Efficient Identification of Critical Residues Based Only on Protein Structure by Network Analysis

Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p

ePlant and the 3D Data Display Initiative: Integrative Systems Biology on the World Wide Web

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed “ePlant” (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the “3D Data Display Initiative” (http://3ddi.org)

Evolutionary Diversification of Plant Shikimate Kinase Gene Duplicates

Shikimate kinase (SK; EC 2.7.1.71) catalyzes the fifth reaction of the shikimate pathway, which directs carbon from the central metabolism pool to a broad range of secondary metabolites involved in plant development, growth, and stress responses. In this study, we demonstrate the role of plant SK gene duplicate evolution in the diversification of metabolic regulation and the acquisition of novel and physiologically essential function. Phylogenetic analysis of plant SK homologs resolves an orthologous cluster of plant SKs and two functionally distinct orthologous clusters. These previously undescribed genes, shikimate kinase-like 1 (SKL1) and -2 (SKL2), do not encode SK activity, are present in all major plant lineages, and apparently evolved under positive selection following SK gene duplication over 400 MYA. This is supported by functional assays using recombinant SK, SKL1, and SKL2 from Arabidopsis thaliana (At) and evolutionary analyses of the diversification of SK-catalytic and -substrate binding sites based on theoretical structure models. AtSKL1 mutants yield albino and novel variegated phenotypes, which indicate SKL1 is required for chloroplast biogenesis. Extant SKL2 sequences show a strong genetic signature of positive selection, which is enriched in a protein–protein interaction module not found in other SK homologs. We also report the first kinetic characterization of plant SKs and show that gene expression diversification among the AtSK inparalogs is correlated with developmental processes and stress responses. This study examines the functional diversification of ancient and recent plant SK gene duplicates and highlights the utility of SKs as scaffolds for functional innovation

Structural and biochemical analysis of Bacillus anthracis prephenate dehydrogenase reveals an unusual mode of inhibition by tyrosine via the ACT domain

Tyrosine biosynthesis via the shikimate pathway is absent in humans and other animals, making it an attractive target for next-generation antibiotics, which is increasingly important due to the looming proliferation of multidrug-resistant pathogens. Tyrosine biosynthesis is also of commercial importance for the environmentally friendly production of numerous compounds, such as pharmaceuticals, opioids, aromatic polymers, and petrochemical aromatics. Prephenate dehydrogenase (PDH) catalyzes the penultimate step of tyrosine biosynthesis in bacteria: the oxidative decarboxylation of prephenate to 4-hydroxyphenylpyruvate. The majority of PDHs are competitively inhibited by tyrosine and consist of a nucleotide-binding domain and a dimerization domain. Certain PDHs, including several from pathogens on the WHO priority list of antibiotic-resistant bacteria, possess an additional ACT domain. However, biochemical and structural knowledge was lacking for these enzymes. In this study, we successfully established a recombinant protein expression system for PDH from Bacillus anthracis (BaPDH), the causative agent of anthrax, and determined the structure of a BaPDH ternary complex with NAD(+) and tyrosine, a binary complex with tyrosine, and a structure of an isolated ACT domain dimer. We also conducted detailed kinetic and biophysical analyses of the enzyme. We show that BaPDH is allosterically regulated by tyrosine binding to the ACT domains, resulting in an asymmetric conformation of the BaDPH dimer that sterically prevents prephenate binding to either active site. The presented mode of allosteric inhibition is unique compared to both the competitive inhibition established for other PDHs and to the allosteric mechanisms for other ACT-containing enzymes. This study provides new structural and mechanistic insights that advance our understanding of tyrosine biosynthesis in bacteria