NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins

<p>Abstract</p> <p>Background</p> <p>Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.</p> <p>Results</p> <p>Several feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested <it>k</it>-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.</p> <p>Conclusions</p> <p>The final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at <url>http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/</url></p

Fast subcellular localization by cascaded fusion of signal-based and homology-based methods

<p>Abstract</p> <p>Background</p> <p>The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.</p> <p>Results</p> <p>This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).</p> <p>Conclusions</p> <p>Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.</p

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

The characterisation of trypanosomal type 1 DnaJ-like proteins

Trypanosomes are protozoans, of which many are parasitic, and possess complex lifecycles which alternate between mammalian and arthropod hosts. As is the case with most organisms, molecular chaperones and heat shock proteins are encoded within the genomes of these protozoans. These proteins are an integral part of maintaining the structural integrity of proteins during normal and stress conditions. Heat shock protein 40 (Hsp40) is a co-chaperone of heat shock protein 70 (Hsp70) and in some cases can act as a chaperone. These proteins work together to bind non-native polypeptide structures to prevent unfolded protein aggregrate formation in times of stress, translocate proteins across organelle membranes, and transport unsalvageable proteins to proteolytic degradation by the cellular proteasome. Hsp40s are divided into four types based on their domain structure. Analysis of the nuclear genomes of eight trypanosomatid species revealed that less than 10 of the approximate 70 Hsp40 sequences per genome were Type 1 Hsp40s, many of which contained putative orthologues in the other seven trypanosomatid genomes. One of these Type 1 Hsp40s from T b. brucei, Trypanosoma brucei DnaJ 2 (Tbj2), was functionally characterised in T brucei brucei. RNA interference knockdown of expression in T brucei brucei showed that cells deficient in Tbj2 displayed a severe inhibition of the growth of the cell population. The levels of the Tbj2 protein population in T brucei brucei cells increases after exposure to 42°c and the protein was found to have a generalized cytoplasmic subcellular localization at 37°c. These findings provide evidence that Tbj2 is an orthologue of Yeast DnaJ 1 (Y dj l), an essential S. cerevisiae protein. Hsp40s interact with their partner Hsp70s through their J-domain. The amino acids of the J-domain important for a functional interaction with Hsp70 were examined in Trypanosoma cruzi DnaJ 2 (Tcj2) (the orthologue of Tbj2) and T cruzi DnaJ protein 3 (Tcj3) by testing their ability to substitute for Y dj l in Saccharomyces cerevisae and for DnaJ in Escherichia coli. In both systems, the positively charged amino acids of Helix II and III of the J-domain disrupted the functional interaction of these Hsp40s with their partner Hsp70s. Substitutions in Helix I and IV of the J-domains of Tcj2 and Tcj3 produced varied results in the two different systems, possibly suggesting that these helices serve to define with which Hsp70s a given Hsp40 can interact. The inability of an Hsp40 and an Hsp70 to interact functionally does not necessarily mean a total absence of physical interaction between these proteins. The amino acid substitution of the histidine in the HPD motif (H34Q) of the J-domain of Tcj2 and Tcj3 removed the ability of these proteins to interact functionally with S. cerevisiae Hsp70 (Ssal) in vivo. However, preliminary binding studies using the quartz crystal microbalance with dissipation monitoring (QCM-D) show that Tcj2 and Tcj2(H34Q) both physically interact with M sativa Hsp70 in vitro. This study is the first report to provide evidence that certain trypanosoma! Type 1 Hsp40s are essential proteins. Futhermore, the interaction of these Hsp40s with Hsp70 identified important features of the functional interface of this chaperone machinery