Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

The pH stability of foot-and-mouth disease virus

?

This review summarized the molecular determinants of the acid stability of FMDV in order to explore the uncoating mechanism of FMDV and improve the acid stability of vaccines.

Background

The foot-and-mouth disease virus (FMDV) capsid is highly acid labile and tends to dissociate into pentameric subunits at acidic condition to release viral RNA for initiating virus replication. However, the acid stability of virus capsid is greatly required for the maintenance of intact virion during the process of virus culture and vaccine production. The conflict between the acid lability in vivo and acid stability in vitro of FMDV capsid promotes the selection of a series of amino acid substitutions which can confer resistance to acid-induced FMDV inactivation. In order to explore the uncoating activity of FMDV and enhance the acid stability of vaccines, we summarized the available works about the pH stability of FMDV.

Main body of the abstract

In this review, we analyzed the intrinsic reasons for the acid instability of FMDV from the structural and functional aspects. We also listed all substitutions obtained by different research methods and showed them in the partial capsid of FMDV. We found that a quadrangle region in the viral capsid was the place where a great many pH-sensitive residues were distributed. As the uncoating event of FMDV is dependent on the pH-sensitive amino acid residues in the capsid, this most pH-sensitive position indicates a potential candidate location for RNA delivery triggered by the acid-induced coat disassociation.

Short conclusion

This review provided an overview of the pH stability of FMDV. The study of pH stability of FMDV not only contributes to the exploration of molecule and mechanism information for FMDV uncoating, but also enlightens the development of FMDV vaccines, including the traditionally inactivated vaccines and the new VLP (virus-like particle) vaccines.

     

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.

     

Evaluation of texture features at staging liver fibrosis based on phase contrast X-ray imaging

Background

The purpose of this study is to explore the potential of phase contrast imaging to detect fibrotic progress in its early stage; to investigate the feasibility of texture features for quantified diagnosis of liver fibrosis; and to evaluate the performance of back propagation (BP) neural net classifier for characterization and classification of liver fibrosis.

Methods

Fibrous mouse liver samples were imaged by X-ray phase contrast imaging, nine texture measures based on gray-level co-occurrence matrix were calculated and the feasibility of texture features in the characterization and discrimination of liver fibrosis at early stages was investigated. Furthermore, 36 or 18 features were applied to the input of BP classifier; the classification performance was evaluated using receiver operating characteristic curve.

Results

The phase contrast images displayed a vary degree of texture pattern from normal to severe fibrosis stages. The BP classifier could distinguish liver fibrosis among normal, mild, moderate and severe stages; the average accuracy was 95.1% for 36 features, and 91.1% for 18 features.

Conclusion

The study shows that early stages of liver fibrosis can be discriminated by the morphological features on the phase contrast images. BP network model based on combination of texture features is demonstrated effective for staging liver fibrosis.

     

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.

     

An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction

Background

Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences.

Methods

This paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice.

Results

The simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy.

Conclusions

Thanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction.

     

Identification and characterization of a novel antioxidant peptide from feather keratin hydrolysate

Objectives

To improve the potential value of feather, which is a valuable protein resource, we have separated and identified antioxidant peptide(s) from feather hydrolysate.

Results

Feather hydrolysate was prepared by fermentation with Bacillus subtilis S1–4. Antioxidative peptides were separated by sequential acid precipitation, cation exchange, and reversed-phase fast performance liquid chromatography. Finally, a peptide with antioxidative activity was identified as Ser-Asn-Leu-Cys-Arg-Pro-Cys-Gly by MALDI time-of-flight (TOF)/TOF analysis, and determined to represent a portion of feather keratin near its N-terminal. A synthesized peptide with the same sequence was used to characterize its antioxidative properties, including scavenging free radicals, reducing power, and Fe²⁺ chelation. In terms of the peptide’s amino acid composition, the antioxidative activity might be mainly attributed to Cys and other amino acid residues.

Conclusion

Feather keratin is a good source for the quantitative preparation of antioxidative peptides.

     

Prediction of enhancer-promoter interactions via natural language processing

Background

Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput.

Results

We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~?0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~?0.940 can be achieved by combining sequence embedding features and experimental features.

Conclusions

EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.

     

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.

     

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.

     

Interactions of in vitro selected fluorogenic peptide aptamers with calmodulin

Objectives

We examined the importance of aptamer usage under the same condition as the selection process by employing the previously selected aptamers for calmodulin (CaM) which includes a non-natural fluorogenic amino acid, 7-nitro-2,1,3-benzoxadiazole.

Results

We added five amino acids at the N-terminus which was employed for the selection and then we tested the affinity and selectivity for CaM binding. Surface plasmon resonance and fluorescence measurements showed that the additional amino acids for one of the aptamers drastically improved binding affinity to CaM, indicating the importance of aptamer use under the same conditions as the selection process. Such drastic improvement in affinity was not observed for the sequence which had been reported previously. Nuclear magnetic resonance data identified that the primary binding site is located in a C-terminal of CaM and the additional residues enhance interactions with CaM.

Conclusions

We found that the addition of the common sequence, which was employed for ribosome display, makes the affinity of a selected peptide as strong as the previously reported peptide.

     

Gene mention normalization and interaction extraction with context models and sentence motifs

Background:

The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.

Results:

We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.

Conclusion:

For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.

Availability:

Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see http://bcms.bioinfo.cnio.es/.

     

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.

     

Expression and purification of classical swine fever virus E2 protein from Sf9 cells using a modified vector

Objective

To develop a simple method for efficient expression of classical swine fever virus (CSFV) E2 protein.

Results

The pFastBac HT B vector (pFastHTB-M1) was modified by adding a melittin signal peptide sequence. The E2 gene fragment without the transmembrane region was cloned into pFastHTB-M1. The modified vector has clear advantage over the original one, as evidenced by the purified recombinant E2 protein that was detected significantly by SDS-PAGE.

Conclusions

The modified vector has the potential for large-scale production and easy purification of the CSFV E2 protein or other proteins of interests.

     

Metabolic risks of neonates at birth following in utero exposure to HIV-ART: the amino acid profile of cord blood

Introduction

Untargeted metabolomics of cord blood indicated that antiretroviral therapy to HIV-infected mothers (HIV-ART) did not compromise the exposed neonates with regard to the stress of neonatal hypoglycaemia at birth. However, identified biomarkers reflected stress in their energy metabolism, raising concern over developmental risks in some newborns exposed to ART.

Objectives

This study addresses the concern over HIV-ART-induced metabolic perturbations by expanding the metabolomics study to the amino acid profiles in cord blood collected at birth from newborns either exposed or unexposed to HIV-ART in utero.

Methods

Amino acid profiles derived from liquid chromatographic triple quadruple spectra of cord blood from neonates exposed and unexposed to HIV-ART (cohort 1) were investigated using a metabolomics approach. Amino acid data, generated by ultra performance liquid chromatography–tandem mass spectrometry from similar cases (cohort 2), were included for comparison.

Results

Multivariate and supporting statistics indicated differentiation between the exposed and unexposed neonates in both cohorts, caused by a general decrease or downregulation of amino acid concentrations in the cord blood samples from the exposed cases. Specifically, significant upregulation of aspartic acid in both cohorts and downregulation of arginine, and of threonine, tryptophan and lysine in cohorts 1 and 2, respectively, were observed.

Conclusions

The benefits of ART for HIV-infected pregnant women are well established. However, the amino acid profile of cord blood, obtained from the two independent cohorts, adds to observed metabolic risks of in utero HIV-ART-exposed newborns. These risks could potentially have adverse consequences for the future health of some exposed infants.