Molecular modeling and in silico characterization of Mycobacterium tuberculosis TlyA: Possible misannotation of this tubercle bacilli-hemolysin

<p>Abstract</p> <p>Background</p> <p>The TlyA protein has a controversial function as a virulence factor in <it>Mycobacterium tuberculosis </it>(<it>M. tuberculosis</it>). At present, its dual activity as hemolysin and RNA methyltransferase in <it>M. tuberculosis </it>has been indirectly proposed based on <it>in vitro </it>results. There is no evidence however for TlyA relevance in the survival of tubercle bacilli inside host cells or whether both activities are functionally linked. A thorough analysis of structure prediction for this mycobacterial protein in this study shows the need for reevaluating TlyA's function in virulence.</p> <p>Results</p> <p>Bioinformatics analysis of TlyA identified a ribosomal protein binding domain (S4 domain), located between residues 5 and 68 as well as an FtsJ-like methyltranferase domain encompassing residues 62 and 247, all of which have been previously described in translation machinery-associated proteins. Subcellular localization prediction showed that TlyA lacks a signal peptide and its hydrophobicity profile showed no evidence of transmembrane helices. These findings suggested that it may not be attached to the membrane, which is consistent with a cytoplasmic localization. Three-dimensional modeling of TlyA showed a consensus structure, having a common core formed by a six-stranded β-sheet between two α-helix layers, which is consistent with an RNA methyltransferase structure. Phylogenetic analyses showed high conservation of the <it>tlyA </it>gene among <it>Mycobacterium </it>species. Additionally, the nucleotide substitution rates suggested purifying selection during <it>tlyA </it>gene evolution and the absence of a common ancestor between TlyA proteins and bacterial pore-forming proteins.</p> <p>Conclusion</p> <p>Altogether, our manual <it>in silico </it>curation suggested that TlyA is involved in ribosomal biogenesis and that there is a functional annotation error regarding this protein family in several microbial and plant genomes, including the <it>M. tuberculosis </it>genome.</p

Statistical potentials for RNA-protein interactions optimized by CMA-ES

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses

RNA-Protein Structure Classifiers Incorporated into Second-Generation Statistical Potentials

Computational modeling of RNA-protein interactions remains an important endeavor. However, exclusively all-atom approaches that model RNA-protein interactions via molecular dynamics are often problematic in their application. One possible alternative is the implementation of hierarchical approaches, first efficiently exploring configurational space with a coarse-grained representation of the RNA and protein. Subsequently, the lowest energy set of such coarse-grained models can be used as scaffolds for all-atom placements, a standard method in modeling protein 3D-structure. However, the coarse-grained modeling likely will require improved ribonucleotide-amino acid potentials as applied to coarse-grained structures. As a first step we downloaded 1,345 PDB files and clustered them with PISCES to obtain a non-redundant complex data set. The contacts were divided into nine types with DSSR according to the 3D structure of RNA and then 9 sets of potentials were calculated. The potentials were applied to score fifty thousand poses generated by FTDock for twenty-one standard RNA-protein complexes. The results compare favorably to existing RNA-protein potentials. Future research will optimize and test such combined potentials

RNA base-amino acid interaction strengths derived from structures and sequences.

We investigate RNA base-amino acid interactions by counting their contacts in structures and their implicit contacts in various functional sequences where the structures can be assumed to be preserved. These frequencies are cast into equations to extract relative interaction energetics. Previously we used this approach in considering the major groove interactions of DNA, and here we apply it to the more diverse interactions observed in RNA. Structures considered are the three different tRNA synthetase complexes, the U1A spliceosomal protein with an RNA hairpin and the BIV TAR-Tat complex. We use binding data for the base frequencies for the seryl, aspartyl and glutaminyl tRNA-synthetase and U1 RNA-protein complexes. We compare with the previously reported DNA major groove peptide contacts the results for atoms of RNA bases, usually in the major groove. There are strong similarities between the rank orders of interacting bases in the DNA and the RNA cases. The apparent strongest RNA interaction observed is between arginine and guanine which was also one of the strongest DNA interactions. The similar data for base atomic interactions, whether base paired or not, support the importance of strong atomic interactions over local structure considerations, such as groove width and alpha-helicity

Mutagenesis and functional characterisation of toxin HicA from the HicBA TA system in Burkholderia pseudomallei.

Doctoral thesis studying the TA toxin HicA from Burkholderia pseudomallei. Firstly, toxin HicA was evaluated as a potential antimicrobial compound, through ectopic expression in a range of bacterial species. Secondly, residues or regions of the toxin involved in toxicity or TA complex formation were identified using a scanning alanine mutagenesis study. Thirdly, interactions between non-cognate TA pairs were examined to determine if type II TA systems are able to form co-operative networks in B. pseudomallei.Four type II toxin-antitoxin (TA) systems were previously identified in Burkholderia pseudomallei K96243. Type II TA toxins are able to induce cell growth arrest or death by interfering with key processes within the organism. BPSS0390-0391 is one of the TA systems previously identified and has homology to hicBA system in Acinetobacter baumannii. B. pseudomallei HicA is able to cause a reduction in the number of culturable cells after expression in E. coli. This study aimed to characterise B. pseudomallei HicA in three ways: by inducing expression of HicA in bacterial species other than E. coli, by identifying amino acids in HicA involved in toxicity and neutralisation by the antitoxin HicB and by examining the interaction of HicA with other TA antitoxins identified within B. pseudomallei genome. A broad host range plasmid encoding BPSS0390 was transformed into a range of Gram negative bacteria including Yersinia pseudotuberculosis IP32953, Vibrio vulnificus E64MW, Salmonella enterica serovar Typhimurium SL1344 and Burkholderia thailandensis E264. Expression of BPSS0390 was toxic in all bacterial species tested, despite the presence of antitoxin BPSS0391 homologues in some species. Unregulated expression in E. coli resulted in the appearance of escape mutants encoding non-toxic variants of HicA. An alanine scanning mutagenesis study of HicA identified 20 mutants where toxicity was abolished despite high levels of expression, but identified no mutants that affected TA complex formation. Finally an existing co-expression assay was modified to examine interactions between HicA and other type II TA antitoxins in B. pseudomallei. The assay revealed no interaction between HicA and non-cognate antitoxins and clarified the role of IPTG as an inhibitor of PBAD promoter on the arabinose operon.University or Exeter and DSTL

Computational analysis and prediction of protein-RNA interactions

Protein-RNA interactions are essential for many important processes including all phases of protein production, regulation of gene expression, and replication and assembly of many viruses. This dissertation has two related goals: 1) predicting RNA-binding sites in proteins from protein sequence, structure, and conservation information, and 2) characterizing protein-RNA interactions. We present several machine learning classifiers for predicting RNA-binding sites in proteins based on the protein sequence, protein structure, and conservation information. Our first classifier uses only amino acid sequence information as input and predicts RNA-binding sites with an area under the receiver operator characteristic curve (AUC) of 0.74. Using the neighboring amino acids in the protein structure improves prediction performance over using sequence alone. We show that using evolutionary information in the form of position specific scoring matrices provides a further significant improvement in predictions. Finally, we create an ensemble classifier that combines the predictions of the sequence, structure, and PSSM based classifiers and gives the best prediction performance, with an AUC of 0.81. We construct the Protein-RNA Interaction Database, PRIDB, a comprehensive collection of all protein-RNA complexes in the PDB. PRIDB focuses on characterizing the molecular interaction at the protein-RNA interface in terms of van der Waals contacts, direct hydrogen bonds, and water-mediated hydrogen bonds. We perform an extensive analysis of the RNA-binding characteristics of a non-redundant dataset of 181 proteins to determine general characteristics of protein-RNA binding sites. We find that the overall interaction propensities for Watson-Crick paired nucleotides and non Watson-Crick paired nucleotides are very similar, with the propensities for amino acids binding to single stranded nucleotides showing more differences. We find that van der Waals contacts are more numerous than hydrogen bonds and amino acids interact with RNA through their side chain atoms more frequently than their main chain atoms. We also find that contacts to the RNA base are not as frequent as contacts to the RNA backbone. Together, the prediction and characterization presented in this dissertation have increased our understanding of how proteins and RNA interact