The Drosophila mutation turnip has pleiotropic behavioral effects and does not specifically affect learning

The Drosophila mutant turnip (tur) was isolated on the basis of its poor performance in an olfactory learning task, and also has a reduction in protein kinase C (PKC) activity. PKC has been found in the nervous systems of a wide range of organisms and appears to have an important role in learning and memory-related processes. Unfortunately, previous reports documenting the learning defect of tur lacked the controls required to assess the origins of the poor performance of the mutant. We have analyzed the effects of the tur mutation on both associative and nonassociative learning as well as on PKC activity. Additionally, the effects of the mutation on the task-relevant sensorimotor abilities of the flies were assessed. Although we were able to replicate previous behavioral and biochemical results obtained with tur, we discovered that the tur mutation also affected response to electric shock and caused a drastic reduction in the locomotor ability of the flies. Because locomotion is an essential component of the learning assays, this result makes it impossible to conclude that tur specifically affects learning and demonstrates the crucial importance of sensorimotor controls in conditioning experiments

Evolutionary Trace Annotation Server: automated enzyme function prediction in protein structures using 3D templates

Summary:The Evolutionary Trace Annotation (ETA) Server predicts enzymatic activity. ETA starts with a structure of unknown function, such as those from structural genomics, and with no prior knowledge of its mechanism uses the phylogenetic Evolutionary Trace (ET) method to extract key functional residues and propose a function-associated 3D motif, called a 3D template. ETA then searches previously annotated structures for geometric template matches that suggest molecular and thus functional mimicry. In order to maximize the predictive value of these matches, ETA next applies distinctive specificity filters—evolutionary similarity, function plurality and match reciprocity. In large scale controls on enzymes, prediction coverage is 43% but the positive predictive value rises to 92%, thus minimizing false annotations. Users may modify any search parameter, including the template. ETA thus expands the ET suite for protein structure annotation, and can contribute to the annotation efforts of metaservers

Incorporating background frequency improves entropy-based residue conservation measures

BACKGROUND: Several entropy-based methods have been developed for scoring sequence conservation in protein multiple sequence alignments. High scoring amino acid positions may correlate with structurally or functionally important residues. However, amino acid background frequencies are usually not taken into account in these entropy-based scoring schemes. RESULTS: We demonstrate that using a relative entropy measure that incorporates amino acid background frequency results in improved performance in identifying functional sites from protein multiple sequence alignments. CONCLUSION: Our results suggest that the application of appropriate background frequency information may lead to more biologically relevant results in many areas of bioinformatics

Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks

ResBoost: characterizing and predicting catalytic residues in enzymes

Abstract Background Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. Results We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). Conclusion ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA

A Combinatorial Approach to Detect Coevolved Amino Acid Networks in Protein Families of Variable Divergence

Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence

Separation of Recombination and SOS Response in Escherichia coli RecA Suggests LexA Interaction Sites

RecA plays a key role in homologous recombination, the induction of the DNA damage response through LexA cleavage and the activity of error-prone polymerase in Escherichia coli. RecA interacts with multiple partners to achieve this pleiotropic role, but the structural location and sequence determinants involved in these multiple interactions remain mostly unknown. Here, in a first application to prokaryotes, Evolutionary Trace (ET) analysis identifies clusters of evolutionarily important surface amino acids involved in RecA functions. Some of these clusters match the known ATP binding, DNA binding, and RecA-RecA homo-dimerization sites, but others are novel. Mutation analysis at these sites disrupted either recombination or LexA cleavage. This highlights distinct functional sites specific for recombination and DNA damage response induction. Finally, our analysis reveals a composite site for LexA binding and cleavage, which is formed only on the active RecA filament. These new sites can provide new drug targets to modulate one or more RecA functions, with the potential to address the problem of evolution of antibiotic resistance at its root

Reduced conditioned fear response in mice that lack Dlx1 and show subtype-specific loss of interneurons

The inhibitory GABAergic system has been implicated in multiple neuropsychiatric diseases such as schizophrenia and autism. The Dlx homeobox transcription factor family is essential for development and function of GABAergic interneurons. Mice lacking the Dlx1 gene have postnatal subtype-specific loss of interneurons and reduced IPSCs in their cortex and hippocampus. To ascertain consequences of these changes in the GABAergic system, we performed a battery of behavioral assays on the Dlx1 mutant mice, including zero maze, open field, locomotor activity, food intake, rotarod, tail suspension, fear conditioning assays (context and trace), prepulse inhibition, and working memory related tasks (spontaneous alteration task and spatial working memory task). Dlx1 mutant mice displayed elevated activity levels in open field, locomotor activity, and tail suspension tests. These mice also showed deficits in contextual and trace fear conditioning, and possibly in prepulse inhibition. Their learning deficits were not global, as the mutant mice did not differ from the wild-type controls in tests of working memory. Our findings demonstrate a critical role for the Dlx1 gene, and likely the subclasses of interneurons that are affected by the lack of this gene, in behavioral inhibition and associative fear learning. These observations support the involvement of particular components of the GABAergic system in specific behavioral phenotypes related to complex neuropsychiatric diseases

L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance