Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Intrinsic disorder in proteins: Relevance to protein assemblies, drug design and host-pathogen interactions

Intrinsic disorder in proteins resulting in considerable variation in structure can lead to multiple functions including multi-specificity and diverse pathologies. Protein interfaces can involve disordered regions that assemble through a concerted-fold-and-bind mechanism. The binding involves both enthalpic and entropic gains by exploiting 'hot spots' on the partner and displacing water molecules placed in thermodynamically unfavorable situations. The examples of Rad51-BRCA2 and Artemis-DNA-PKCs/LigIV complexes illustrate this in the context of drug design. This overview tracks the seamless involvement of protein disorder in multi-specificity of biocatalysts, protein assembly formations and host-pathogen interactions, where intrinsic disorder can in Mycobacteria, compensate for genome reduction by carrying out multiple functions and in some RNA viruses facilitate adaption to the host. These present challenging opportunities for designing new drugs and interventions.

Evolution of protein kinase substrate recognition at the active site

Protein kinases catalyse the phosphorylation of target proteins, controlling most cellular processes. The specificity of serine/threonine kinases is partly determined by interactions with a few residues near the phospho-acceptor residue, forming the so-called kinase-substrate motif. Kinases have been extensively duplicated throughout evolution, but little is known about when in time new target motifs have arisen. Here, we show that sequence variation occurring early in the evolution of kinases is dominated by changes in specificity-determining residues. We then analysed kinase specificity models, based on known target sites, observing that specificity has remained mostly unchanged for recent kinase duplications. Finally, analysis of phosphorylation data from a taxonomically broad set of 48 eukaryotic species indicates that most phosphorylation motifs are broadly distributed in eukaryotes but are not present in prokaryotes. Overall, our results suggest that the set of eukaryotes kinase motifs present today was acquired around the time of the eukaryotic last common ancestor and that early expansions of the protein kinase fold rapidly explored the space of possible target motifs.

Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

Background

The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is typically of interest to conduct pairwise comparison tests. Current approaches to such tests rely on large-sample approximations, due to the numerical complexity of computing the exact distribution. These approximate methods lead to inaccurate estimates in the tail of the distribution, which is most relevant for p-value calculation.

Results

We propose an efficient, combinatorial exact approach for calculating the probability mass distribution of the rank sum difference statistic for pairwise comparison of Friedman rank sums, and compare exact results with recommended asymptotic approximations. Whereas the chi-squared approximation performs inferiorly to exact computation overall, others, particularly the normal, perform well, except for the extreme tail. Hence exact calculation offers an improvement when small p-values occur following multiple testing correction. Exact inference also enhances the identification of significant differences whenever the observed values are close to the approximate critical value. We illustrate the proposed method in the context of biological machine learning, were Friedman rank sum difference tests are commonly used for the comparison of classifiers over multiple datasets.

Conclusions

We provide a computationally fast method to determine the exact p-value of the absolute rank sum difference of a pair of Friedman rank sums, making asymptotic tests obsolete. Calculation of exact p-values is easy to implement in statistical software and the implementation in R is provided in one of the Additional files and is also available at http://www.ru.nl/publish/pages/726696/friedmanrsd.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1486-2) contains supplementary material, which is available to authorized users.

Phytophthora infestans specific phosphorylation patterns and new putative control targets

In this study we applied biomathematical searches of gene regulatory mechanisms to learn more about oomycete biology and to identify new putative targets for pesticides or biological control against Phytophthora infestans. First, oomycete phylum-specific phosphorylation motifs were found by discriminative n-gram analysis. We found 11.600 P. infestans specific n-grams, mapping 642 phosphoproteins. The most abundant group among these related to phosphatidylinositol metabolism. Due to the large number of possible targets found and our hypothesis that multi-level control is a sign of usefulness as targets for intervention, we identified overlapping targets with a second screen. This was performed to identify proteins dually regulated by small RNA and phosphorylation. We found 164 proteins to be regulated by both sRNA and phosphorylation and the dominating functions where phosphatidylinositol signalling/metabolism, endocytosis, and autophagy. Furthermore we performed a similar regulatory study and discriminative n-gram analysis of proteins with no clear orthologs in other species and proteins that are known to be unique to P. infestans such as the RxLR effectors, Crinkler (CRN) proteins and elicitins. We identified CRN proteins with specific phospho-motifs present in all life stages. PITG_12626, PITG_14042 and PITG_23175 are CRN proteins that have species-specific phosphorylation motifs and are subject to dual regulation.