HAPIscreen,a method for high-throughput aptamer identification

Background

Aptamers are oligonucleotides displaying specific binding properties for a predetermined target. They are selected from libraries of randomly synthesized candidates through an in vitro selection process termed SELEX (Systematic Evolution of Ligands by EXponential enrichment) alternating selection and amplification steps. SELEX is followed by cloning and sequencing of the enriched pool of oligonucleotides to enable comparison of the selected sequences. The most represented candidates are then synthesized and their binding properties are individually evaluated thus leading to the identification of aptamers. These post-selection steps are time consuming and introduce a bias to the expense of poorly amplified binders that might be of high affinity and are consequently underrepresented. A method that would circumvent these limitations would be highly valuable.

Results

We describe a novel homogeneous solution-based method for screening large populations of oligonucleotide candidates generated from SELEX. This approach, based on the AlphaScreen^® technology, is carried out on the exclusive basis of the binding properties of the selected candidates without the needs of performing a priori sequencing. It therefore enables the functional identification of high affinity aptamers. We validated the HAPIscreen (High throughput APtamer Identification screen) methodology using aptamers targeted to RNA hairpins, previously identified in our laboratory. We then screened pools of candidates issued from SELEX rounds in a 384 well microplate format and identify new RNA aptamers to pre-microRNAs.

Conclusions

HAPIscreen, an Alphascreen^®-based methodology for the identification of aptamers is faster and less biased than current procedures based on sequence comparison of selected oligonucleotides and sampling binding properties of few individuals. Moreover this methodology allows for screening larger number of candidates. Used here for selecting anti-premiR aptamers, HAPIscreen can be adapted to any type of tagged target and is fully amenable to automation.

     

Development of RNA aptamer that inhibits methyltransferase activity of dengue virus

Objectives

To develop an RNA aptamer specific for the methyltransferase (MTase) of dengue virus (DENV) which is essential for viral genome replication and translation acting directly on N-7 and 2′-O-methylation of the type-I cap structure of the viral RNA.

Results

We identified 2′-fluoro-modified RNA aptamers that can specifically bind DENV serotype 2 (DENV2) MTase using systematic evolution of ligands by exponential enrichment technology. We truncated the chosen aptamer into a 45-mer RNA sequence that can bind DENV2 MTase with K _d  ~ 28 nM and inhibit N-7 methylation activity of the protein. Moreover, the 45-mer truncated aptamer could not only bind with an K _d  ~ 15.6 nM but also inhibit methylation activity of DENV serotype 3 (DENV3) MTase. The 45-mer aptamer competitively impeded binding of both DENV2 and DENV3 genomic RNA to MTase of each serotype.

Conclusion

The selected 45-mer truncated RNA aptamer specifically and avidly bound DENV MTase and competitively inhibited its methylation activity, and thus could be useful for the development of anti-DENV agents.

     

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Background

The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

Methods

We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

Results

Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

Conclusions

The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

     

Nucleotide binding domain 1 pharmacophore modeling for visualization and analysis of P-glycoprotein–flavonoid molecular interactions

Background

P-glycoprotein (P-gp) is a 170-kDa membrane protein. It provides a barrier function and help to excrete toxins from the body as a transporter. Some bioflavonoids have been shown to block P-gp activity.

Objective

To evaluate the important amino acid residues within nucleotide binding domain 1 (NBD1) of P-gp that play a key role in molecular interactions with flavonoids using structure-based pharmacophore model.

Methods

In the molecular docking with NBD1 models, a putative binding site of flavonoids was proposed and compared with the site for ATP. The binding modes for ligands were achieved using LigandScout to generate the P-gp–flavonoid pharmacophore models.

Results

The binding pocket for flavonoids was investigated and found these inhibitors compete with the ATP for binding site in NBD1 including the NBD1 amino acid residues identified by the in silico techniques to be involved in the hydrogen bonding and van der Waals (hydrophobic) interactions with flavonoids.

Conclusion

These flavonoids occupy with the same binding site of ATP in NBD1 proffering that they may act as an ATP competitive inhibitor.

     

A modified yeast three-hybrid system enabling both positive and negative selections

Objective

To increase the reporter repertoire of the yeast three-hybrid system and introduce the option of negative selection.

Results

Two new versions of the yeast three-hybrid system were made by modifying the MS2 coat RNA-binding protein and fusing it to the Gal4 DNA-binding protein. This allows the use of Gal4 inducible reporters to measure RNA–protein interactions. We introduced two mutations, V29I and N55K into the tandem MS2 dimer and an 11 amino acid deletion to increase RNA–protein affinity and inhibit capsid formation. Introduction of these constructs into the yeast strains MaV204K and PJ69-2A (which contain more reporters than the conventional yeast three-hybrid strains L40-coat and YBZ-1) allows RNA–protein binding interactions with a wide range of affinities to be detected using histidine auxotrophy, and negative selection with 5-fluoroorotic acid.

Conclusion

This yeast three-hybrid system has advantages over previous versions as demonstrated by the increased dynamic range of detectable binding interactions using yeast survival assays and colony forming assays with multiple reporters using known RNA–protein interactions.

     

Pranlukast,a novel binding ligand of human Raf1 kinase inhibitory protein

Objectives

To study the binding of pranlukast to hRKIP and its regulatory role in the Raf1/MEK/ERK signal pathway.

Results

NMR and fluorescence experiments demonstrated hRKIP could bind pranlukast with a binding constant of 1016 mM^?1. Residues (Y81, S109 and Y181) on the conserved ligand-binding pocket of hRKIP played a crucial role in binding pranlukast, and their mutations reduced the binding affinity more than 85 %. Furthermore, 25 μM pranlukast could up-regulate the ERK phosphorylation by about 17 %.

Conclusion

Pranlukast may be used as a potential drug precursor for treating hRKIP involved diseases.

     

How the chemical features of molecules may have addressed the settlement of metabolic steps

Introduction

While the evolutionary adaptation of enzymes to their own substrates is a well assessed and rationalized field, how molecules have been originally selected in order to initiate and assemble convenient metabolic pathways is a fascinating, but still debated argument.

Objectives

Aim of the present study is to give a rationale for the preferential selection of specific molecules to generate metabolic pathways.

Methods

The comparison of structural features of molecules, through an inductive methodological approach, offer a reading key to cautiously propose a determining factor for their metabolic recruitment.

Results

Starting with some commonplaces occurring in the structural representation of relevant carbohydrates, such as glucose, fructose and ribose, arguments are presented in associating stable structural determinants of these molecules and their peculiar occurrence in metabolic pathways.

Conclusions

Among other possible factors, the reliability of the structural asset of a molecule may be relevant or its selection among structurally and, a priori, functionally similar molecules.

     

Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes

Background

Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology.

Methods

We have developed an energy based approach for identifying the binding site residues in protein–protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching.

Results

We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix.

Conclusions

The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Pseudomyxoma extraperitonei occurring 35 years after appendicectomy: a case report and review of literature

Background

Pseudomyxoma peritonei is a rare condition consisting of mucinous ascites, most commonly arising from mucinous tumors of the appendix and occasionally from the ovary. Very rarely mucinous implants arise in the retroperitoneum without any intra-peritoneal involvement. This has been termed as pseudomyxoma extraperitonei.

Case presentation

We report a case of a 57 year old man who developed pseudomyxoma extraperitonei, 35 years after undergoing an appendicectomy for a perforated appendix.

Conclusions

Pseudomyxoma extraperitonei has been previously reported, however we report the longest incubation period of 35 years for this condition.

     

A lack of peptide binding and decreased thermostability suggests that the CASKIN2 scaffolding protein SH3 domain may be vestigial

Background

CASKIN2 is a neuronal signaling scaffolding protein comprised of multiple ankyrin repeats, two SAM domains, and one SH3 domain. The CASKIN2 SH3 domain for an NMR structural determination because its peptide-binding cleft appeared to deviate from the repertoire of aromatic enriched amino acids that typically bind polyproline-rich sequences.

Results

The structure demonstrated that two non-canonical basic amino acids (K290/R319) in the binding cleft were accommodated well in the SH3 fold. An K290Y/R319W double mutant restoring the typical aromatic amino acids found in the binding cleft resulted in a 20 °C relative increase in the thermal stability. Considering the reduced stability, we speculated that the CASKIN2 SH3 could be a nonfunctional remnant in this scaffolding protein.

Conclusions

While the NMR structure demonstrates that the CASKIN2 SH3 domain is folded, its cleft has suffered two substitutions that prevent it from binding typical polyproline ligands. This observation led us to additionally survey and describe other SH3 domains in the Protein Data Bank that may have similarly lost their ability to promote protein-protein interactions.

     

Variation of the microbiota and metabolome along the canine gastrointestinal tract

Introduction

The fecal microbiota are relevant to the health and disease of many species. The importance of the fecal metabolome has more recently been appreciated, but our knowledge of the microbiota and metabolome at other sites along the gastrointestinal tract remains deficient.

Objective

To analyze the gastrointestinal microbiota and metabolome of healthy domestic dogs at four anatomical sites.

Methods

Samples of the duodenal, ileal, colonic, and rectal contents were collected from six adult dogs after humane euthanasia for an unrelated study. The microbiota were characterized using Illumina sequencing of 16S rRNA genes. The metabolome was characterized by mass spectrometry-based methods.

Results

Prevalent phyla throughout the samples were Proteobacteria, Firmicutes, Fusobacteria, and Bacteroidetes, consistent with previous findings in dogs and other species. A total of 530 unique metabolites were detected; 199 of these were identified as previously named compounds, but 141 of them had at least one significantly different site-pair comparison. Noteworthy examples include relative concentrations of amino acids, which decreased from the small to large intestine; pyruvate, which peaked in the ileum; and several phenol-containing carboxylic acid compounds that increased in the large intestine.

Conclusion

The microbiota and metabolome vary significantly at different sites along the canine gastrointestinal tract.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Engineered protein A ligands,derived from a histidine-scanning library,facilitate the affinity purification of IgG under mild acidic conditions

Background

In antibody purification processes, the acidic buffer commonly used to elute the bound antibodies during conventional affinity chromatograph, can damage the antibody. Herein we describe the development of several types of affinity ligands which enable the purification of antibodies under much milder conditions.

Results

Staphylococcal protein A variants were engineered by using both structure-based design and combinatorial screening methods. The frequency of amino acid residue substitutions was statistically analyzed using the sequences isolated from a histidine-scanning library screening. The positions where the frequency of occurrence of a histidine residue was more than 70% were thought to be effective histidine-mutation sites. Consequently, we identified PAB variants with a D36H mutation whose binding of IgG was highly sensitive to pH change.

Conclusion

The affinity column elution chromatograms demonstrated that antibodies could be eluted at a higher pH (?pH**≧2.0) than ever reported (?pH?=?1.4) when the Staphylococcal protein A variants developed in this study were used as affinity ligands. The interactions between Staphylococcal protein A and IgG-Fab were shown to be important for the behavior of IgG bound on a SpA affinity column, and alterations in the affinity of the ligands for IgG-Fab clearly affected the conditions for eluting the bound IgG. Thus, a histidine-scanning library combined with a structure-based design was shown to be effective in engineering novel pH-sensitive proteins.

     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

     

Profiling of faecal water and urine metabolites among Papua New Guinea highlanders believed to be adapted to low protein intake

Introduction

Adequate amount of proteins from foods are normally needed to maintain muscle mass of the human body. Although protein intakes of Papua New Guinea (PNG) highlanders are less than biologically adequate, protein deficiency related disorders have rarely been reported. It has been postulated that gut microbiota play a role in such low-protein-adaptation.

Objective

To explore underlying biological mechanisms of low-protein adaptation among PNG highlanders by investigating metabolomic profiles of faecal water and urine.

Methods

We performed metabolome analysis using faecal water extracted from faecal samples of PNG highlanders, PNG non-highlanders and Japanese subjects. We paid special attention to amino acids and other metabolites produced by gut microbiota, as well as to metabolites involved in nitrogen recycling in the human gut.

Results

Our results indicated that amino acid levels were higher in faecal water from PNG highlanders than PNG non-highlanders, but amino acid levels did not differ between PNG highlanders and Japanese subjects. Among PNG highlander samples, amino acid levels tended to be higher in those who consumed less protein.

Conclusion

We speculated that a greater proportion of urea was excreted to the intestine among the PNG highlanders than other groups, and that the urea was used for nitrogen salvage. Intestinal bacteria are essential for producing ammonia from urea and also for producing amino acids from ammonia, which is a key process in low-protein adaptation. Profiling the gut microbiota of PNG highlanders is an important avenue for further research into the mechanisms of low-protein adaptation.

     

A protocol for CABS-dock protein–peptide docking driven by side-chain contact information

Background

The characterization of protein–peptide interactions is a challenge for computational molecular docking. Protein–peptide docking tools face at least two major difficulties: (1) efficient sampling of large-scale conformational changes induced by binding and (2) selection of the best models from a large set of predicted structures. In this paper, we merge an efficient sampling technique with external information about side-chain contacts to sample and select the best possible models.

Methods

In this paper we test a new protocol that uses information about side-chain contacts in CABS-dock protein–peptide docking. As shown in our recent studies, CABS-dock enables efficient modeling of large-scale conformational changes without knowledge about the binding site. However, the resulting set of binding sites and poses is in many cases highly diverse and difficult to score.

Results

As we demonstrate here, information about a single side-chain contact can significantly improve the prediction accuracy. Importantly, the imposed constraints for side-chain contacts are quite soft. Therefore, the developed protocol does not require precise contact information and ensures large-scale peptide flexibility in the broad contact area.

Conclusions

The demonstrated protocol provides the extension of the CABS-dock method that can be practically used in the structure prediction of protein–peptide complexes guided by the knowledge of the binding interface.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.