The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.

Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically establish comprehensive associations between AMP sequences and structures through structural folds and to provide an easy access to view their relationships. 30 distinct AMP structural fold clusters with more than one structure are detected and about a thousand AMPs are associated with at least one structural fold cluster. According to ADAM, AMP structural folds are limited—AMPs only cover about 3% of the overall protein fold space.

Gramene (http://www.gramene.org) is a comparative genome mapping database for grasses and a community resource for rice (Oryza sativa). It combines a semi-automatically generated database of cereal genomic and expressed sequence tag sequences, genetic maps, map relations, and publications, with a curated database of rice mutants (genes and alleles), molecular markers, and proteins. Gramene curators read and extract detailed information from published sources, summarize that information in a structured format, and establish links to related objects both inside and outside the database, providing seamless connections between independent sources of information. Genetic, physical, and sequence-based maps of rice serve as the fundamental organizing units and provide a common denominator for moving across species and genera within the grass family. Comparative maps of rice, maize (Zea mays), sorghum (Sorghum bicolor), barley (Hordeum vulgare), wheat (Triticum aestivum), and oat (Avena sativa) are anchored by a set of curated correspondences. In addition to sequence-based mappings found in comparative maps and rice genome displays, Gramene makes extensive use of controlled vocabularies to describe specific biological attributes in ways that permit users to query those domains and make comparisons across taxonomic groups. Proteins are annotated for functional significance using gene ontology terms that have been adopted by numerous model species databases. Genetic variants including phenotypes are annotated using plant ontology terms common to all plants and trait ontology terms that are specific to rice. In this paper, we present a brief overview of the search tools available to the plant research community in Gramene.

Gramene (http://www.gramene.org/) is a comparative genome database for cereal crops and a community resource for rice. We are populating and curating Gramene with annotated rice (Oryza sativa) genomic sequence data and associated biological information including molecular markers, mutants, phenotypes, polymorphisms and Quantitative Trait Loci (QTL). In order to support queries across various data sets as well as across external databases, Gramene will employ three related controlled vocabularies. The specific goal of Gramene is, first to provide a Trait Ontology (TO) that can be used across the cereal crops to facilitate phenotypic comparisons both within and between the genera. Second, a vocabulary for plant anatomy terms, the Plant Ontology (PO) will facilitate the curation of morphological and anatomical feature information with respect to expression, localization of genes and gene products and the affected plant parts in a phenotype. The TO and PO are both in the early stages of development in collaboration with the International Rice Research Institute, TAIR and MaizeDB as part of the Plant Ontology Consortium. Finally, as part of another consortium comprising macromolecular databases from other model organisms, the Gene Ontology Consortium, we are annotating the confirmed and predicted protein entries from rice using both electronic and manual curation.

The goal was to identify HLA-DQ8-bound β cell epitopes important in the T cell response in autoimmune diabetes. We first identified HLA-DQ8 (DQA1*0301/DQB1*0302) β cell epitopes using a computational approach and then related their identification to CD4 T cell responses. The computational program (TEA-DQ8) was adapted from one previously developed for identifying peptides bound to the I-Ag7 molecule and based on a library of naturally processed peptides bound to HLA-DQ8 molecules of antigen-presenting cells. We then examined experimentally the response of NOD.DQ8 mice immunized with peptides derived from the Zinc transporter 8 protein. Log-of-odds scores on peptides were experimentally validated as an indicator of peptide binding to HLA-DQ8 molecules. We also examined previously published data on diabetic autoantigens, including glutamic acid decarboxylase-65, insulin and insulinoma-associated antigen-2, all tested in NOD.DQ8 transgenic mice. In all examples, many peptides identified with a favorable binding motif generated an autoimmune T cell response, but importantly many did not. Moreover, some peptides with weak-binding motifs were immunogenic. These results indicate the benefits and limitations in predicting autoimmune T cell responses strictly from MHC-binding data. TEA-DQ8 performed significantly better than other prediction programs