Structure-based Alignment Research Articles

Structure of a DNA Polymerase α-Primase Domain That Docks on the SV40 Helicase and Activates the Viral Primosome

DNA polymerase alpha-primase (pol-prim) plays a central role in DNA replication in higher eukaryotes, initiating synthesis on both leading and lagging strand single-stranded DNA templates. Pol-prim consists of a primase heterodimer that synthesizes RNA primers, a DNA polymerase that extends them, and a fourth subunit, p68 (also termed B-subunit), that is thought to regulate the complex. Although significant knowledge about single-subunit primases of prokaryotes has accumulated, the functions and regulation of pol-prim remain poorly understood. In the SV40 replication model, the p68 subunit is required for primosome activity and binds directly to the hexameric viral helicase T antigen, suggesting a functional link between T antigen-p68 interaction and primosome activity. To explore this link, we first mapped the interacting regions of the two proteins and discovered a previously unrecognized N-terminal globular domain of p68 (p68N) that physically interacts with the T antigen helicase domain. NMR spectroscopy was used to determine the solution structure of p68N and map its interface with the T antigen helicase domain. Structure-guided mutagenesis of p68 residues in the interface diminished T antigen-p68 interaction, confirming the interaction site. SV40 primosome activity of corresponding pol-prim mutants decreased in proportion to the reduction in p68N-T antigen affinity, confirming that p68-T antigen interaction is vital for primosome function. A model is presented for how this interaction regulates SV40 primosome activity, and the implications of our findings are discussed in regard to the molecular mechanisms of eukaryotic DNA replication initiation.

Discovery and Characterization of an Amidinotransferase Involved in the Modification of Archaeal tRNA

The presence of the 7-deazaguanosine derivative archaeosine (G(+)) at position 15 in tRNA is one of the diagnostic molecular characteristics of the Archaea. The biosynthesis of this modified nucleoside is especially complex, involving the initial production of 7-cyano-7-deazaguanine (preQ(0)), an advanced precursor that is produced in a tRNA-independent portion of the biosynthesis, followed by its insertion into the tRNA by the enzyme tRNA-guanine transglycosylase (arcTGT), which replaces the target guanine base yielding preQ(0)-tRNA. The enzymes responsible for the biosynthesis of preQ(0) were recently identified, but the enzyme(s) catalyzing the conversion of preQ(0)-tRNA to G(+)-tRNA have remained elusive. Using a comparative genomics approach, we identified a protein family implicated in the late stages of archaeosine biosynthesis. Notably, this family is a paralog of arcTGT and is generally annotated as TgtA2. Structure-based alignments comparing arcTGT and TgtA2 reveal that TgtA2 lacks key arcTGT catalytic residues and contains an additional module. We constructed a Haloferax volcanii DeltatgtA2 derivative and demonstrated that tRNA from this strain lacks G(+) and instead accumulates preQ(0). We also cloned the corresponding gene from Methanocaldococcus jannaschii (mj1022) and characterized the purified recombinant enzyme. Recombinant MjTgtA2 was shown to convert preQ(0)-tRNA to G(+)-tRNA using several nitrogen sources and to do so in an ATP-independent process. This is the only example of the conversion of a nitrile to a formamidine known in biology and represents a new class of amidinotransferase chemistry.

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

BackgroundWhile the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.ResultsWe compared near-optimal protein sequence alignments produced by the Zuker algorithm and a set of probabilistic alignments produced by the probA program with structural alignments produced by four different structure alignment algorithms. There is significant overlap between the solution spaces of structural alignments and both the near-optimal sequence alignments produced by commonly used scoring parameters for sequences that share significant sequence similarity (E-values < 10-5) and the ensemble of probA alignments. We constructed a logistic regression model incorporating three input variables derived from sets of near-optimal alignments: robustness, edge frequency, and maximum bits-per-position. A ROC analysis shows that this model more accurately classifies amino acid pairs (edges in the alignment path graph) according to the likelihood of appearance in structural alignments than the robustness score alone. We investigated various trimming protocols for removing incorrect edges from the optimal sequence alignment; the most effective protocol is to remove matches from the semi-global optimal alignment that are outside the boundaries of the local alignment, although trimming according to the model-generated probabilities achieves a similar level of improvement. The model can also be used to generate novel alignments by using the probabilities in lieu of a scoring matrix. These alignments are typically better than the optimal sequence alignment, and include novel correct structural edges. We find that the probA alignments sample a larger variety of alignments than the Zuker set, which more frequently results in alignments that are closer to the structural alignments, but that using the probA alignments as input to the regression model does not increase performance.ConclusionsThe pool of suboptimal pairwise protein sequence alignments substantially overlaps structure-based alignments for pairs with statistically significant similarity, and a regression model based on information contained in this alignment pool improves the accuracy of pairwise alignments with respect to structure-based alignments.

ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment

Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation.Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites.Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.siContact: dusa@cmm.ki.siSupplementary information: Supplementary data are available at Bioinformatics online.

Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison

BackgroundDetection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.ResultsHere, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at http://www.ibt.lt/bioinformatics/coma.ConclusionDue to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.

Structural definition and substrate specificity of the S28 protease family: the crystal structure of human prolylcarboxypeptidase

BackgroundThe unique S28 family of proteases is comprised of the carboxypeptidase PRCP and the aminopeptidase DPP7. The structural basis of the different substrate specificities of the two enzymes is not understood nor has the structure of the S28 fold been described.ResultsThe experimentally phased 2.8 Å crystal structure is presented for human PRCP. PRCP contains an α/β hydrolase domain harboring the catalytic Asp-His-Ser triad and a novel helical structural domain that caps the active site. Structural comparisons with prolylendopeptidase and DPP4 identify the S1 proline binding site of PRCP. A structure-based alignment with the previously undescribed structure of DPP7 illuminates the mechanism of orthogonal substrate specificity of PRCP and DPP7. PRCP has an extended active-site cleft that can accommodate proline substrates with multiple N-terminal residues. In contrast, the substrate binding groove of DPP7 is occluded by a short amino-acid insertion unique to DPP7 that creates a truncated active site selective for dipeptidyl proteolysis of N-terminal substrates.ConclusionThe results define the structure of the S28 family of proteases, provide the structural basis of PRCP and DPP7 substrate specificity and enable the rational design of selective PRCP modulators.

Finding of residues crucial for supersecondary structure formation

This work evaluates the hypothesis that proteins with an identical supersecondary structure (SSS) share a unique set of residues--SSS-determining residues--even though they may belong to different protein families and have very low sequence similarities. This hypothesis was tested on two groups of sandwich-like proteins (SPs). Proteins in each group have an identical SSS, but their sequence similarity is below the "twilight zone." To find the SSS-determining residues specific to each group, a unique structure-based algorithm of multiple sequences alignment was developed. The units of alignment are individual strands and loops rather than whole sequences. The algorithm is based on the alignment of residues that form hydrogen bonds between corresponding strands. Structure-based alignment revealed that 30-35% of the positions in the sequences in each group of proteins are "conserved positions" occupied either by hydrophobic-only or hydrophilic-only residues. Moreover, each group of SPs is characterized by a unique set of SSS-determining residues found at the conserved positions. The set of SSS-determining residues has very high sensitivity and specificity for identifying proteins with a corresponding SSS: It is an "amino acid tag" that brands a sequence as having a particular SSS. Thus, the sets of SSS-determining residues can be used to classify proteins and to predict the SSS of a query amino acid sequence.

Nearly complete rRNA genes assembled from across the metazoan animals: Effects of more taxa, a structure-based alignment, and paired-sites evolutionary models on phylogeny reconstruction

This study (1) uses nearly complete rRNA-gene sequences from across Metazoa (197 taxa) to reconstruct animal phylogeny; (2) presents a highly annotated, manual alignment of these sequences with special reference to rRNA features including paired sites (http://purl.oclc.org/NET/rRNA/Metazoan_alignment) and (3) tests, after eliminating as few disruptive, rogue sequences as possible, if a likelihood framework can recover the main metazoan clades. We found that systematic elimination of ∼6% of the sequences, including the divergent or unstably placed sequences of cephalopods, arrowworm, symphylan and pauropod myriapods, and of myzostomid and nemertodermatid worms, led to a tree that supported Ecdysozoa, Lophotrochozoa, Protostomia, and Bilateria. Deuterostomia, however, was never recovered, because the rRNA of urochordates goes (nonsignificantly) near the base of the Bilateria. Counterintuitively, when we modeled the evolution of the paired sites, phylogenetic resolution was not increased over traditional tree-building models that assume all sites in rRNA evolve independently. The rRNA genes of non-bilaterians contain a higher % AT than do those of most bilaterians. The rRNA genes of Acoela and Myzostomida were found to be secondarily shortened, AT-enriched, and highly modified, throwing some doubt on the location of these worms at the base of Bilateria in the rRNA tree—especially myzostomids, which other evidence suggests are annelids instead. Other findings are marsupial-with-placental mammals, arrowworms in Ecdysozoa (well supported here but contradicted by morphology), and Placozoa as sister to Cnidaria. Finally, despite the difficulties, the rRNA-gene trees are in strong concordance with trees derived from multiple protein-coding genes in supporting the new animal phylogeny.

Reverse transcriptases of retroviruses and retroelements: an evolutionary perspective

Reverse transcriptases (RT) are central to retrovirology (as the key enzymes encoded by retroviruses) as well as to evolutionary genomics (as the only unifying component for numerous types of retroelements inhabiting genomic DNA in both prokaryotes and eukaryotes). A great deal of effort has been put into understanding the evolutionary relationships between RT-containing entities, including retroviruses, retrotransposons, and reverse transcriptase-related genes such as telomerases. However, there is still no consensus on phylogenetic history of retroelements [1-3]. The recent explosion in the amounts of genome sequence data from evolutionarily diverse organisms provides us with a rich source of novel RT-related sequences, which may be regarded as evolutionary intermediates and ''missing links'' connecting seemingly unrelated groups of reverse transcriptases. Our genomic surveys of rotifers of the Class Bdelloidea and comparisons with data from other sequenced genomes provided us with a number of examples of novel RT sequences fitting this description, including both published [3-5] and unpublished findings. Structure-based alignments and comparisons of RT-associated N- and C-terminal extension domains in newly identified groups of retroelements reveal deeper evolutionary connections between different RT classes than previously uncovered by phylogenetic analyses of the core RT domain spanning the seven most highly conserved motifs. Reverse transcriptase-related genes appear to be more common than previously thought, and may have independently given rise to mobile genetic elements several times in early evolution, via subsequent association with various catalytic domains able to cleave target genomic DNA and thereby confer intragenomic mobility to retroelement-encoded RNAs.

Structural basis for the nuclease activity of a bacteriophage large terminase

The DNA-packaging motor in tailed bacteriophages requires nuclease activity to ensure that the genome is packaged correctly. This nuclease activity is tightly regulated as the enzyme is inactive for the duration of DNA translocation. Here, we report the X-ray structure of the large terminase nuclease domain from bacteriophage SPP1. Similarity with the RNase H family endonucleases allowed interactions with the DNA to be predicted. A structure-based alignment with the distantly related T4 gp17 terminase shows the conservation of an extended β-sheet and an auxiliary β-hairpin that are not found in other RNase H family proteins. The model with DNA suggests that the β-hairpin partly blocks the active site, and in vivo activity assays show that the nuclease domain is not functional in the absence of the ATPase domain. Here, we propose that the nuclease activity is regulated by movement of the β-hairpin, altering active site access and the orientation of catalytically essential residues.

SECISaln, a web-based tool for the creation of structure-based alignments of eukaryotic SECIS elements

Summary: Selenoproteins contain the 21st amino acid selenocysteine which is encoded by an inframe UGA codon, usually read as a stop. In eukaryotes, its co-translational recoding requires the presence of an RNA stem–loop structure, the SECIS element in the 3 untranslated region of (UTR) selenoprotein mRNAs. Despite little sequence conservation, SECIS elements share the same overall secondary structure. Until recently, the lack of a significantly high number of selenoprotein mRNA sequences hampered the identification of other potential sequence conservation. In this work, the web-based tool SECISaln provides for the first time an extensive structure-based sequence alignment of SECIS elements resulting from the well-defined secondary structure of the SECIS RNA and the increased size of the eukaryotic selenoproteome. We have used SECISaln to improve our knowledge of SECIS secondary structure and to discover novel, conserved nucleotide positions and we believe it will be a useful tool for the selenoprotein and RNA scientific communities.Availability: SECISaln is freely available as a web-based tool at http://genome.crg.es/software/secisaln/.Contact: charles.chapple@crg.esSupplementary information: Supplementary data are available at Bioinformatics online.

Domain evolution in the GH13 pullulanase subfamily with focus on the carbohydrate-binding module family 48

Glycoside hydrolase (GH) family 13 comprises about 30 different specificities. Four of them have been proposed to form the GH13 pullulanase subfamily: pullulanase, isoamylase, maltooligosyl trehalohydrolase and branching enzyme forming the seven CAZy GH13 subfamilies: GH13 8-GH13 14. Recently, a new family of carbohydrate-binding modules (CBMs), the family CBM48 has been established containing the putative starch-binding domains from the pullulanase subfamily, the β-subunit of AMP-activated protein kinase and some other GH13 enzymes with pullulanase and/or α-amylase-pullulanase specificity. Since all of these enzymes are multidomain proteins and the structure for at least one representative of each enzyme specificity has already been determined, the main goal of the present study was to elucidate domain evolution within this GH13 pullulanase subfamily (84 real enzymes) focusing on the CBM48 module. With regard to CBM48 positioning in the amino acid sequence, the N-terminal end of a protein appears to be a predominant position. This is especially true for isoamylases and maltooligosyl trehalohydrolases. Secondary structure-based alignment of CBM modules from CBM48, CBM20 and CBM21 revealed that several residues known as consensus for CBM20 and CBM21 could also be identified in CBM48, but only branching enzymes possess the aromatic residues that correspond with the two tryptophans forming the evolutionary conserved starch-binding site 1 in CBM20. The evolutionary trees constructed for the individual domains, complete alignment, and the conserved sequence regions of the α-amylase family were found to be comparable to each other (except for the C-domain tree) with two basic parts: (i) branching enzymes and maltooligosyl trehalohydrolases; and (ii) pullulanases and isoamylases. Taxonomy was respected only within clusters with pure specificity, i.e. the evolution of CBM48 reflects the evolution of specificities rather than evolution of species. This is a feature different from the one observed for the starch-binding domain of the family CBM20 where the starch-binding domain evolution reflects the evolution of species.

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble

Motivation: So far various bioinformatics and machine learning techniques applied for identification of sequence and functionally conserved residues in proteins. Although few computational methods are available for the prediction of structurally conserved residues from protein structure, almost all methods require homologous structural information and structure-based alignments, which still prove to be a bottleneck in protein structure comparison studies. In this work, we developed a neural network approach for identification of structurally important residues from a single protein structure without using homologous structural information and structural alignment.Results: A neural network ensemble (NNE) method that utilizes negative correlation learning (NCL) approach was developed for identification of structurally conserved residues (SCRs) in proteins using features that represent amino acid conservation and composition, physico-chemical properties and structural properties. The NCL-NNE method was applied to 6042 SCRs that have been extracted from 496 protein domains. This method obtained high prediction sensitivity (92.8%) and quality (Matthew's correlation coefficient is 0.852) in identification of SCRs. Further benchmarking using 60 protein domains containing 1657 SCRs that were not part of the training and testing datasets shows that the NCL-NNE can correctly predict SCRs with ∼ 90% sensitivity. These results suggest the usefulness of NCL-NNE for facilitating the identification of SCRs utilizing information derived from a single protein structure. Therefore, this method could be extremely effective in large-scale benchmarking studies where reliable structural homologs and alignments are limited.Availability: The executable for the NCL-NNE algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SCR.htmContact: epnsugan@ntu.edu.sg; chakraba@ncbi.nlm.nih.gov.Supplementary information: Supplementary data are available at Bioinformatics online.

Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors

Docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis were performed on a wide set of c-Src inhibitors. The study was conducted using a structure-based alignment and by applying the GRID/GOLPE approach. The present 3D-QSAR investigation proved to be of good statistical value, displaying r 2, q 2 and cross-validation SDEP values of 0.94, 0.84 and 0.42, respectively. Moreover, such a model also proved to be capable of predicting the activities of an external test set of compounds. The availability of the 3D structure of the target made possible the interpretation of steric and electrostatic maps within the binding site environment and provided useful insight into the structural requirements for inhibitory activity against c-Src. Two regions whose occupation by hydrophobic portions of ligands would favourably affect the activity were clearly identified. Moreover, hydrogen bond interactions involving residues Met343, Asp406 and Ser347 emerged as playing a key role in determining the affinity of the active inhibitors toward c-Src. Furthermore, the inhibitors bearing a basic nitrogen provided enhanced potency through protonation and salt bridge formation with Asp350. A preliminary pharmacokinetic profile of the molecules under analysis was also drawn on the basis of Volsurf predictions.

Implementing the LIM code: the structural basis for cell type-specific assembly of LIM-homeodomain complexes

LIM-homeodomain (LIM-HD) transcription factors form a combinatorial 'LIM code' that contributes to the specification of cell types. In the ventral spinal cord, the binary LIM homeobox protein 3 (Lhx3)/LIM domain-binding protein 1 (Ldb1) complex specifies the formation of V2 interneurons. The additional expression of islet-1 (Isl1) in adjacent cells instead specifies the formation of motor neurons through assembly of a ternary complex in which Isl1 contacts both Lhx3 and Ldb1, displacing Lhx3 as the binding partner of Ldb1. However, little is known about how this molecular switch occurs. Here, we have identified the 30-residue Lhx3-binding domain on Isl1 (Isl1(LBD)). Although the LIM interaction domain of Ldb1 (Ldb1(LID)) and Isl1(LBD) share low levels of sequence homology, X-ray and NMR structures reveal that they bind Lhx3 in an identical manner, that is, Isl1(LBD) mimics Ldb1(LID). These data provide a structural basis for the formation of cell type-specific protein-protein interactions in which unstructured linear motifs with diverse sequences compete to bind protein partners. The resulting alternate protein complexes can target different genes to regulate key biological events.

Structure of the C-terminal head domain of the fowl adenovirus type 1 short fibre

There are more than 100 known adenovirus serotypes, including 50 human serotypes. They can infect all 5 major vertebrate classes but only Aviadenovirus infecting birds and Mastadenovirus infecting mammals have been well studied. CELO (chicken embryo lethal orphan) adenovirus is responsible for mild respiratory pathologies in birds. Most studies on CELO virus have focussed on its genome sequence and organisation whereas the structural work on CELO proteins has only recently started. Contrary to most adenoviruses, the vertices of CELO virus reveal pentons with two fibres of different lengths. The distal parts (or head) of those fibres are involved in cellular receptor binding. Here we have determined the atomic structure of the short-fibre head of CELO (amino acids 201–410) at 2.0 Å resolution. Despite low sequence identity, this structure is conserved compared to the other adenovirus fibre heads. We have used the existing CELO long-fibre head structure and the one we show here for a structure-based alignment of 11 known adenovirus fibre heads which was subsequently used for the construction of an evolutionary tree. Both the fibre head sequence and structural alignments suggest that enteric human group F adenovirus 41 (short fibre) is closer to the CELO fibre heads than the canine CAdV-2 fibre head, that lies closer to the human virus fibre heads.

Structure‐based approach to pharmacophore identification, in silico screening, and three‐dimensional quantitative structure–activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

Mining small RNA structure elements in untranslated regions of human and mouse mRNAs using structure-based alignment

BackgroundUnTranslated Regions (UTRs) of mRNAs contain regulatory elements for various aspects of mRNA metabolism, such as mRNA localization, translation, and mRNA stability. Several RNA stem-loop structures in UTRs have been experimentally identified, including the histone 3' UTR stem-loop structure (HSL3) and iron response element (IRE). These stem-loop structures are conserved among mammalian orthologs, and exist in a group of genes encoding proteins involved in the same biological pathways. It is not known to what extent RNA structures like these exist in all mammalian UTRs.ResultsIn this paper we took a systematic approach, named GLEAN-UTR, to identify small stem-loop RNA structure elements in UTRs that are conserved between human and mouse orthologs and exist in multiple genes with common Gene Ontology terms. This approach resulted in 90 distinct RNA structure groups containing 748 structures, with HSL3 and IRE among the top hits based on conservation of structure.ConclusionOur result indicates that there may exist many conserved stem-loop structures in mammalian UTRs that are involved in coordinate post-transcriptional regulation of biological pathways.

Distinguishing Molecular Features and Clinical Characteristics of a Putative New Rhinovirus Species, Human Rhinovirus C (HRV C)

BackgroundHuman rhinoviruses (HRVs) are the most frequently detected pathogens in acute respiratory tract infections (ARTIs) and yet little is known about the prevalence, recurrence, structure and clinical impact of individual members. During 2007, the complete coding sequences of six previously unknown and highly divergent HRV strains were reported. To catalogue the molecular and clinical features distinguishing the divergent HRV strains, we undertook, for the first time, in silico analyses of all available polyprotein sequences and performed retrospective reviews of the medical records of cases in which variants of the prototype strain, HRV-QPM, had been detected.Methodology/Principle FindingsGenomic analyses revealed that the six divergent strains, residing within a clade we previously called HRV A2, had the shortest polyprotein of all picornaviruses investigated. Structure-based amino acid alignments identified conserved motifs shared among members of the genus Rhinovirus as well as substantive deletions and insertions unique to the divergent strains. Deletions mostly affected regions encoding proteins traditionally involved in antigenicity and serving as HRV and HEV receptor footprints. Because the HRV A2 strains cannot yet be cultured, we created homology models of predicted HRV-QPM structural proteins. In silico comparisons confirmed that HRV-QPM was most closely related to the major group HRVs. HRV-QPM was most frequently detected in infants with expiratory wheezing or persistent cough who had been admitted to hospital and required supplemental oxygen. It was the only virus detected in 65% of positive individuals. These observations contributed to an objective clinical impact ranging from mild to severe.ConclusionsThe divergent strains did not meet classification requirements for any existing species of the genus Rhinovirus or Enterovirus. HRV A2 strains should be partitioned into at least one new species, putatively called Human rhinovirus C, populated by members detected with high frequency, from individuals with respiratory symptoms requiring hospital admission.