The dolphin proline-rich antimicrobial peptide Tur1A inhibits protein synthesis by targeting the bacterial ribosome

Proline-rich antimicrobial peptides (PrAMPs) internalize into susceptible bacteria using specific transporters and interfere with protein synthesis and folding. To date, mammalian PrAMPs have so far only been identified in artiodactyls. Since cetaceans are co-phyletic with artiodactyls, we mined the genome of the bottlenose dolphin Tursiops truncates, leading to the identification of two PrAMPs, Tur1A and Tur1B. Tur1A, which is orthologous to the bovine PrAMP Bac7, is internalized into E. coli without damaging the membranes using the inner membrane transporters SbmA and YjiL/MdM. Furthermore, like Bac7, Tur1A also inhibits bacterial protein synthesis by binding to the ribosome and blocking the transition from the initiation to the elongation phase. By contrast, Tur1B is a poor inhibitor of protein synthesis and may utilize another mechanism of action. An X-ray structure of Tur1A bound within the ribosomal exit tunnel provides a basis to develop these peptides as novel antimicrobial agents

Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling

The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown

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

Does prenatal micronutrient supplementation improve children's mental development? A systematic review

<p>Abstract</p> <p>Background</p> <p>Although maternal nutrient status influences all aspects of fetal development including the brain, the impact of micronutrient supplementation on the baby's mental function is a topic of debate. This systematic review assesses the effect of single and multiple micronutrient supplementation during pregnancy on offspring mental development.</p> <p>Methods</p> <p>Eleven electronic literature databases were searched using key terms of various combinations and filter string terms. Reference lists of articles selected for review were scanned for citations fitting the same inclusion criteria. Each stage of the literature retrieval and review process was conducted independently by two reviewers. The CONSORT checklist was used to assess study quality.</p> <p>Results</p> <p>A total of 1316 articles were retrieved from the electronic database search, of which 18 met the inclusion criteria and were evaluated. The selected studies were randomized controlled trials published from 1983 to 2010, with high variance in sample size, intervention type, and outcome measures. The median CONSORT score was 15 (range 12 - 19). Due to inconsistent interventions and outcome measures among the studies, no conclusive evidence was found that enhancing the intrauterine environment through micronutrient supplementation was associated with child mental development in a number of dimensions. There was some evidence to support n-3 fatty acids or multi-micronutrients having some positive effect on mental development, but the evidence for single nutrients was much weaker.</p> <p>Conclusions</p> <p>The study of children's mental outcomes as a function of prenatal supplementation is still relatively new, but the results of this systematic review suggest that further work with multiple micronutrients and/or n-3 fatty acids should be conducted.</p

Exome Sequencing Identifies Compound Heterozygous Mutations in CYP4V2 in a Pedigree with Retinitis Pigmentosa

Retinitis pigmentosa (RP) is a heterogeneous group of progressive retinal degenerations characterized by pigmentation and atrophy in the mid-periphery of the retina. Twenty two subjects from a four-generation Chinese family with RP and thin cornea, congenital cataract and high myopia is reported in this study. All family members underwent complete ophthalmologic examinations. Patients of the family presented with bone spicule-shaped pigment deposits in retina, retinal vascular attenuation, retinal and choroidal dystrophy, as well as punctate opacity of the lens, reduced cornea thickness and high myopia. Peripheral venous blood was obtained from all patients and their family members for genetic analysis. After mutation analysis in a few known RP candidate genes, exome sequencing was used to analyze the exomes of 3 patients III2, III4, III6 and the unaffected mother II2. A total of 34,693 variations shared by 3 patients were subjected to several filtering steps against existing variation databases. Identified variations were verified in the rest family members by PCR and Sanger sequencing. Compound heterozygous c.802-8_810del17insGC and c.1091-2A>G mutations of the CYP4V2 gene, known as genetic defects for Bietti crystalline corneoretinal dystrophy, were identified as causative mutations for RP of this family

Omega-3 Fatty Acids from Fish Oil Lower Anxiety, Improve Cognitive Functions and Reduce Spontaneous Locomotor Activity in a Non-Human Primate

Omega-3 (ω3) polyunsaturated fatty acids (PUFA) are major components of brain cells membranes. ω3 PUFA-deficient rodents exhibit severe cognitive impairments (learning, memory) that have been linked to alteration of brain glucose utilization or to changes in neurotransmission processes. ω3 PUFA supplementation has been shown to lower anxiety and to improve several cognitive parameters in rodents, while very few data are available in primates. In humans, little is known about the association between anxiety and ω3 fatty acids supplementation and data are divergent about their impact on cognitive functions. Therefore, the development of nutritional studies in non-human primates is needed to disclose whether a long-term supplementation with long-chain ω3 PUFA has an impact on behavioural and cognitive parameters, differently or not from rodents. We address the hypothesis that ω3 PUFA supplementation could lower anxiety and improve cognitive performances of the Grey Mouse Lemur (Microcebus murinus), a nocturnal Malagasy prosimian primate. Adult male mouse lemurs were fed for 5 months on a control diet or on a diet supplemented with long-chain ω3 PUFA (n = 6 per group). Behavioural, cognitive and motor performances were measured using an open field test to evaluate anxiety, a circular platform test to evaluate reference spatial memory, a spontaneous locomotor activity monitoring and a sensory-motor test. ω3-supplemented animals exhibited lower anxiety level compared to control animals, what was accompanied by better performances in a reference spatial memory task (80% of successful trials vs 35% in controls, p<0.05), while the spontaneous locomotor activity was reduced by 31% in ω3-supplemented animals (p<0.001), a parameter that can be linked with lowered anxiety. The long-term dietary ω3 PUFA supplementation positively impacts on anxiety and cognitive performances in the adult mouse lemur. The supplementation of human food with ω3 fatty acids may represent a valuable dietary strategy to improve behavioural and cognitive functions

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