Offmann, B. | bioRxiv Science

bioRxiv2018

EcRBPome: a comprehensive database of all known E. coli RNA-binding proteins

BackgroundThe repertoire of RNA-binding proteins (RBPs) in bacteria play crucial role for their survival, and interactions with the host machinery, but there is little information, record or characterisation in bacterial genomes. As a first step towards this, we have chosen the bacterial model system Escherichia coli, and organised all RBPs in this organism into a comprehensive database named EcRBPome.\n\nResultsEcRBPome contains RBPs recorded from 166 complete E. coli proteomes available in the RefSeq database (as of May 2016). The database provides various features related to the E. coli RBPs, like their domain architectures, PDB structures, GO and EC annotations etc. It provides the assembly, bioproject and biosample details of each strain, as well as cross-strain comparison of occurrences of various RNA-binding domains (RBDs). The percentage of RBPs, the abundance of the various RBDs harboured by each strain have been graphically represented in this database and available alongside other files for user download.\n\nConclusionTo the best of our knowledge, this is the first database of its kind and we hope that it will be of great use to the biological community. Database URL: http://caps.ncbs.res.in/ecrbpome

bioinformatics↗

bioRxiv2017

PB-kPRED: Knowledge-Based Prediction Of Protein Backbone Conformation Using A Structural Alphabet

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.\n\nBased on the strategy used for scanning the database, the method was able to achieve efficient mean Q16 accuracies between 40.8% and 66.3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

bioinformatics↗