Engineering topics
Publications and source records attributed to Samudrala, Ram.
The true identity of the protein found in the crystals reported by Bondoc et al. [(2019), Acta Cryst. F75, 646–651] is given.
Abstract not provided
In this review, we provide an overview of the methods employed by four recent papers that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. The results of the studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVEserver (http://www.biopilot.org). Finally, we assess the accuracy of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we have recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif, and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.
The number of protein-coding genes predicted from the rice genome remains much larger than the number of genes for Arabidopsis. Most of the excess rice genes show low similarity to Arabidopsis, and eudicots in general. It is difficult to repudiate these genes because even for the 19,079 non-redundant rice cDNAs that have been sequenced, 34.3% are low similarity. Here, we describe a major contributor to this excess of low similarity genes: the frequent and continued occurrence of gene duplications in plants, and the post- duplicative degradation (i.e. relaxed selection) that is typically expected to occur in one of the two gene copies. The salient feature of this degradation is an increase in Ka/Ks for Ks per available site, respectively. We show that low similarity genes are expressed at lower mRNA and protein levels than other genes, and that regions of the encoded proteins are structurally disordered. Indeed, many are evolutionary “transients” that may be destined to die, even if they remain transcriptionally active for millions of years.