receptor activation Mapping the DDR-binding sites on collagen proved challenging due to the large size and insoluble nature of this structural ECM protein. The fibrillar collagen types I–III consist of three polypeptide chains of ~ 1000 residues, termed α chains, that form a right-handed triple helix with a one-residue stagger between chains. The α chains have a characteristic sequence of repeating glycine–X–Y residues, with positions X and Y often occupied by proline and 4-hydroxyproline, respectively The first attempt to map DDR-binding sites in collagen was made using atomic force and transmission electron microscopy The identification of a minimal DDR2-binding motif allowed the design of a triple-helical collagen-like peptide for co-crystallisation with the DS domain. The resulting crystal structure In contrast to DDR2, which binds to several Toolkit peptides derived from type II and type III collagens, DDR1 binding is restricted to the GVMGFO motif The affinity of a single DS domain for collagen is modest. Ichikawa et al. used surface plasmon resonance to determine a dissociation constant of 25 μM for the binding of the DDR2 DS domain to immobilised type II collagen The structures of the free DS domain of DDR1  revealed that the domain following the DS domain adopts a similar eight-stranded β-barrel fold , despite a sequence identity of only 9% ing site . The DS-The crystallisation of the DDR1 ectodomain was facilitated by the Fab fragment of mAb 3E3, which belongs to a series of anti-DDR1 mAbs that block the collagen-induced phosphorylation of DDR1 allosterically, i.e. without interfering with collagen binding 5It seems highly unlikely that the slow activation of DDRs in response to collagen The central question for structural biologists is how ligand binding is converted into receptor activation. It was originally proposed that RTK activation is the result of ligand-induced receptor dimerisation, which brings the cytosolic tyrosine kinase domains into close proximity and thereby favours trans-autophosphorylation Collagens form supramolecular assemblies with multiple binding sites for cellular receptors Another important property of the DDRs is that they have a high propensity to form non-covalent dimers (or higher oligomers) within the cell membrane. Co-expression of differently tagged DDRs, followed by cell lysis and immunoprecipitation, demonstrated an association of the mature, cell surface-expressed, DDR glycoform, as well as of the intracellular biosynthetic precursors How do the crystal structures of DDR ectodomain fragments fit into this picture? The DDR1 ectodomain lacking the JM region is monomeric in solution Assuming that collagen somehow changes the conformation or oligomeric state of DDRs, how is this change translated into the activation of the cytosolic kinase domain? Given the unusually long JM regions at either side of the cell membrane, it is difficult to envisage a tight conformational coupling of the ligand-binding ectodomain and the cytosolic kinase domain 6DDR2 gene cause a rare form of dwarfism, spondylo-meta-epiphyseal dysplasia short limb-hand type Mutations in the human DDR1 and DDR2 genes have been found in cancer cells The expression and activation status of DDRs is often increased in human cancers 7Since the discovery of collagen as the DDR ligand 15 years ago, steady progress has been made in understanding the structural basis of collagen recognition and signalling by DDRs, but major questions remain. Single collagen triple helices can activate the DDRs in cell culture experiments, but how does this relate to DDR signalling in vivo? Are the DDRs activated by collagen fibres, which are abundant in many tissues? If so, how do cells interpret the sustained signal of tonically active DDRs? Alternatively, the DDRs may only be activated by isolated triple helices in tissues undergoing remodelling and repair. Another important question that needs to be addressed is the mechanism underlying the slow activation kinetics. Here, we expect new insights to come from the application of live cell imaging at single-molecule resolution. Finally, it is frustrating that the crystallographic characterisation of the DDRs has not been more informative about the mechanism of receptor activation. It would be gratifying if the reagents generated by these efforts  could be used to unlock the remaining mysteries.


H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces.H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate  M. tuberculosis--the causative agent of tuberculosis-- infects around one-third of the world's population [Tuberculosis is an infectious disease which causes millions of deaths each year. pulation ,2. Tuberpulation ,4.H. sapiens and M. tuberculosis H37Rv. Many computational approaches have been developed to predict host-pathogen PPIs including approaches based on homology, interacting domain/motif, structure, and even machine learning [Host-pathogen PPIs are essential for a pathogen's colonization, adhesion and invasion of host cells, which are crucial for the understanding of infection mechanism and the interaction between pathogen and host. Unfortunately, high-quality large-scale experimental host-pathogen PPIs are not available in many host-pathogen systems, especially between learning . DDI-baslearning .H. sapiens-M. tuberculosis H37Rv PPIs by taking into account of the differences between each specific domain sequence (we name it "domain instance") on each annotated region of proteins under the same domain ID. The interactions between query domain instances are made based on very stringent sequence alignment to the structural template domain instances. Moreover, we adopt an effective scoring strategy in ranking how likely the predicted proteins are interacting with each other by examining the interacting residues in the interaction interfaces. As long as the two amino acids have one of the atomic interaction: hydrogen bonds, electrostatic or van de Waals interactions between two domain instances that are defined as interacting residues in this study. Thus, we are standing on a much more stringent and finer level of domain interaction by examining not only the sequence similarity of each domain instances but also the interaction interface compatibility between them. In contrast, conventional DDI-based approaches generally use some popular tools to annotate the domains in proteins and then see whether two proteins contain a pair of domains whose IDs match a pair of domains that are known to interact in some other pair of proteins. Matching query domain instance to template domain instance based on domain ID--as done in such conventional DDI-based approaches--is rather coarse and often leads to matching of domain instances that do not have the same interaction interfaces.In this work, we develop a stringent DDI-based approach for predicting the H. sapiens PPIs, we assess the performance of our stringent DDI-based approach and the conventional DDI-based approach by comparing their precision-recall curves and the number of predicted PPIs overlapping with gold standard PPIs. We also use the percentage of coherent informative Gene Ontology(GO) annotations to assess the predicted H. sapiens PPIs to compare the performance of our stringent DDI-based approach and the conventional DDI-based approach. These assessments demonstrate that our stringent DDI-based approach has much better performance than a conventional DDI-based approach. Cellular compartment distribution analysis, pathway enrichment analysis, and functional category enrichment analysis supports the validity of our predicted H. sapiens-M. tuberculosis H37Rv PPI dataset. Our stringent DDI-based approach can be used for predicting host-pathogen PPIs in a variety of different host-pathogen systems. We have also discovered some interesting properties of both pathogen and host proteins participating in host-pathogen PPIs, including the tendency to have more domains, and the domains on the proteins involved in host-pathogen PPIs tend to have much higher degrees.Using gold standard ≤ 20% and sequence similarity ≥ 50%) and transfer the possible interactions between template structural domain instances to our query domain instances. Here the length difference is calculated by the difference of length (longer sequence length minus shorter sequence length) divided by the length of query domain instance; sequence similarity is the number of correctly aligned residues divided by the length of query domain instance.Our stringent DDI-based approach predicts PPIs by inferring domain instance interactions from structural template domain instance interactions. Using the MUSCLE alignment program , we accuH. sapiens-M. tuberculosis H37Rv) and intra-species (H. sapiens) PPIs in this work. For a comparison study, we use a conventional DDI-based approach [H. sapiens) PPIs. We assess our stringent DDI-based approach and the conventional approach using gold standard H. sapiens PPIs and by the percentage of the predicted PPIs that have coherent informative GO annotation. These assessments show that our stringent DDI-based approach has better performance in predicting PPIs than the conventional approach. Cellular compartment distribution analysis, pathway enrichment analysis, and functional enrichment analysis support our prediction results and show that the predicted PPIs correspond to the M. tuberculosis H37Rv infection process. We further analyze some of the basic domain properties of proteins involved in the host-pathogen Protein-Protein Interaction Network (PPIN), comparing with other proteins involved in intra-species PPIN, by examining the number of domains and domain interaction degrees.We then predict the possible PPIs from interacting query domain instances. The structural domain instances are extracted from the 3did database . Each inapproach  to predii Aand i Bbe two instances of domain A and B, respectively. Suppose we know that these two instances have a direct physical interaction . Given the observation of i Aand iB, one could infer the interaction of another instance pair of A and B, j Aand jB, by using a sequence similarity threshold between  and .It is a reasonable assumption that an observed interaction between two domain instances can be used to infer the interaction of another domain instance pair, provided the two domain instance pairs are sufficiently similar as to preserve the relevant interaction interfaces. Specifically, consider two protein domains A and B. Let In general, conventional DDI-based approaches disregard the details of the interaction between these domain instances in the real 3D space--i.e., the interaction interface between the two instances--and thus effectively match the domain instances based on name. In contrast, we formulate a stringent approach that emphasizes the similarity of the interaction interface of the domain instances. Specifically, we assign a positive prediction score on pairs with high interface residue similarity with respect to the observed interaction instances in the existing protein structural data.H. sapiens and M. tuberculosis H37Rv proteins are obtained from Uniprot [≤ 20% and sequence similarity≥ 50% are kept for the following analysis. For each pair  of query domain instances that meets the stringent alignment threshold to a template interacting domain instance pair , we infer the interaction interface residues in  as the residues that are aligned to the interaction interface residues in . A score of this interaction interface of  is then computed by summing the BLOSUM62 substitution score [The data on structural domain instances, including the interacting domain pair, the structural and sequence details of interacting domain instances, the interacting residues in the interaction interfaces are extracted from the 3did database . These i Uniprot . Their r Uniprot , from wh Uniprot . Only quon score  between H. sapiens-M. tuberculosis H37Rv) to identify a set of potential host-pathogen PPIs; the result is visualized in Figure We apply this DDI-based prediction approach on human proteins and this results in 839 predicted human intra-species PPIs. We also predict inter-species PPIs  that two proteins containing a specific pair of domains  would interact is estimated for each pair of domains in a Bayesian manner. Finally, given a new pair of proteins, their probability of interaction is estimated by a naive combination i, ejd) contained in the pair of proteins [The conventional DDI-based approach predicts how likely two proteins are interacting with each other by integrating known intra-species PPIs with domain profiles based on an association method  proposed by Sprinzak et al.  Specificproteins . This prH. sapiens and one in M. tuberculosis), we compute their probability of interactions as described above based on DDIs in a yeast physical PPI dataset collected from MINT [H. sapiens), we compute their probability of interactions as described above based on DDIs in the same yeast physical PPI dataset. As a control study, we ensure that the domains considered are the same domain set considered in the stringent DDI-based approach--i.e., we restrict the domain set to domains contained in 3did.This conventional DDI-based approach is applied to predict host-pathogen PPIs as follows. For each pair of proteins , and see how many of these predicted PPIs overlap with gold standard. We also choose another point on the precision-recall curve, that has a similar number of overlapping PPIs with the gold standard as the stringent DDI-based approach, and see how many predictions are made by conventional DDI-based approach. The results are shown in Table As the two PPI datasets predicted by the stringent and the conventional DDI-based approaches are very different in the number of PPIs, their precision-recall curves may not be sufficient for judging the performance of the two prediction approaches. So we choose some special points to provide a more informative statistics. The stringent DDI-based approach predicted 839 M. tuberculosis H37Rv. For the PPI datasets predicted by the stringent DDI-based approach and by the conventional DDI-based approach, the PPIs in each dataset are sorted according to their respective "interaction strength" , then the percentage of PPIs that has coherent informative GO terms are calculated. For each dataset we move along from the bottom to the top to set the threshold of how many top PPIs are considered, and calculate the percentage of these PPIs having coherent informative GO terms. For the stringent DDI-based approach we choose an interval of 10 PPIs and move along from the bottom to the top, then calculate the percentage of PPIs that have coherent informative GO terms and plot the percentage; see Figure A PPI is more likely to be real, if its two protein components have coherent GO annotation--i.e., the two proteins are annotated with at least one "informative" GO term in common. The percentage of PPIs having coherent GO annotation is also frequently used in assessing the quality of the PPI dataset . Note thThe assessments above prove that our stringent DDI-based approach has a much better performance than the conventional DDI-based approach in predicting more reliable intra-species PPIs. We now analyze the host-pathogen PPIs predicted by our stringent DDI-based approach.H. sapiens proteins targeted by the predicted H. sapiens-M. tuberculosis H37Rv PPIs are useful in telling the quality of the predicted host-pathogen PPIs. If the targeted H. sapiens proteins are located in cellular compartments that are very relevant to the pathogen's infection or are very likely to be involved in interactions with the pathogen, then the result supports the host-pathogen predictions. Gene Ontology  is a very comprehensive annotation system for human proteins. However, as the Gene Ontology is hierarchical, we only use informative CC terms for our analysis.The cellular compartments of the H. sapiens-M. tuberculosis PPIs: An informative CC term is defined here to be a term that has at least 90 proteins annotated with it, but each of its child terms has less than 90 proteins annotate with it. The cellular compartment distribution tells how many proteins(and the percentage) in the datasets fall into each cellular compartment. We show the cellular compartments of the H. sapiens proteins that are targeted by the stringent DDI-based prediction approach in Table Different from using the coherent informative GO annotation for the assessment of the human intra-species PPI dataset, we choose a different resolution of the GO terms for the category distribution analysis of human proteins involved in H. sapiens proteins involved in the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach. We use the DAVID database [>2, p-value <0.1"). On the other hand, as we have found in another work [M. tuberculosis H37Rv are not specific enough to provide effective functional enrichment analysis. Thus, the functional analysis of M. tuberculosis H37Rv proteins are not discussed in this work.Functional enrichment analysis is important for revealing the functional relevance of the proteins involved in the host-pathogen PPIs predicted by our stringent DDI-based approach. The presence of enriched(over-represented) functional categories that are closely related to pathogen infection, serves as a support for the validity of the predicted host-pathogen PPIs. The Gene Ontology  is a comprehensive functional annotation system. Therefore we conduct MF term enrichment analysis on the database  for the her work , most ofin vivo. Thus, pathway enrichment analysis is also one of the most frequently used strategy for analyzing predicted host-pathogen PPIs.Pathway data are very important functional information for identifying a list of proteins' overall related functions in a cell. For a set of proteins which is significantly enriched in some pathways, it is very likely that this set of proteins play similar or co-ordinated roles H. sapiens protein set predicted by the stringent DDI-based approach, the pathway enrichment analysis result is shown in Table We use the IntPath  databaseM. tuberculosis H37Rv proteins, because IntPath [M. tuberculosis H37Rv proteins involved in H. sapiens-M. tuberculosis H37Rv PPIs predicted by the stringent DDI-based approach is given in Table We also analyze the pathway enrichments for the  IntPath  also supM. tuberculosis H37Rv and H. sapiens involved in the predicted host-pathogen PPIs, and comparing them with other proteins in their own intra-species PPIN. As a control experiment, we also conduct the same analysis on the H. sapiens proteins in the gold standard H. sapiens-HIV PPIs [H. sapiens proteins in the gold standard H. sapiens-HIV PPIs exhibit similar properties.The analysis of protein domain properties considers the number of domains and the degrees of domains on proteins. The protein domain properties directly reflect differences between the proteins involved in inter-species host-pathogen PPIN and intra-species PPIN. We analyze the domain properties of both HIV PPIs  to see wM. tuberculosis H37Rv and H. sapiens protein domains is accomplished using HMMER-V3.0 [≤ E − 20 and accuracy ≥ 0.9. For each domain annotated on each protein, we retrieve the sequences of these domains on every protein for the following analysis.As the host-pathogen PPIs are predicted by the stringent DDI-based approach, to avoid biased analysis, we use a different domain annotation system in this analysis. The annotation of both MER-V3.0 . The domMER-V3.0 . The thrFor the domain degree analysis, we obtain the DDI(Domain-Domain Interaction) data from the DOMINE database. DDIs "inferred from PDB entries" and "high confidence predictions" in the DOMINE database are considered in this study, while "medium confidence predictions" and "low confidence predictions" are discarded. For each domain, we count the number of interaction partners in the DOMINE database(only "inferred from PDB entries" and "high confidence predictions") as the degree of that domain. We analyze the above protein domain properties and summarize the results in Table The software packages and database tools used in this study are:• IntPath • Cytoscape • InterPro • InterProScan • DAVID The datasets used in this study are:M. tuberculosis H37Rv PPI dataset consisting of four reliable subsets of the B2H PPI dataset and STRING PPI dataset(threshold at 770) [•  at 770) .H. sapiens physical PPI dataset collected from MINT [• rom MINT , BioGRIDrom MINT , and Introm MINT ; date ofS. cerevisiae physical PPI dataset collected from MINT [• rom MINT , BioGRIDrom MINT , and Introm MINT ; date of• Protein domain annotation (protein2ipr) from InterPro ; date of• DDI data from the 3did database , and treats all domain instances annotated under the same domain ID as the same. So a large number of PPIs can be predicted by the conventional DDI-based approach. We compare the performance of our stringent DDI-based approach and the conventional DDI-based approach based on gold standard PPI datasets and percentage of PPIs having coherent informative GO terms.Currently no large-scale high-quality H. sapiens physical PPI dataset from MINT [H. sapiens PPIs predicted by the stringent and the conventional DDI-based approaches. We calculate and plot the precision-recall curve of the stringent and the conventional DDI-based approaches; see Figure H. sapiens PPIs, 82 of which overlap with the gold standard; when the conventional DDI-based approach predicts 885 H. sapiens PPIs, only 11 of which overlap with the gold standard. Our stringent DDI-based approach has to predict 839 H. sapiens PPIs in order to have 82 H. sapiens PPIs overlapping with the gold standard. The conventional DDI-based approach has to predict 3085 H. sapiens PPIs in order to have 81 H. sapiens PPIs overlapping with the gold standard; see Table H. sapiens PPIs clearly show that our stringent DDI-based approach is more stringent and has better performance than that of the conventional DDI-based approach.We collect the known rom MINT , BioGRIDrom MINT , and Introm MINT  as our gTo further compare the performance of the stringent and the conventional DDI-based approaches, we calculate the percentage of PPIs that have coherent informative GO terms. From Figure H. sapiens proteins targeted by the host-pathogen PPIs predicted by our stringent DDI-based approach is an important indicator of the performance of the prediction approach and the quality of the H. sapiens-M. tuberculosis H37Rv PPIs predicted. If the targeted H. sapiens proteins are mostly located in cellular compartments having a close relationship with pathogen infection then the predicted results are more convincing. We identify the informative CC terms in H. sapiens proteins. Then we calculate the number and percentage of proteins in the datasets that have been annotated with each of the informative CC terms. Then we plot the located informative CC terms for the targeted H. sapiens proteins by the stringent DDI-based approach in Figure The cellular compartment distribution of the H. sapiens proteins located in very relevant cellular compartments. M. tuberculosis H37Rv infection has a close relationship with mitochondria activities and function and induces quantitatively distinct changes in the mitochondrial proteome [M. tuberculosis H37Rv infected cells [M. tuberculosis H37Rv enables manipulation of host cellular mechanisms to inhibit apoptosis and ensure fortification against anti-microbial pathways [M. tuberculosis H37Rv infection.Many of the host-pathogen PPIs predicted by the stringent DDI-based approach target proteome . Ultrasted cells . The augpathways . TherefoH. sapiens proteins located at flagellum (GO:0019861) have much higher chance of interacting with M. tuberculosis H37Rv during infection as proteins located at flagellum are the first set of proteins that M. tuberculosis H37Rv comes across before invading the cell.M. tuberculosis infection. It is found that the interaction between the mycobacterial phagosome and the endoplasmic reticulum leads to proteasome degradation and MHC class I presentation of M. tuberculosis antigens.The CC term peroxisomal part(GO:0044439) is also strongly related to M. tuberculosis infection process. In many bacterial pathogens, protein tyrosine phosphatases (PTPases) are essential for dephosphorylating host focal adhesion proteins and focal adhesion kinase. This dephosphorylation leads to destabilization of focal adhesions involved in the internalization of bacterial pathogens by eukaryotic cells [M. tuberculosis infection of host. This strongly supports the validity of the prediction results of our stringent DDI-based approach.Focal adhesion(GO:0005925) is also closely interconnected to the ic cells ,27. TherM. tuberculosis affects actin polymerisation [The cellular compartment lamellipodiumGO:0030027) also supports the validity of our prediction results. It has been reported that host cell's actin filament network is interfered by pathogenic species of mycobateria -30. A mo027 also risation .M. tuberculosis infection, as M. tuberculosis infection of human macrophages blocks several responses to IFN-γ. The inhibitory effect of M. tuberculosis is directed at the transcription of IFN-γ-responsive genes [M. tuberculosis and its purified protein derivative induced HIV LTR primarily through transcriptional activation [The CC term nucleolus(GO:0005730) may also be related to ve genes . Severaltivation .H. sapiens proteins targeted by host-pathogen PPIs strongly supports the validity of the PPI dataset predicted by our stringent DDI-based approach.The cellular compartment distribution analysis of the H. sapiens proteins involved in the H. sapiens-M. tuberculosis H37Rv PPIN predicted by the stringent DDI-based approaches. The representative result--the most significantly enriched level 5 MF GO terms--is given in Table Functional enrichment analysis points out the possible functional relevance of M. tuberculosis H37Rv infection, including adenyl ribonucleotide binding(GO:0032559), ATPase activity, coupled to transmembrane movement of substances (GO:0042626), P-P-bond-hydrolysis-driven transmembrane transporter activity(GO:0015405), ATPase activity, coupled to transmembrane movement of ions(GO:0042625), long-chain-acyl-CoA dehydrogenase activity(GO:0004466), NADPH:quinone reductase activity(GO:0003960), NADPH binding(GO:0070402), retinol dehydrogenase activity(GO:0004745), retinol binding(GO:0019841), and MHC class I protein binding(GO:0042288).Most of the significantly enriched functional categories are strongly related to M. tuberculosis H37Rv infection is closely related to the mitochondria. Therefore all those MF terms closely related to mitochondria are relevant to M. tuberculosis H37Rv infection; the relevant GO terms include ATPase activity, coupled to transmembrane movement of substances (GO:0042626), P-P-bond-hydrolysis-driven transmembrane transporter activity(GO:0015405), ATPase activity, coupled to transmembrane movement of ions(GO:0042625), NADPH:quinone reductase activity(GO:0003960), NADPH binding(GO:0070402).As described above, M. tuberculosis H37Rv infection. Proteins enriched in this term play an important role in presenting M. tuberculosis antigens, which is essential for the immune response to this pathogen.MHC class I protein binding(GO:0042288) is a strongly immune-related term which is also very relevant to M. tuberculosis H37Rv infection. Fatty acids and cholesterol appear to be the favored nutrients for M. tuberculosis inside H. sapiens cells [M. tuberculosis expands the acetyl-CoA pool and alleviates the pressure from propionyl-CoA [The long-chain-acyl-CoA dehydrogenase activity(GO:0004466) is a fatty acid-related term which is very relevant to ns cells . The brens cells . Throughonyl-CoA .H. sapiens proteins that are involved in H. sapiens-M. tuberculosis H37Rv PPIs.This functional enrichment analysis shows that our stringent DDI-based approach is accurate and has merits in identifying possible H. sapiens proteins involved in the H. sapiens-M. tuberculosis H37Rv PPIN predicted by the stringent DDI-based approach are mostly enriched in the pathways are closely relevant to M. tuberculosis infection; see Table M. tuberculosis infection as fatty acids are one of the favored nutrients for M. tuberculosis inside H. sapiens cells [M. tuberculosis is able to grow on a variety of carbon sources, but mounting evidence has implicated fatty acids as the major source of carbon and energy for M. tuberculosis during infection [M. tuberculosis switches its carbon source from sugars to fatty acids during the persistent phase of infection [M. tuberculosis infection as they are closely involved with intermediates of the tricarboxylic acid cycle which is essential for the growth of M. tuberculosis [M. tuberculosis metabolism inside the human cell.Pathway enrichment analysis of the proteins involved in host-pathogen PPIN can provide rich information on the functional relevance of (both the host and pathogen) proteins involved in the host-pathogen PPIN. The analysis should show that the host proteins involved in host-pathogen interactions is a set of proteins that have functional correlation to pathways relevant to the pathogen's infection. Indeed ns cells . M. tubenfection . And M. nfection . Biosyntnfection . The patrculosis . And theM. tuberculosis H37Rv proteins involved in the H. sapiens-M. tuberculosis H37Rv PPIN predicted by the stringent DDI-based approach are significantly enriched in the "Fatty Acid β oxidation I" pathway, see Table M. tuberculosis during infection [M. tuberculosis H37Rv. It is found that when the pathogen's acyl-coenzyme A synthetase gene is disrupted, infected mice survive significantly longer than those infected with the wild type, thus suggesting attenuation of the mutated pathogen. In fact the pathogen never attains the plateau phase of infection in mouse lungs when pathogen's acyl-coenzyme A synthetase gene is disrupted [M. tuberculosis fatty acyl-coenzyme A synthetase gene may serve to recycle mycolic acids for the long-term survival of the tubercle bacilli [nfection , and pat bacilli . This anH. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach. Therefore the prediction results from our stringent DDI-based approach can serve as a reliable reference of PPIs between H. sapiens and M. tuberculosis H37Rv.All the results support the validity of the H. sapiens and M. tuberculosis H37Rv proteins in the predicted H sapiens-M. tuberculosis H37Rv PPIN and their own intra-species PPIN. We also conduct a similar analysis on H. sapiens proteins involved in the gold standard H. sapiens-HIV PPIN [H. sapiens and M. tuberculosis H37Rv proteins. It is obvious that H. sapiens proteins targeted by the predicted H. sapiens-M. tuberculosis H37Rv PPIN show properties very similar to those H. sapiens proteins targeted by the gold standard H. sapiens-HIV PPIN [We compare two domain properties of both HIV PPIN  as a conHIV PPIN . This alH. sapiens-M. tuberculosis H37Rv PPIN and in the gold standard H. sapiens-HIV PPIN, H. sapiens proteins tend to have more domains and those domains tend to have higher degrees than those proteins in the intra-species H. sapiens PPIN.Both in the predicted The discoveries found by analyzing domain properties may be helpful in illuminating the basic mechanisms of how the host and pathogen proteins interact with each other, and may be useful in assessing the predicted host-pathogen PPIN.Comparing with conventional DDI-based approaches, our stringent DDI-based approach emphasizes the importance of domain instances in inferring interactions from template DDIs. While this emphasis on stringent sequence similarity between template and query domain instances in transferring interaction results in significant improvement on prediction performance, it also draws attention to the large sequence variation among domain instances which may limit conventional DDI-based approaches.It is also noteworthy that many new prediction algorithms based on the stringent alignment of domain instances can be proposed to predict possible intra- and inter-species PPIs.The advantages of our stringent DDI-based approach have been discussed above, as it can predict more accurate PPIs on a small scale. The possible limitation of this approach is the lack of large-scale high-quality structurally-resolved DDIs. However, it is reasonable to expect more protein complex structures will be resolved, and the effectiveness of our stringent DDI-based approach will consequently be significantly strengthened.Producing only a small amount of PPIs does not distract us from the merits of our stringent DDI-based approach, because the small number of highly accurate PPIs may already be more valuable than a huge amount of PPIs with a substantial fraction of noise. Highly accurate predicted PPIs, even though small in size, are usually very welcomed in experimental research, as they are a much more valuable reference for experimental verification than large datasets with high noise.Accurate sequence alignment among domain instances are much more computationally expensive than the conventional DDI-based approach. This may limit the application of our stringent DDI-based approach to large-scale prediction of PPIs across many host-pathogen systems.H. sapiens PPIs and informative GO annotation shows that the stringent DDI-based approach performs better than the conventional DDI-based approach. We have also predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs. Through cellular compartment distribution, functional enrichment, and pathway enrichment analysis, we have demonstrated that this small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs is valid and closely corresponds to M. tuberculosis H37Rv infection. This dataset of H. sapiens-M. tuberculosis H37Rv PPIs can be used for a variety of related studies as an important reference.In this work, we have proposed a stringent DDI-based prediction approach based on high sequence similarity between template domain instances and query domain instances. The assessment based on gold-standard The authors declare that they have no competing interests.This work was jointly conceived, planned, and written up by Limsoon Wong and Hufeng Zhou. The analytical experiments were performed by Hufeng Zhou. The DDI scoring strategy was proposed and implemented by Willy Hugo and Javad Rezaie. The physical PPI datasets were collected and prepared by Chern-Han Yong. The visualization of PPIN was done by Michal Wozniak. The functional analysis was proposed, suggested and implemented by Hufeng Zhou, Shangzhi Gao, Jingjing Jin and Mengyuan Fan. The statistical test was jointly implemented by Shangzhi Gao and Hufeng Zhou.http://compbio.ddns.comp.nus.edu.sg/~zhouhufeng/Research/DDIbased/data/.Interacting domain instances and structural information from 3did can be downloaded from: H. sapiens-M. tuberculosis H37Rv PPI datasets. We predicted H. sapiens-M. tuberculosis H37Rv PPIs using our stringent DDI-based prediction approach. The predicted PPI data are recorded in simple text format in additional file 1.Predicted Click here for file


The human Argonaute2 protein (Ago2) is a key player in RNA interference pathway and small RNA recognition by Ago2 is the crucial step in siRNA mediated gene silencing mechanism. The present study highlights the structural and functional dynamics of human Ago2 and the interaction mechanism of Ago2 with a set of seven siRNAs for the first time. The human Ago2 protein adopts two conformations such as “open” and “close” during the simulation of 25 ns. One of the domains named as PAZ, which is responsible for anchoring the 3′-end of siRNA guide strand, is observed as a highly flexible region. The interaction between Ago2 and siRNA, analyzed using a set of siRNAs  designed to target tdp43 mutants causing Amyotrophic Lateral Sclerosis (ALS) disease, revealed the stable and strong recognition of siRNA by the Ago2 protein during dynamics. Among the studied siRNAs, the siRNA C. elegans and later was also reported in various eukaryotes including human [ Neurospora crassa [The noncoding genes play important roles in regulatory processes and control gene expression via sequence specific interactions , 2. The ng human –4. In pla crassa –7. Both a crassa . The RNAa crassa . In RNAia crassa , 11.The siRNA is a duplex RNA (formed by both guide and passenger strands) having 21 to 25 nucleotides in length with two overhangs at both 3′- and 5′-ends and is generated when the double stranded longer RNAs are processed by the enzyme called dicer . Binding S. pombe, C. elegans, D. melanogaster, and various mammals have been carried out during the past few years [The complete mechanism of RNAi is not fully explored . The repew years . Ago proew years . Pyrococcus furiosus and Aquifex aeolicus, respectively, has been reported [ Thermus thermophilus and Rhodobacter spheroids revealed the gene silencing mechanism in prokaryotes [2+ ions, coordinating the catalytic triad formed by DDH motif in PIWI domain, play a catalytic role in the endonucleolytic cleavage of target mRNA. Mutation at the catalytic residues in active site fails to express slicer activity [The crystal structure of both archaeal and prokaryotic Ago proteins fromreported , 25. Reckaryotes , 27. Thekaryotes . Recentlkaryotes . Human Akaryotes . The MIDkaryotes . The twoactivity . In Ago,activity .To explore the role of Ago2 in line with RNAi mechanism, the incurable neurological disorder named as Amyotrophic Lateral Sclerosis (ALS) has been considered for this study. ALS is a fatal neurological disorder, involving the large motor neurons of brain and spinal cord. ALS is characterized by the progressive paralysis and subsequent death due to respiratory failure . The disKnowledge on the interaction of siRNAs with Ago2 protein gains more importance as they play a vital role in RNAi therapy. Very recently, a systematic analysis has been performed by our group to design and screen possible siRNAs to effectively target the C-terminal region of TDP43. Among the studied siRNAs, few of the identified potential lead candidates  [2+ to maintain the metal coordination. The relaxed structure of Ago2 was subjected to (i) qualitative analysis using SAVES server and (ii) secondary structure analysis using Ramachandran plot.The crystal structure of full length human Ago2 protein with a resolution of 2.3 Å was retrieved from Protein Data Bank (PDB ID: 4OLA). The missing loop regions were modeled using Sybyl biopolymer module. The divalent MgB: 3HVR)  using DiThe complex formation of any ligand with a biomolecule is effectively modeled using docking procedure. In a standard docking protocol, the ligand would identify a comfortable interaction site at the periphery of the biomolecule based on the interaction energy , 48.Modeling of the complex structures of Ago2-siRNA was carefully performed as the single stranded RNA is embedded in between the globular domain formed by the four domains such as NTD, PAZ, MID, and PIWI of Ago2 protein and particularly held by both PAZ and MID domains . As repoA crystal structure of Ago2 with guide RNA molecules (PDB ID: 4OLA superseded with 4EI1), reported by Schrile and MacRae , was useFor the present analysis, a set of seven siRNAs  designed to target tdp43 mutants causing ALS disease  have bee− counter ions. All the systems were simulated with periodic boundary conditions and were minimized for 50,000 cycles using steepest descent followed by conjugate gradient algorithms. The system was equilibrated for a period of 100 ps using NVT and NPT ensembles, respectively. During simulation, the temperature was maintained at 300 K using a coupling method called Berendsen thermostat to regulate the temperature [All atom molecular dynamic simulations were carried out for the modeled human Ago2 and Ago2-siRNA complexes using Gromacs 4.5.5 package  with AMBperature , and theperature . The lonperature  algorithRg have been measured and were analyzed using the tool Grace 6.0. The three-dimensional structures were visualized by UCSF Chimera 1.9 [The Ago2-siRNA complexes were also subjected to similar protocol of simulation and were analyzed for a period of 15 ns. The parameters explaining the conformational stability such as RMSD, RMSF, and mera 1.9  and the mera 1.9 . The intmera 1.9 .α-atoms. The cross correlation is given by ri and Δrj are the displacements from the mean position of ith and jth of Cα-atoms averaged over the trajectory [C = 1 represents the complete correlated motion  and C = −1 represents the completely anticorrelated motions .The concerted motions exerted by Ago2 protein during molecular dynamics simulation have been analyzed using the cross correlations expressed by the time dependent fluctuations of Cajectory , 58. TheGbind) of complex between human Ago2 and selected siRNAs was performed as follows: GAgo2-siRNA, GAgo2, and GsiRNA are free energies of Ago2-siRNA complex, Ago2, and siRNA, respectively. The individual components mentioned above are calculated as given below:EMM is molecular mechanical energy and GPB and GSA are polar and nonpolar terms from implicit calculation. The recently published tool “g_mmpbsa” was used for these calculations [ϕ(r) is electrostatic potential, ε(r) is dielectric constant, ρf(r) is the fixed charge density, and k2 is the reciprocal of Debye length independent of ionic strength of solution.The calculation of binding free energy  and A. aeolicus (1YVU) are reported [341) with Ago2 was further simulated for a period of 25 ns to explore the binding mechanism of siRNA guide strand with Ago2 protein.Several crystal structures of Ago proteins with guide strand from various species includingreported , 25, 30.reported . In the reported . The strAs the first phase of study, the dynamics of Ago2 protein was observed for a period of 25 ns to understand its structural dynamics. The variation in RMSD observed during simulation is shown in http://dx.doi.org/10.1155/2016/8792814). Among the studied complexes, the Ago2-siRNA251 and Ago-siRNA341 express the least and highest RMSD , while the rest of the complexes fluctuate between 2.5 and 3.5 Å. The root mean square fluctuations of the Cα-atoms in all Ago2-siRNA complexes have been analyzed. All these complexes expressed similar pattern of fluctuation . The residues of Ago2-siRNA251,383,537,1113,1115 complexes show higher fluctuations than other studied complexes. It is also seen that the flexible dynamics of Ago2 are highly driven by the fluctuation of the PAZ domain of Ago2.All atom simulations have also been performed on siRNA bound Ago2 complexes . Among the studied complexes, one of the complexes, formed between siRNA341 and Ago2 protein, revealed a highly stable dynamics over other complex systems and hence the conformational evolution of this Ago2-siRNA341 complex was compared with the dynamics of free Ago2 protein  and reveals stable dynamics after 15 ns. The stable dynamics of all Ago2-siRNA complexes are reinforced by the stable variation in the Rg value (3.11 ± 0.4 nm). Among the selected siRNAs, the siRNA341 is identified as the strongly bound substrate with Ago2 (converging at 3.02 nm of Rg). For a comparison analysis, the Rg value of Ago2, in the presence and absence of siRNA341, is plotted in 341 bound forms shows stable dynamics after 17 ns with Rg less than 3.1 nm . The open and close conformations of Ago2 are depicted in 341 by the close conformation of Ago2 protein is shown in The structural compactness of Ago2 protein in both free and complexed form was monitored during simulation by calculating the “Rg values.The domains such as N-terminal, PAZ, MID, and PIWI also reveal stable dynamics during simulation and are shown in Supplementary Figure 2. The spatial arrangement of these stable domains in Ago2 protein was analyzed by calculating the interdomain distance using a representative atom from the respective stable domains  and is shown in Supplementary Figure 3. It is observed that the MID domain maintains the interdomain distance with both PIWI and NTD domains, whereas the instantaneous positional change in PAZ domain results in a fluctuation as well as decrease in 341 is identified as the most stable complex as evidenced by RMSD, RMSF, and Rg analyses . Most of the complexes are held stable by several strong H-bonding interactions (about 25 to 40) of distance less than 3 Å. A 2D representation of Ago2-siRNA interactions is analyzed using NUCPLOT software [Among the studied complexes, the complex Ago2-siRNAsoftware  and the 341 complex during simulation. In this 2D representation, the red, blue, purple, grey, green, and yellow colors represent the β-sheet, α-helix, π-helix, “310” helix, bend, and turn, respectively.The changes observed in the secondary structure of Ago2 in free and siRNA bound forms during simulation have been analyzed using DSSP program . Supplemα” helices at N-terminal is slightly distorted and adopts the turn conformation, whereas the antiparallel “β”-sheets remain unchanged. The N-terminal helix formed by residues between Arg68 and His81 in free Ago2 protein loses its helicity in the close conformation of Ago2 while retaining its helicity intact in open conformation. The PAZ domain plays a key role in anchoring the 3′-end of guide strand, and the “β”-sheets in this flexible domain expressed a moderate structural disturbance. The 310 helix formed by Thr307-Asp314 residues showed minor changes without altering its helicity during simulation. In the open conformation of Ago2, two short lived 310-helices are formed by the residues (i) Pro229-Val237 and (ii) Thr251-Ile262 of the MID domain that holds the 5′-end of the guide strand. The MID domain also expresses the formation of a “π” helix by the residues Pro527-Thr538, which vanishes after 17 ns. The C-terminal PIWI domain of Ago2 undergoes a major conformational change during simulation.The structure of free Ago2 undergoes a relaxation mechanism in which its secondary structure settles down in a tender fashion. Some of the observed structural relaxations are discussed in this paragraph. The helical nature of “10-helix present at the terminal PIWI domain undergoes fluctuation and changes its conformation into α-helix at the end of simulation. The movement of PAZ domain towards mid domain is responsible for this conformational change. PIWI domain has a catalytic triad (DDH) and these conformational changes are necessary to promote the orientation of the target mRNA with which the complementary siRNA would pair and facilitate the catalytic cleavage by nucleophilic attack.The helicity of two short C-terminal helices such as His839-Gln847 and Gln850-Arg854 in the PIWI domain is altered during the dynamics of open conformation of Ago2. One of these helices, Gln850-Arg854, regains its helical conformation from 17 ns onwards. The bend located at the terminal region of PIWI domain  disappears after 10 ns of simulation. The 3341 complex. The residues of Ago2 involved in interactions with siRNA expressed stable dynamics of the secondary structure compared to those of free Ago2. The N-terminal helix formed by the residues Arg68-His81 is reduced for a short period of time (within 5-6 ns) to Val70-His81 and thereafter maintains its helicity throughout the simulation. In the bound form of Ago2  the helix formed between the residues Arg-68-His81 is changed to Arg69-Gln80. The Arg68 residue is actively engaged in the electrostatic interaction with siRNA. Neighboring residues like Glu64, Lys65, and Pro67 are also participating in the nonbonded interactions  with siRNA. The interactions of these residues with siRNAs disturb the nature of this helix. A single short lived 310 helix is formed by the residues Pro585-Gln588 in the catalytic PIWI domain. The helix formed by the residues Pro373-Arg384 from linker L2 is unfolded during the opening and closing motions of both PAZ and MID domains in bound Ago2, while this helix retain its helicity during simulation in free Ago2.Similarly, the secondary structure analysis has also been performed for the Ago2 protein in Ago2-siRNAIn summary, secondary structure of Ago2 in both open and close conformations retains its structural stability during the simulation, though there are minor conformational changes. The observed conformational changes in the secondary structures of MID and PAZ domains of Ago2 are attributed to the flexible nature of the conformation of Ago2 protein to sense the guide strand of siRNA for the stable binding with target mRNA.The analysis of interdomain H-bonds revealed strong H-bonding interactions between the domains of Ago2. Ago2 expresses different pattern of interdomain H-bonds in both “open” and “close” conformations. Throughout the simulation, the MID and PIWI domains of free Ago2 are stabilized by about 28 interdomain H-bonding interactions. The N-terminal and PAZ domains show only 8 interdomain H-bonds. The interaction between N-terminal and PAZ domains is further decreased, when the PAZ domain moves towards MID domain  to attain the close conformation of Ago2. Hence, the short lived close conformation of Ago2 is governed by the movement of flexible PAZ domain. The interdomain distance calculated using the representative residue from each domain gives insight into the spatial distribution of all the domains of Ago2 during simulation and is shown in Supplementary Figure 3. The distance between (i) MID and PIWI domains (shown in black line) and (ii) MID and N-terminal domains (shown in red line) remains stable throughout the simulation . As the flexible PAZ domain mediates both the open and close conformations of Ago2 protein (which facilitates further anchoring of guide siRNA between PAZ and MID domains), the distance between PAZ and NTD fluctuates during simulation. The observed flexible nature of PAZ domain has also been reported by various experimental and theoretical studies –65.341 bound Ago2 protein has been performed to understand the binding mechanism of siRNA with Ago2. 341 bound Ago2 protein . The diagonal line (amber color) expresses the strong autocorrelation between Ago2 residues. The spectrum of correlated to anticorrelated motions is scaled from amber to blue, respectively.The correlation map representing the intra- as well as interdomain motions has been generated from the stable trajectory observed at the last 5 ns of simulation using Amber tool 14.0 , 67. A cIn free Ago2, the domains including NTD, PAZ, and MID express strong autocorrelation, whereas the C-terminal PIWI domain expresses a comparatively lesser autocorrelation. The regions NTD and linker L1 (Ser139-Pro229) and few residues from L2  are involved in a concerted motion between themselves. Both MID and PIWI domains are involved in a less amplitude cooperative motion. The catalytic residues Asp597, Asp669, and His807 (DDH motif) ensure a conserved evolution during simulation. At the same time, a part of PAZ domain  is not interactive with NTD. Overall, the free Ago2 expresses significant cooperative as well as anticooperative domain motions during dynamics and the anticooperative motion observed between PAZ and NTD domains delocalizes the PAZ domain closer to the MID domain and facilitates the transition to “close” state of Ago2.When siRNA binds to Ago2, (i) the degree of autocorrelation in NTD is decreased, and the linker L1 moves along with PIWI to bring the seed region closer to catalytic triad, (ii) a high amplitude autocorrelation is observed with PAZ domain, when compared to both MID and PIWI, and (iii) the PAZ and NTD move opposite to each other; that is, the flexible PAZ domain moves towards MID domain to accommodate the longer guide strand of siRNA and such closure movement of PAZ domain towards MID domain is responsible for the anticooperative motion of PAZ domain with NTD. The helix (formed by Pro527-Asp537) of MID domain (where 5′-end of siRNA is anchored) shows strong correlation with PAZ domain that holds the 3′-end of siRNA. Overall, both MID and PAZ domains are involved in anticooperative/scissoring motion with which the Ago2 adopts either open or close conformations. Binding of siRNA inhibits this anticooperative/scissoring motion of these domains and establishes a cooperative motion between them in order to facilitate its binding to Ago2 and hence stabilizes the complex at close conformation .Protein domain motion analysis was performed for the representative average frames extracted from both open (within 12-13 ns) and close (within 18-19 ns) conformations of Ago2 protein using DynDom server. The free Ago2 expresses two domains such as (i) the moving PAZ domain (from Tyr225 to Arg351) for which the residues Phe224-Tyr225 and Arg351-Cys352 act as hinge and (ii) the fixed domain formed by N-terminal, MID, and PIWI domains. In α), beta (β), gamma (γ), delta (δ), epsilon (ε), and zeta (ξ)) of the single stranded siRNA reveals that, after the complexation with Ago2, the guide strand siRNA is stabilized in A-conformation , which is reported as one of the necessary factors for an efficient RNAi/gene silencing [The conformation of single stranded siRNA in the presence of Ago2 has great significance in RNAi therapy and was analyzed for all Ago2-siRNA complexes using DSSR software , 69. Anailencing . The pseIt is observed that binding of siRNA with Ago2 is highly mediated by H-bonding interactions. The interaction between siRNA and Ago2 was analyzed by considering the three fragments of RNA such as base, sugar, and phosphate group and the interacting sites are tabulated in The nonbonded contacts such as Van der Waals and electrostatics interactions majorly contribute to form a stronger interaction between the Ago2 and siRNAs in all studied complexes. In addition, several salt bridges are also observed between the positively charged residues  and the negatively charged phosphate group of siRNA  in Ago2-siRNA complexes. All these bonded and nonbonded interactions are shown in Supplementary Figure 4. Mainly, residues such as Lysine, Arginine, Aspartate, and Glutamine present at the PAZ domain form significant H-bonds with the 3′-end of siRNA guide strand. These observed interacting residues are in agreement with the reported siRNA binding residues . The PAZThe 5′- and 3′-terminal bases of siRNA are involved in maximum number of H-bonding interactions as well as other nonbonded interactions (such as Vdw and electrostatics) and allow the rest of bases to interact freely for sensing mRNA in a complementary fashion. The flexibility of PAZ domain is responsible for the release of 3′-end of guide strand. The dynamic PAZ domain also facilitates the access of siRNA bases to the target mRNA. Superimposition of all siRNAs to depict their positioning inside Ago2 protein after complexation is shown in Supplementary Figure 7.341 shows more affinity towards Ago2  whereas the siRNA1115 shows less affinity  and highlights the sequence specific binding affinity of siRNA towards Ago2.Binding efficiency of the selected siRNAs with Ago2 was analyzed by calculating the relative binding free energy using MMPBSA method . The cal341 despite the observed high RMSD during dynamics. Unlike other siRNAs, the 7–18 nucleosides of siRNA341 are positioned closer to the linker region of both N-terminal and PAZ domains and establish stronger interactions with Ago2. Such strong interactions facilitate further strong affinity towards Ago2 and hence promote least binding energy for this complex. From the simulated data, the siRNA341 is identified as the efficient siRNA among the studied siRNAs. Such stronger as well as favorable interactions between Ago2 and siRNA are responsible for successful RNA interference.It is interesting to observe a highly favorable (most negative) binding energy for siRNATwo experimentally validated siRNAs, which are evaluated to target the ICAM2 and GRK genes, were used to form complex with Ago2 protein and are compared to the complexes of Ago2 with the rationally designed siRNAs to target the tdp43 mutants. The complexes of these two experimentally proven siRNAs with Ago2 protein expressed stable dynamics similar to those of Ago2 complexes with the siRNAs designed to target the tdp43 mutants. And the interaction patterns expressed by the experimentally proven siRNAs with Ago2 are significantly comparable to that of siRNAs used for the present study. The potential of newly designed siRNAs in sensing the Ago2 protein was analyzed in comparison with these experimentally reported siRNAs, by performing binding free energy analysis. The siRNAs designed to target ICAM2 and GRK genes express the binding free energy values −4846.78 and −4610.42 kcal/mol, respectively, with Ago2 protein, which are in the similar range of siRNAs designed to target mutant tdp43. In the complexes of Ago2 with both siRNAs (designed to target mutant tdp43 and target ICAM2 and GRK genes), the energy components such as van der Waals, electrostatic, and solvation parameters that explicitly define the efficiency of siRNA to interact with Ago2 also vary in agreement with each other. As the binding free energy for the experimentally reported siRNAs and rationally designed siRNAs falls in the same range, we could infer that the rationally identified siRNAs designed to target the mutant tdp43 are also expected to perform equally well in gene silencing phenomenon as that of experimentally reported siRNAs.The human Ago2 protein plays a key role in the sequence specific cleavage of target mRNA in RNAi pathway. The dynamics of complete human Ago2 protein have been studied for the first time, to understand (i) the global dynamics of Ago2 and the role of individual domains for its functional dynamics and (ii) the interactions of Ago2 with siRNAs (which are designed to target tdp43 mutants causing ALS disease) with a view to highlight mRNA recognition by siRNA. The molecular dynamics study performed for a period of 25 ns revealed the conformational transition of Ago2 between open and close conformations promoted by the flexible PAZ domain to accommodate the siRNAs (which range in length within 20–28 base pairs) between the MID and PAZ domains. It was also ensured by the molecular dynamics of complex Ago2-siRNA that the close conformation promotes stable siRNA binding. It is also observed that the catalytic DDH triad from the PIWI domain moves towards the seed region of the target to facilitate further cleavage of target mRNA at the seed region (10-11 bases). Dynamics analysis on the interaction of Ago2 with these selected 7 siRNAs  reveals that the studied siRNAs have significant specificity towards Ago2 and form stable complex.341 as the most compatible siRNA among the studied ones.In the complex, the 3′-end of siRNA is held by the PAZ domain by forming H-bonds with Gln297, Arg315, Arg277, and Asp125 residues of Ago2, whereas the 5′-end interacts with Gln548, Pro523, Thr526, Asn551, Lys566, Asn562, Gln545, Tyr529, Tye790, and Arg792 residues of MID domain via stable H-bonds. The rest of siRNA nucleotides positioned on the inner cleft  are freely available to sense mRNA during RNAi. The binding free energy analysis reveals siRNAIn summary, knowledge on the structure and dynamics of human Ago2 protein and its interactions with siRNA would promote further understanding of RNAi mechanism in human, which is not yet explored significantly. The lack of efficient treatment for the neurological ALS disorder further necessitates the identification of alternatives to the existing treatment. Success rate of gene therapy, especially using rational design of siRNA, has increased drastically in the past few years and has no/fewer side effects. Hence, the present observations highlight the possible potent/more efficient siRNAs against tdp43 mutants in RNAi mechanism.Supplementary material contains total 7 figures which are highlighted in the manuscript. Supplementary Figures 1 and 2 represent the parameters explaining the structural stability observed during simulations. Supplementary Figures 3 and 4 show the inter domain distance in free Ago2 and site specific interactions between Ago2 and siRNA, respectively. Changes in the secondary structures are given in the supplementary Figure 5. Supplementary Figures 6 and 7 represent the three dimensional conformations.


Dear Editor,A multi-domain protein consists of two or more well-folded domains connected by flexible linkers, which may lead to large scale inter-domain motions related to the protein function. Therefore, it is appropriate to represent a flexible multi-domain protein by an ensemble of structures containing multiple conformational states  is a structural component of the mammalian spliceosomal A/B complex and has functionality in pre-mRNA splicing  is located at around 16.3 Å, but the maximal distance Dmax can be longer than 62.0 Å  of the protein estimated from Guinier analysis is about 19.0 Å. For each structural model in the NMR ensemble, we back-calculated its theoretical SAXS curve and get the average values (Eqn. S2). The SAXS profile of the NMR ensemble . Starting from model 1 in the NMR ensemble, a 2 μs MD simulation was carried out (denoted as single-2µs). From each of the 20 models, two 100 ns MD simulations were run independently. These short simulations were then combined into two 2-μs trajectories . Intra- and inter-domain motions were firstly estimated by computing root mean square deviations (RMSD) of different MD trajectories with respect to their starting structures. The RMSD of the two domains , which has a χ of 0.18 when fitting the experimental data