Facile de Novo Sequencing of Tetrazine-Cyclized Peptides through UV-Induced Ring-Opening and Cleavage from the Solid Phase

While most FDA-approved peptide drugs are cyclic, the robust cyclization chemistry of peptides and the deconvolution of cyclic peptide sequences by using tandem mass spectrometry render cyclic peptide drug discovery difficult. Here we present the successful design of cyclic peptides on solid phase that addresses both of these problems. We demonstrate that this peptide cyclization method using dichloro-s-tetrazine on solid phase allows successful cyclization of a panel of random peptide sequences with various charges and hydrophobicities. The cyclic peptides can be linearized and cleaved from the solid phase by simple UV light irradiation, and we demonstrate that accurate sequence information can be obtained for the UV-cleaved linearized peptides by using tandem mass spectrometry. The tetrazine linker used in the cyclic peptides can further be explored for inverse electron-demand Diels-Alder (IEDDA) reactions for screening or bioconjugation applications in the future.     

An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Abstract: Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics. A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology. Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset. According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates.