FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

How accurate and statistically robust are catalytic site predictions based on closeness centrality?

<p>Abstract</p> <p>Background</p> <p>We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex <it>i </it>and all other vertices.</p> <p>Results</p> <p>We benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.</p> <p>Conclusion</p> <p>Our results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.</p

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

Novel Feature for Catalytic Protein Residues Reflecting Interactions with Other Residues

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10fold crossvalidation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structurefunction relationship

The search for the ideal biocatalyst

While the use of enzymes as biocatalysts to assist in the industrial manufacture of fine chemicals and pharmaceuticals has enormous potential, application is frequently limited by evolution-led catalyst traits. The advent of designer biocatalysts, produced by informed selection and mutation through recombinant DNA technology, enables production of process-compatible enzymes. However, to fully realize the potential of designer enzymes in industrial applications, it will be necessary to tailor catalyst properties so that they are optimal not only for a given reaction but also in the context of the industrial process in which the enzyme is applied

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches

Optimization of Rifamycin B fermentation in shake flasks via a machine-learning-based approach

Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions Eire possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity. (C) 2004

Combined effects of carbon, nitrogen and phosphorus substrates on D-ribose production via transketolase deficient strain of Bacillus pumilus

BACKGROUND: D-ribose, a five carbon sugar, can be produced via mutant strains with impairment in the non-oxidative part of the pentose phosphate pathway (PPP). D-ribose is an important intermediate in the pharmaceutical industry. This work focuses on the effect of various substrates on D-ribose production with a transketolase deficient strain of Bacillus pumilus IFO13322. RESULT: D-ribose titer is found to increase with a concomitant decrease in the yield (on a g product g(-1) substrate basis) with increasing glucose concentration. Likewise, higher concentration of ammonium sulfate led to lower product yield while glucose and corn steep liquor concentrations were kept unaltered. Among the complex nitrogen substrates, cas amino acids showed better product yield than corn steep liquor. When tested for supplements of defined amino acids, media supplemented with tyrosine, phenylalanine, tryptophan and histidine gave better yields and higher biomass growth. This is possibly due to the fact that mutant strain is an aromatic amino acids auxotroph owing to impairment in aromatic amino acid biosynthesis. Further, higher phosphate concentrations in the media led to greater biomass formation and lower D-ribose yield. CONCLUSIONS: At higher glucose or ammonium sulfate concentrations, a greater proportion of the carbon was channeled toward byproducts such as acetate, and acetoin and biomass formation. Thus, the relative rates of PPP and in turn the D-ribose yield were dependent on not only the carbon substrate concentration but also the nitrogen and phosphorus substrates. (C) 2008 Society of Chemical Industry

Gene essentiality, conservation index and co-evolution of genes in cyanobacteria

Cyanobacteria, a group of photosynthetic prokaryotes, dominate the earth with similar to 10(15) g wet biomass. Despite diversity in habitats and an ancient origin, cyanobacterial phylum has retained a significant core genome. Cyanobacteria are being explored for direct conversion of solar energy and carbon dioxide into biofuels. For this, efficient cyanobacterial strains will need to be designed via metabolic engineering. This will require identification of target knockouts to channelize the flow of carbon toward the product of interest while minimizing deletions of essential genes. We propose "Gene Conservation Index" (GCI) as a quick measure to predict gene essentiality in cyanobacteria. GCI is based on phylogenetic profile of a gene constructed with a reduced dataset of cyanobacterial genomes. GCI is the percentage of organism clusters in which the query gene is present in the reduced dataset. Of the 750 genes deemed to be essential in the experimental study on S. elongatus PCC 7942, we found 494 to be conserved across the phylum which largely comprise of the essential metabolic pathways. On the contrary, the conserved but non-essential genes broadly comprise of genes required under stress conditions. Exceptions to this rule include genes such as the glycogen synthesis and degradation enzymes, deoxyribose-phosphate aldolase (DERA), glucose-6-phosphate 1-dehydrogenase (zwf) and fructose-1,6-bisphosphatase class1, which are conserved but non-essential. While the essential genes are to be avoided during gene knockout studies as potentially lethal deletions, the non-essential but conserved set of genes could be interesting targets for metabolic engineering. Further, we identify clusters of co-evolving genes (CCG), which provide insights that may be useful in annotation. Principal component analysis (PCA) plots of the CCGs are demonstrated as data visualization tools that are complementary to the conventional heatmaps. Our dataset consists of phylogenetic profiles for 23,643 non-redundant cyanobacterial genes. We believe that the data and the analysis presented here will be a great resource to the scientific community interested in cyanobacteria