Prediction of structural features and application to outer membrane protein identification

Analysis of a new negevirus-like sequence from Bemisia tabaci unveils a potential new taxon linking nelorpi- and centiviruses

This study presents the complete genome sequence of a novel nege-like virus identified in whiteflies (Bemisia tabaci MEAM1), provisionally designated as whitefly negevirus 1 (WfNgV1). The virus possesses a single-stranded RNA genome comprising 11,848 nucleotides, organized into four open reading frames (ORFs). These ORFs encode the putative RNA-dependent-RNA-polymerase (RdRp, ORF 1), a glycoprotein (ORF 2), a structural protein with homology to those in the SP24 family, (ORF 3), and a protein of unknown function (ORF 4). Phylogenetic analysis focusing on RdRp and SP24 amino acid sequences revealed a close relationship between WfNgV1 and Bemisia tabaci negevirus 1, a negevirus sequence recently discovered in whiteflies from Israel. Both viruses form a clade sharing a most recent common ancestor with the proposed nelorpivirus and centivirus taxa. The putative glycoprotein from ORF 2 and SP24 (ORF 3) of WfNgV1 exhibit the characteristic topologies previously reported for negevirus counterparts. This marks the first reported negevirus-like sequence from whiteflies in the Americas.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods

The zinc (Zn²⁺) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at . The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.

Identification of B- and T-cell epitopes on HtrA protein of Orientia tsutsugamushi

Orientia tsutsugamushi, a cause of scrub typhus is emerging as an important pathogen in several parts of the tropics. The control of this infection relies on rapid diagnosis, specific treatment, and prevention through vector control. Development of a vaccine for human use would be very important as a public health measure. Antibody and T-cell response have been found to be important in the protection against scrub typhus. This study was undertaken to predict the peptide vaccine that elicits both B- and T-cell immunity. The outer-membrane protein, 47-kDa high-temperature requirement A was used as the target protein for the identification of protective antigen(s). Using BepiPred2 program, the potential B-cell epitope PNSSWGRYGLKMGLR with high conservation among O. tsutsugamushi and the maximum surface exposed residues was identified. Using IEDB, NetMHCpan, and NetCTL programs, T-cell epitopes MLNELTPEL and VTNGIISSK were identified. These peptides were found to have promiscuous class-I major histocompatibility complex (MHC) binding affinity to MHC supertypes and high proteasomal cleavage, transporter associated with antigen processing prediction, and antigenicity scores. In the I-TASSER generated model, the C-score was -0.69 and the estimated TM-score was 0.63 ± 0.14. The location of the epitope in the 3D model was external. Therefore, an antibody to this outer-membrane protein epitope could opsonize the bacterium for clearance by the reticuloendothelial system. The T-cell epitopes would generate T-helper function. The B-cell epitope(s) identified could be evaluated as antigen(s) in immunodiagnostic assays. This cocktail of three peptides would elicit both B- and T-cell immune response with a suitable adjuvant and serve as a vaccine candidate.

Transmembrane region prediction by using sequence-derived features and machine learning methods

Membrane proteins are central to carrying out impressive biological functions.

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Protein dephosphorylation, which is an inverse process of phosphorylation, plays a crucial role in a myriad of cellular processes, including mitotic cycle, proliferation, differentiation, and cell growth. Compared with tyrosine kinase substrate and phosphorylation site prediction, there is a paucity of studies focusing on computational methods of predicting protein tyrosine phosphatase substrates and dephosphorylation sites. In this work, we developed two elegant models for predicting the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. The first predictor is called MGPS-DEPHOS, which is modified from the GPS (Group-based Prediction System) algorithm with an interpretable capability. The second predictor is called CKSAAP-DEPHOS, which is built through the combination of support vector machine (SVM) and the composition of k-spaced amino acid pairs (CKSAAP) encoding scheme. Benchmarking experiments using jackknife cross validation and 30 repeats of 5-fold cross validation tests show that MGPS-DEPHOS and CKSAAP-DEPHOS achieved AUC values of 0.921, 0.914 and 0.912, for predicting dephosphorylation sites of the three phosphatases PTP1B, SHP-1, and SHP-2, respectively. Both methods outperformed the previously developed kNN-DEPHOS algorithm. In addition, a web server implementing our algorithms is publicly available at http://genomics.fzu.edu.cn/dephossite/ for the research community.

A neural network learning approach for improving the prediction of residue depth based on sequence-derived features

Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface.

SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites

SOHPRED is a new and competitive bioinformatics tool for characterizing and predicting human S-sulfenylation sites.