A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins

Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases

Phospholipases, as important lipolytic enzymes, have diverse industrial applications. Regarding the stability of extremophilic archaea's proteins in harsh conditions, analyses of unusual features of their proteins are significantly important for their utilization. This research was accomplished to in silico study of archaeal phospholipases' properties and to develop a pioneering method for distinguishing these enzymes from other archaeal enzymes via machine learning algorithms and Chou's pseudo-amino acid composition concept. The non-redundant sequences of archaeal phospholipases were collected. BioSeq-Analysis sever was used with Support Vector Machine (SVM), Random Forests (RF), Covariance Discrimination (CD), and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) as powerful machine learnings algorithms. Also, different Chou's pseudo-amino acid composition modes were performed and then, 5-fold cross-validation was applied to the sequences. Based on our results, the OET-KNN predictor, with 96% accuracy, yields the best performance in SC-PseAAC mode by 5-fold cross-validation. This predictor also achieved very high values of specificity (95%), sensitivity (96%), Matthews's correlation coefficient (0.92), and accuracy (96%). The present investigation yielded a robust anticipatory model for the archaeal phospholipase prediction utilizing the tenets PseAAC and OET-KNN machine learning algorithm.

Virtual screening on an α-helix to β-strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators

The secondary structure of some protein segments may vary between α-helix and β-strand. To predict these switchable segments, we have developed an algorithm, Switch-P, based solely on the protein sequence. This algorithm was used on the extracellular parts of FGF receptors. For FGFR2, it predicted that β4 and β5 strands of the third Ig-like domain were highly switchable. These two strands possess a high number of somatic mutations associated with cancer. Analysis of PDB structures of FGF receptors confirmed the switchability prediction for β5. We thus evaluated if compound-driven α-helix/β-strand switching of β5 could modulate FGFR2 signaling. We performed the virtual screening of a library containing 1.4 million of chemical compounds with two models of the third Ig-like domain of FGFR2 showing different secondary structures for β5, and we selected 32 compounds. Experimental testing using proliferation assays with FGF7-stimulated SNU-16 cells and a FGFR2-dependent Erk1/2 phosphorylation assay with FGFR2-transfected L6 cells, revealed activators and inhibitors of FGFR2. Our method for the identification of switchable proteinic regions, associated with our virtual screening approach, provides an opportunity to discover new generation of drugs with under-explored mechanism of action.

Transcriptomic characterization of the novel avian-origin influenza A (H7N9) virus: specific host response and responses intermediate between avian (H5N1 and H7N7) and human (H3N2) viruses and implications for treatment options

A novel avian-origin H7N9 influenza A virus (IAV) emerged in China in 2013, causing mild to lethal human respiratory infections. H7N9 originated with multiple reassortment events between avian viruses and carries genetic markers of human adaptation. Determining whether H7N9 induces a host response closer to that with human or avian IAV is important in order to better characterize this emerging virus. Here we compared the human lung epithelial cell response to infection with A/Anhui/01/13 (H7N9) or highly pathogenic avian-origin H5N1, H7N7, or human seasonal H3N2 IAV. The transcriptomic response to H7N9 was highly specific to this strain but was more similar to the response to human H3N2 than to that to other avian IAVs. H7N9 and H3N2 both elicited responses related to eicosanoid signaling and chromatin modification, whereas H7N9 specifically induced genes regulating the cell cycle and transcription. Among avian IAVs, the response to H7N9 was closest to that elicited by H5N1 virus. Host responses common to H7N9 and the other avian viruses included the lack of induction of the antigen presentation pathway and reduced proinflammatory cytokine induction compared to that with H3N2. Repression of these responses could have an important impact on the immunogenicity and virulence of H7N9 in humans. Finally, using a genome-based drug repurposing approach, we identified several drugs predicted to regulate the host response to H7N9 that may act as potential antivirals, including several kinase inhibitors, as well as FDA-approved drugs, such as troglitazone and minocycline. Importantly, we validated that minocycline inhibited H7N9 replication in vitro, suggesting that our computational approach holds promise for identifying novel antivirals.

IMPORTANCE

Whether H7N9 will be the next pandemic influenza virus or will persist and sporadically infect humans from its avian reservoir, similar to H5N1, is not known yet. High-throughput profiling of the host response to infection allows rapid characterization of virus-host interactions and generates many hypotheses that will accelerate understanding and responsiveness to this potential threat. We show that the cellular response to H7N9 virus is closer to that induced by H3N2 than to that induced by H5N1, reflecting the potential of this new virus for adaptation to humans. Importantly, dissecting the host response to H7N9 may guide host-directed antiviral development.

Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine

The amino acid gamma-aminobutyric-acid receptors (GABA(A)Rs) belong to the ligand-gated ion channels (LGICs) superfamily. GABA(A)Rs are highly diverse in the central nervous system. These channels play a key role in regulating behavior. As a result, the prediction of GABA(A)Rs from the amino acid sequence would be helpful for research on these receptors. We have developed a method to predict these proteins using the features obtained from Chou's pseudo-amino acid composition concept and support vector machine as a powerful machine learning approach. The predictor efficiency was assessed by five-fold cross-validation. This method achieved an overall accuracy and Matthew's correlation coefficient (MCC) of 94.12% and 0.88, respectively. Furthermore, to evaluate the effect and power of each feature, the minimum Redundancy and Maximum Relevance (mRMR) feature selection method was implemented. An interesting finding in this study is the presence of all six characters (hydrophobicity, hydrophilicity, side chain mass, pK1, pK2 and pI) or combination of the characters among the 5 higher ranked features (pk2 and pI, hydrophobicity and mass, pk1, hydrophilicity and mass) obtained from the mRMR feature selection method. The results show a biologically justifiable ranked attributes of pk2 and pI; hydrophobicity, hydrophilicity and mass; mass and pk1; pk2 and mass. Based on our results, using the concept of Chou's pseudo-amino acid composition and support vector machine is an effective approach for the prediction of GABA(A)Rs.

Extending Asia Pacific bioinformatics into new realms in the "-omics" era

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation dating back to 1998, was organized as the 8^th International Conference on Bioinformatics (InCoB), Sept. 7-11, 2009 at Biopolis, Singapore. Besides bringing together scientists from the field of bioinformatics in this region, InCoB has actively engaged clinicians and researchers from the area of systems biology, to facilitate greater synergy between these two groups. InCoB2009 followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India), Hong Kong and Taipei (Taiwan), with InCoB2010 scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010. The Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and symposia on Clinical Bioinformatics (CBAS), the Singapore Symposium on Computational Biology (SYMBIO) and training tutorials were scheduled prior to the scientific meeting, and provided ample opportunity for in-depth learning and special interest meetings for educators, clinicians and students. We provide a brief overview of the peer-reviewed bioinformatics manuscripts accepted for publication in this supplement, grouped into thematic areas. In order to facilitate scientific reproducibility and accountability, we have, for the first time, introduced minimum information criteria for our pubilcations, including compliance to a Minimum Information about a Bioinformatics Investigation (MIABi). As the regional research expertise in bioinformatics matures, we have delineated a minimum set of bioinformatics skills required for addressing the computational challenges of the "-omics" era.