• In silico analysis using a total of 337 lentil protein sequences provides an extensive list of the potential allergens. • PR-10b and DRR49 showed ≥75% sequence similarity and >94% identity with the 3D epitope, thus considered strong evidence with allergenicity potential. • The proposed strategies might be beneficial to determine low allergen-containing lentil cultivars.

A mesophilic sulphate-reducing micro-organism, able to grow chemolithoautotrophically with H2/CO2 (20 : 80) and with elemental iron as a sole electron donor, was isolated from a consortium capable of degrading long-chain paraffins and designated strain DRH4T. Cells were oval shaped often with bright refractile cores and occurred singly or in pairs. The cells formed pili. Strain DRH4T could grow chemolithoautotrophically with H2/CO2 or elemental iron and chemoorganotrophically utilizing a number of organic substrates, such as fatty acids from formate to octanoate (C1–C8). Sulphate and thiosulphate served as terminal electron acceptors, but sulphite and nitrate did not. Optimal growth was observed from 37 to 40 °C and pH from 6.5 to 7.2. Strain DRH4T did not require NaCl for growth and could proliferate under a broad range of salinities from freshwater (1 g l−1 NaCl) to seawater (27 g l−1 NaCl) conditions. The genomic DNA G+C content was 54.46 mol %. Based on 16S rRNA gene sequence analysis. strain DRH4T was distinct from previously described Deltaproteobacteria species exhibiting the closest affiliation to Desulforhabdus amnigena ASRB1T, Syntrophobacterium sulfatireducens TB8106T and Desulfovirga adipica 12016T with 93.35, 93.42 and 92.85 % similarity, respectively. Strain DRH4T showed significant physiological differences with the aforementioned organisms. Based on physiological differences and phylogenetic comparisons, we propose to classify DRH4T as the type strain (=DSM 113 455T=JCM 39 248T) of a novel species of a new genus with the name Desulfoferrobacter suflitae gen. nov., sp. nov.

This article presents a visual analytics framework, idMotif, to support domain experts in identifying motifs in protein sequences. A motif is a short sequence of amino acids usually associated with distinct functions of a protein, and identifying similar motifs in protein sequences helps to predict certain types of disease or infection. idMotif can be used to explore, analyze, and visualize such motifs in protein sequences. We introduce a deep-learning-based method for grouping protein sequences and allow users to discover motif candidates of protein groups based on local explanations of the decision of a deep-learning model. idMotif provides several interactive linked views for between and within protein cluster/group and sequence analysis. Through a case study and experts' feedback, we demonstrate how the framework helps domain experts analyze protein sequences and motif identification.