Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

Rhodnius prolixus uses the peptidoglycan recognition receptor rpPGRP-LC/LA to detect Gram-negative bacteria and activate the IMD pathway

Insects rely on an innate immune system to recognize and eliminate pathogens. Key components of this system are highly conserved across all invertebrates. To detect pathogens, insects use Pattern recognition receptors (PRRs) that bind to signature motifs on the surface of pathogens called Pathogen Associated Molecular Patterns (PAMPs). In general, insects use peptidoglycan recognition proteins (PGRPs) in the Immune Deficiency (IMD) pathway to detect Gram-negative bacteria, and other PGRPs and Gram-negative binding proteins (GNBPs) in the Toll pathway to detect Gram-positive bacteria and fungi, although there is crosstalk and cooperation between these and other pathways. Once pathogens are recognized, these pathways activate the production of potent antimicrobial peptides (AMPs). Most PRRs in insects have been reported from genome sequencing initiatives but few have been characterized functionally. The initial studies on insect PRRs were done using established dipteran model organisms such as Drosophila melanogaster, but there are differences in the numbers and functional role of PRRs in different insects. Here we describe the genomic repertoire of PGRPs in Rhodnius prolixus, a hemimetabolous hemipteran vector of the parasite Trypanosoma cruzi that causes Chagas disease in humans. Using a de novo transcriptome from the fat body of immune activated insects, we found 5 genes encoding PGRPs. Phylogenetic analysis groups R. prolixus PGRPs with D. melanogaster PGRP-LA, which is involved in the IMD pathway in the respiratory tract. A single R. prolixus PGRP gene encodes isoforms that contain an intracellular region or motif (cryptic RIP Homotypic Interaction Motif-cRHIM) that is involved in the IMD signaling pathway in D. melanogaster. We characterized and silenced this gene using RNAi and show that the PGRPs that contain cRHIMs are involved in the recognition of Gram-negative bacteria, and activation of the IMD pathway in the fat body of R. prolixus, similar to the PGRP-LC of D. melanogaster. This is the first functional characterization of a PGRP containing a cRHIM motif that serves to activate the IMD pathway in a hemimetabolous insect.

Human, vector and parasite Hsp90 proteins: A comparative bioinformatics analysis

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Cytosolic Hsp90s are more conserved than those from mitochondrial and ER.

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Cell environment plays a role in the overall physicochemical properties of Hsp90s.

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Serine and tyrosine are favored phosphorylated residues of Hsp90s.

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Mitochondrial and ER Hsp90s have motifs unique to specific organisms.

The treatment of protozoan parasitic diseases is challenging, and thus identification and analysis of new drug targets is important. Parasites survive within host organisms, and some need intermediate hosts to complete their life cycle. Changing host environment puts stress on parasites, and often adaptation is accompanied by the expression of large amounts of heat shock proteins (Hsps). Among Hsps, Hsp90 proteins play an important role in stress environments. Yet, there has been little computational research on Hsp90 proteins to analyze them comparatively as potential parasitic drug targets. Here, an attempt was made to gain detailed insights into the differences between host, vector and parasitic Hsp90 proteins by large-scale bioinformatics analysis. A total of 104 Hsp90 sequences were divided into three groups based on their cellular localizations; namely cytosolic, mitochondrial and endoplasmic reticulum (ER). Further, the parasitic proteins were divided according to the type of parasite (protozoa, helminth and ectoparasite). Primary sequence analysis, phylogenetic tree calculations, motif analysis and physicochemical properties of Hsp90 proteins suggested that despite the overall structural conservation of these proteins, parasitic Hsp90 proteins have unique features which differentiate them from human ones, thus encouraging the idea that protozoan Hsp90 proteins should be further analyzed as potential drug targets.