The house spider genome reveals an ancient whole-genome duplication during arachnid evolution.

BACKGROUND: The duplication of genes can occur through various mechanisms and is thought to make a major contribution to the evolutionary diversification of organisms. There is increasing evidence for a large-scale duplication of genes in some chelicerate lineages including two rounds of whole genome duplication (WGD) in horseshoe crabs. To investigate this further, we sequenced and analyzed the genome of the common house spider Parasteatoda tepidariorum. RESULTS: We found pervasive duplication of both coding and non-coding genes in this spider, including two clusters of Hox genes. Analysis of synteny conservation across the P. tepidariorum genome suggests that there has been an ancient WGD in spiders. Comparison with the genomes of other chelicerates, including that of the newly sequenced bark scorpion Centruroides sculpturatus, suggests that this event occurred in the common ancestor of spiders and scorpions, and is probably independent of the WGDs in horseshoe crabs. Furthermore, characterization of the sequence and expression of the Hox paralogs in P. tepidariorum suggests that many have been subject to neo-functionalization and/or sub-functionalization since their duplication. CONCLUSIONS: Our results reveal that spiders and scorpions are likely the descendants of a polyploid ancestor that lived more than 450 MYA. Given the extensive morphological diversity and ecological adaptations found among these animals, rivaling those of vertebrates, our study of the ancient WGD event in Arachnopulmonata provides a new comparative platform to explore common and divergent evolutionary outcomes of polyploidization events across eukaryotes.

House spider genome uncovers evolutionary shifts in the diversity and expression of black widow venom proteins associated with extreme toxicity.

BACKGROUND: Black widow spiders are infamous for their neurotoxic venom, which can cause extreme and long-lasting pain. This unusual venom is dominated by latrotoxins and latrodectins, two protein families virtually unknown outside of the black widow genus Latrodectus, that are difficult to study given the paucity of spider genomes. Using tissue-, sex- and stage-specific expression data, we analyzed the recently sequenced genome of the house spider (Parasteatoda tepidariorum), a close relative of black widows, to investigate latrotoxin and latrodectin diversity, expression and evolution. RESULTS: We discovered at least 47 latrotoxin genes in the house spider genome, many of which are tandem-arrayed. Latrotoxins vary extensively in predicted structural domains and expression, implying their significant functional diversification. Phylogenetic analyses show latrotoxins have substantially duplicated after the Latrodectus/Parasteatoda split and that they are also related to proteins found in endosymbiotic bacteria. Latrodectin genes are less numerous than latrotoxins, but analyses show their recruitment for venom function from neuropeptide hormone genes following duplication, inversion and domain truncation. While latrodectins and other peptides are highly expressed in house spider and black widow venom glands, latrotoxins account for a far smaller percentage of house spider venom gland expression. CONCLUSIONS: The house spider genome sequence provides novel insights into the evolution of venom toxins once considered unique to black widows. Our results greatly expand the size of the latrotoxin gene family, reinforce its narrow phylogenetic distribution, and provide additional evidence for the lateral transfer of latrotoxins between spiders and bacterial endosymbionts. Moreover, we strengthen the evidence for the evolution of latrodectin venom genes from the ecdysozoan Ion Transport Peptide (ITP)/Crustacean Hyperglycemic Hormone (CHH) neuropeptide superfamily. The lower expression of latrotoxins in house spiders relative to black widows, along with the absence of a vertebrate-targeting α-latrotoxin gene in the house spider genome, may account for the extreme potency of black widow venom.

Combining features in a graphical model to predict protein binding sites.

Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non-binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF-approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature "change in free energy" is introduced into the model, which is then denoted by ΔF-CRF. Parameters are trained with an online large-margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph-structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF-CRF performs significantly better on a large range of false positive rates than the support-vector-machine-based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF-CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method.

CRF-based models of protein surfaces improve protein-protein interaction site predictions.

BACKGROUND: The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved. RESULTS: We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers. CONCLUSIONS: Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.