Phosphorylation site prediction in plants.

Protein phosphorylation events on serine, threonine, and tyrosine residues are the most pervasive protein covalent bond modifications in plant signaling. Both low and high throughput studies reveal the importance of phosphorylation in plant molecular biology. Although becoming more and more common, the proteome-wide screening on phosphorylation by experiments remains time consuming and costly. Therefore, in silico prediction methods are proposed as a complementary analysis tool to enhance the phosphorylation site identification, develop biological hypothesis, or help experimental design. These methods build statistical models based on the experimental data, and they do not have some of the technical-specific bias, which may have advantage in proteome-wide analysis. More importantly computational methods are very fast and cheap to run, which makes large-scale phosphorylation identifications very practical for any types of biological study. Thus, the phosphorylation prediction tools become more and more popular. In this chapter, we will focus on plant specific phosphorylation site prediction tools, with essential illustration of technical details and application guidelines. We will use Musite, PhosPhAt and PlantPhos as the representative tools. We will present the results on the prediction of the Arabidopsis protein phosphorylation events to give users a general idea of the performance range of the three tools, together with their strengths and limitations. We believe these prediction tools will contribute more and more to the plant phosphorylation research community.

COCO: a simple tool to enrich the representation of conformational variability in NMR structures.

NMR structures are typically deposited in databases such as the PDB in the form of an ensemble of structures. Generally, each of the models in such an ensemble satisfies the experimental data and is equally valid. No unique solution can be calculated because the experimental NMR data is insufficient, in part because it reflects the conformational variability and dynamical behavior of the molecule in solution. Even for relatively rigid molecules, the limited number of structures that are typically deposited cannot completely encompass the structural diversity allowed by the observed NMR data, but they can be chosen to try and maximize its representation. We describe here the adaptation and application of techniques more commonly used to examine large ensembles from molecular dynamics simulations, to the analysis of NMR ensembles. The approach, which is based on principal component analysis, we call COCO ("Complementary Coordinates"). The COCO approach analyses the distribution of an NMR ensemble in conformational space, and generates a new ensemble that fills "gaps" in the distribution. The method is very rapid, and analysis of a 25-member ensemble and generation of a new 25 member ensemble typically takes 1-2 min on a conventional workstation. Applied to the 545 structures in the RECOORD database, we find that COCO generates new ensembles that are as structurally diverse-both from each other and from the original ensemble-as are the structures within the original ensemble. The COCO approach does not explicitly take into account the NMR restraint data, yet in tests on selected structures from the RECOORD database, the COCO ensembles are frequently good matches to this data, and certainly are structures that can be rapidly refined against the restraints to yield high-quality, novel solutions. COCO should therefore be a useful aid in NMR structure refinement and in other situations where a richer representation of conformational variability is desired-for example in docking studies. COCO is freely accessible via the website www.ccpb.ac.uk/COCO.

Separating drugs from nondrugs: a statistical approach using atom pair distributions.

A computational approach to quantify the druglike character of chemical compounds is presented. For this purpose, the distribution of atom types and their pair-wise combinations in known drugs and nondrugs was examined. Statistical analysis of the occurrence probabilities was used to derive a drug-likeliness score on a logarithmic scale. "Typical" pharmaceutical agents exhibit scores greater than 0.3, while for ordinary substances, values below 0 are expected. Although any kind of fitting or error minimization scheme is absent in this method, confirmed drugs are predicted with an accuracy of at least 71%. Many falsely predicted nondrugs were found to closely resemble actual drugs or to contain unsuitable substitution patterns that can easily be ruled out by applying medicinal knowledge. As the outlined method is computationally inexpensive, this drug-likeliness score can therefore be used as a filter for the in silico screening of large substance databases.