Scoring and identifying organism-specific functional patterns and putative phosphorylation sites in protein sequences using mutual information

Cryogenian Origin and Subsequent Diversification of the Plant Cell-Wall Enzyme XTH Family

All land plants encode large multigene families of xyloglucan endotransglucosylase/hydrolases (XTHs), plant-specific enzymes that cleave and reconnect plant cell-wall polysaccharides. Despite the ubiquity of these enzymes, considerable uncertainty remains regarding the evolutionary history of the XTH family. Phylogenomic and comparative analyses in this study traced the non-plant origins of the XTH family to Alphaproteobacteria ExoKs, bacterial enzymes involved in loosening biofilms, rather than Firmicutes licheninases, plant biomass digesting enzymes, as previously supposed. The relevant horizontal gene transfer (HGT) event was mapped to the divergence of non-swimming charophycean algae in the Cryogenian geological period. This HGT event was the likely origin of charophycean EG16-2s, which are putative intermediates between ExoKs and XTHs. Another HGT event in the Cryogenian may have led from EG16-2s or ExoKs to fungal Congo Red Hypersensitive proteins (CRHs) to fungal CRHs, enzymes that cleave and reconnect chitin and glucans in fungal cell walls. This successive transfer of enzyme-encoding genes may have supported the adaptation of plants and fungi to the ancient icy environment by facilitating their sessile lifestyles. Furthermore, several protein evolutionary steps, including coevolution of substrate-interacting residues and putative intra-family gene fusion, occurred in the land plant lineage and drove diversification of the XTH family. At least some of those events correlated with the evolutionary gain of broader substrate specificities, which may have underpinned the expansion of the XTH family by enhancing duplicated gene survival. Together, this study highlights the Precambrian evolution of life and the mode of multigene family expansion in the evolutionary history of the XTH family.

New Measurement for Correlation of Co-evolution Relationship of Subsequences in Protein

Many computational tools have been developed to measure the protein residues co-evolution. Most of them only focus on co-evolution for pairwise residues in a protein sequence. However, number of residues participate in co-evolution might be multiple. And some co-evolved residues are clustered in several distinct regions in primary structure. Therefore, the co-evolution among the adjacent residues and the correlation between the distinct regions offer insights into function and evolution of the protein and residues. Subsequence is used to represent the adjacent multiple residues in one distinct region. In the paper, co-evolution relationship in each subsequence is represented by mutual information matrix (MIM). Then, Pearson's correlation coefficient: R value is developed to measure the similarity correlation of two MIMs. MSAs from Catalytic Data Base (Catalytic Site Atlas, CSA) are used for testing. R value characterizes a specific class of residues. In contrast to individual pairwise co-evolved residues, adjacent residues without high individual MI values are found since the co-evolved relationship among them is similar to that among another set of adjacent residues. These subsequences possess some flexibility in the composition of side chains, such as the catalyzed environment.

New measurement for correlation of co-evolution relationship of subsequences in protein

Many computational tools have been developed to measure the protein residues co-evolution. Most of them only focus on co-evolution for pairwise residues in a protein sequence. However, number of residues participate in co-evolution might be multiple. And some co-evolved residues are clustered in several distinct regions in primary structure. Therefore, the co-evolution among the adjacent residues, and the correlation between the distinct regions offer insights into function and evolution of the protein and residues. Subsequence is used to represent the adjacent multiple residues in one distinct region. In the paper, co-evolution relationship in each subsequence is represented by mutual information matrix (MIM). Then, Pearson's Correlation Coefficient: R value is developed to measure the similarity correlation of two MIMs. MSAs from Catalytic Data Base (Catalytic Site Atlas, CSA) is used for testing. R value characterizes a specific class of residues. In contrast to individual pairwise co-evolved residues, adjacent residues without high individual MI values are found since the co-evolved relationship among them is similar to that among another set of adjacent residues. These subsequences possess some flexibility in the composition of side chains, such as the catalyzed environment.

New methods to measure residues coevolution in proteins

BACKGROUND

The covariation of two sites in a protein is often used as the degree of their coevolution. To quantify the covariation many methods have been developed and most of them are based on residues position-specific frequencies by using the mutual information (MI) model.

RESULTS

In the paper, we proposed several new measures to incorporate new biological constraints in quantifying the covariation. The first measure is the mutual information with the amino acid background distribution (MIB), which incorporates the amino acid background distribution into the marginal distribution of the MI model. The modification is made to remove the effect of amino acid evolutionary pressure in measuring covariation. The second measure is the mutual information of residues physicochemical properties (MIP), which is used to measure the covariation of physicochemical properties of two sites. The third measure called MIBP is proposed by applying residues physicochemical properties into the MIB model. Moreover, scores of our new measures are applied to a robust indicator conn(k) in finding the covariation signal of each site.

CONCLUSIONS

We find that incorporating amino acid background distribution is effective in removing the effect of evolutionary pressure of amino acids. Thus the MIB measure describes more biological background information for the coevolution of residues. Besides, our analysis also reveals that the covariation of physicochemical properties is a new aspect of coevolution information.

Plant phosphoproteomics: an update

Phosphoproteomics involves identification of phosphoproteins, precise mapping, and quantification of phosphorylation sites, and eventually, revealing their biological function. In plants, several systematic phosphoproteomic analyses have recently been performed to optimize in vitro and in vivo technologies to reveal components of the phosphoproteome. The discovery of novel substrates for specific protein kinases is also an important issue. Development of a new tool has enabled rapid identification of potential kinase substrates such as kinase assays using plant protein microarrays. Progress has also been made in quantitative and dynamic analysis of mapped phosphorylation sites. Increased quantity of experimentally verified phosphorylation sites in plants has prompted the creation of dedicated web-resources for plant-specific phosphoproteomics data. This resulted in development of computational prediction methods yielding significantly improved sensitivity and specificity for the detection of phosphorylation sites in plants when compared to methods trained on less plant-specific data. In this review, we present an update on phosphoproteomic studies in plants and summarize the recent progress in the computational prediction of plant phosphorylation sites. The application of the experimental and computed results in understanding the phosphoproteomic networks of cellular and metabolic processes in plants is discussed. This is a continuation of our comprehensive review series on plant phosphoproteomics.

Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins

BACKGROUND

Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.

RESULTS

We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information.

CONCLUSION

While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at (http://phos3d.mpimp-golm.mpg.de).

Why Should We Care about Molecular Coevolution?

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and non-parametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations.

PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor

The PhosPhAt database provides a resource consolidating our current knowledge of mass spectrometry-based identified phosphorylation sites in Arabidopsis and combines it with phosphorylation site prediction specifically trained on experimentally identified Arabidopsis phosphorylation motifs. The database currently contains 1187 unique tryptic peptide sequences encompassing 1053 Arabidopsis proteins. Among the characterized phosphorylation sites, there are over 1000 with unambiguous site assignments, and nearly 500 for which the precise phosphorylation site could not be determined. The database is searchable by protein accession number, physical peptide characteristics, as well as by experimental conditions (tissue sampled, phosphopeptide enrichment method). For each protein, a phosphorylation site overview is presented in tabular form with detailed information on each identified phosphopeptide. We have utilized a set of 802 experimentally validated serine phosphorylation sites to develop a method for prediction of serine phosphorylation (pSer) in Arabidopsis. An analysis of the current annotated Arabidopsis proteome yielded in 27,782 predicted phosphoserine sites distributed across 17,035 proteins. These prediction results are summarized graphically in the database together with the experimental phosphorylation sites in a whole sequence context. The Arabidopsis Protein Phosphorylation Site Database (PhosPhAt) provides a valuable resource to the plant science community and can be accessed through the following link http://phosphat.mpimp-golm.mpg.de.

Combining metal oxide affinity chromatography (MOAC) and selective mass spectrometry for robust identification of in vivo protein phosphorylation sites

BACKGROUND

Protein phosphorylation is accepted as a major regulatory pathway in plants. More than 1000 protein kinases are predicted in the Arabidopsis proteome, however, only a few studies look systematically for in vivo protein phosphorylation sites. Owing to the low stoichiometry and low abundance of phosphorylated proteins, phosphorylation site identification using mass spectrometry imposes difficulties. Moreover, the often observed poor quality of mass spectra derived from phosphopeptides results frequently in uncertain database hits. Thus, several lines of evidence have to be combined for a precise phosphorylation site identification strategy.

RESULTS

Here, a strategy is presented that combines enrichment of phosphoproteins using a technique termed metaloxide affinity chromatography (MOAC) and selective ion trap mass spectrometry. The complete approach involves (i) enrichment of proteins with low phosphorylation stoichiometry out of complex mixtures using MOAC, (ii) gel separation and detection of phosphorylation using specific fluorescence staining (confirmation of enrichment), (iii) identification of phosphoprotein candidates out of the SDS-PAGE using liquid chromatography coupled to mass spectrometry, and (iv) identification of phosphorylation sites of these enriched proteins using automatic detection of H3PO4 neutral loss peaks and data-dependent MS3-fragmentation of the corresponding MS2-fragment. The utility of this approach is demonstrated by the identification of phosphorylation sites in Arabidopsis thaliana seed proteins. Regulatory importance of the identified sites is indicated by conservation of the detected sites in gene families such as ribosomal proteins and sterol dehydrogenases. To demonstrate further the wide applicability of MOAC, phosphoproteins were enriched from Chlamydomonas reinhardtii cell cultures.

CONCLUSION

A novel phosphoprotein enrichment procedure MOAC was applied to seed proteins of A. thaliana and to proteins extracted from C. reinhardtii. Thus, the method can easily be adapted to suit the sample of interest since it is inexpensive and the components needed are widely available. Reproducibility of the approach was tested by monitoring phosphorylation sites on specific proteins from seeds and C. reinhardtii in duplicate experiments. The whole process is proposed as a strategy adaptable to other plant tissues providing high confidence in the identification of phosphoproteins and their corresponding phosphorylation sites.

Species-specific analysis of protein sequence motifs using mutual information

BACKGROUND

Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles derived from multiple sequence alignments provide an alternative description of functional motifs characterizing families of related sequences. Such profiles conveniently reflect functional necessities by pointing out proximity at conserved sequence positions as well as depicting distances at variable positions. Discovering significant conservation characteristics within the variable positions of profiles mirrors group-specific and, in particular, evolutionary features of the underlying sequences.

RESULTS

We describe the tool PROfile analysis based on Mutual Information (PROMI) that enables comparative analysis of user-classified protein sequences. PROMI is implemented as a web service using Perl and R as well as other publicly available packages and tools on the server-side. On the client-side platform-independence is achieved by generally applied internet delivery standards. As one possible application analysis of the zinc finger C2H2-type protein domain is introduced to illustrate the functionality of the tool.

CONCLUSION

The web service PROMI should assist researchers to detect evolutionary correlations in protein profiles of defined biological sequences. It is available at http://promi.mpimp-golm.mpg.de where additional documentation can be found.