Discrimination of outer membrane proteins with improved performance

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) perform diverse functional roles in Gram-negative bacteria. Identification of outer membrane proteins is an important task.</p> <p>Results</p> <p>This paper presents a method for distinguishing outer membrane proteins (OMPs) from non-OMPs (that is, globular proteins and inner membrane proteins (IMPs)). First, we calculated the average residue compositions of OMPs, globular proteins and IMPs separately using a training set. Then for each protein from the test set, its distances to the three groups were calculated based on residue composition using a weighted Euclidean distance (WED) approach. Proteins from the test set were classified into OMP versus non-OMP classes based on the least distance. The proposed method can distinguish between OMPs and non-OMPs with 91.0% accuracy and 0.639 Matthews correlation coefficient (MCC). We then improved the method by including homologous sequences into the calculation of residue composition and using a feature-selection method to select the single residue and di-peptides that were useful for OMP prediction. The final method achieves an accuracy of 96.8% with 0.859 MCC. In direct comparisons, the proposed method outperforms previously published methods.</p> <p>Conclusion</p> <p>The proposed method can identify OMPs with improved performance. It will be very helpful to the discovery of OMPs in a genome scale.</p

Functional discrimination of membrane proteins using machine learning techniques

<p>Abstract</p> <p>Background</p> <p>Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters.</p> <p>Results</p> <p>We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane) showed the accuracy of 82%.</p> <p>Conclusion</p> <p>The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.</p

Genome-wide association meta-analyses to identify common genetic variants associated with hallux valgus in Caucasian and African Americans

Objective Hallux valgus (HV) affects ∼36% of Caucasian adults. Although considered highly heritable, the underlying genetic determinants are unclear. We conducted the first genome-wide association study (GWAS) aimed to identify genetic variants associated with HV. Methods HV was assessed in three Caucasian cohorts (n=2263, n=915 and n=1231 participants, respectively). In each cohort, a GWAS was conducted using 2.5 M imputed SNPs. Mixed-effect regression with the additive genetic model adjusted for age, sex, weight and within-family correlations was used for both sex-specific and combined analyses. To combine GWAS results across cohorts, fixed-effect inverse-variance meta-analyses were used. Following meta-analyses, top-associated findings were also examined in an African American cohort (n=327). Results The proportion of HV variance explained by genome-wide genotyped SNPs was 50% in men and 48% in women. A higher proportion of genetic determinants of HV were sex specific. The most significantly associated SNP in men was rs9675316 located on chr17q23-a24 near the AXIN2 gene (p=0.000000546×10−7); the most significantly associated SNP in women was rs7996797 located on chr13q14.1-q14.2 near the ESD gene (p=0.000000721×10−7). Genome-wide significant SNP-by-sex interaction was found for SNP rs1563374 located on chr11p15.1 near the MRGPRX3 gene (interaction p value =0.0000000041×10−9). The association signals diminished when combining men and women. Conclusions The findings suggest that the potential pathophysiological mechanisms of HV are complex and strongly underlined by sex-specific interactions. The identified genetic variants imply contribution of biological pathways observed in osteoarthritis as well as new pathways, influencing skeletal development and inflammation

A Palaeolithic diet improves glucose tolerance more than a Mediterranean-like diet in individuals with ischaemic heart disease.

Aims/hypothesis Most studies of diet in glucose intolerance and type 2 diabetes have focused on intakes of fat, carbohydrate, fibre, fruits and vegetables. Instead, we aimed to compare diets that were available during human evolution with more recently introduced ones. Methods Twenty-nine patients with ischaemic heart disease plus either glucose intolerance or type 2 diabetes were randomised to receive (1) a Palaeolithic ('CyOld Stone Age') diet (n=14), based on lean meat, fish, fruits, vegetables, root vegetables, eggs and nuts; or (2) a Consensus (Mediterranean-like) diet (n=15), based on whole grains, low-fat dairy products, vegetables, fruits, fish, oils and margarines. Primary outcome variables were changes in weight, waist circumference and plasma glucose AUC (AUC Glucose(0-120)) and plasma insulin AUC (AUC Insulin(0-120)) in OGTTs. Results Over 12 weeks, there was a 26% decrease of AUC Glucose(0-120) (p=0.0001) in the Palaeolithic group and a 7% decrease (p=0.08) in the Consensus group. The larger (p=0.001) improvement in the Palaeolithic group was independent (p=0.0008) of change in waist circumference (-5.6 cm in the Palaeolithic group, -2.9 cm in the Consensus group; p=0.03). In the study population as a whole, there was no relationship between change in AUC Glucose(0-120) and changes in weight (r=-0.06, p=0.9) or waist circumference (r=0.01, p=1.0). There was a tendency for a larger decrease of AUC Insulin(0-120) in the Palaeolithic group, but because of the strong association between change in AUC Insulin(0-120) and change in waist circumference (r=0.64, p=0.0003), this did not remain after multivariate analysis. Conclusions/interpretationA Palaeolithic diet may improve glucose tolerance independently of decreased waist circumference

A 3-Dimensional Trimeric β-Barrel Model for Chlamydia MOMP Contains Conserved and Novel Elements of Gram-Negative Bacterial Porins

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ∼60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest β-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded β-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320–334 cannot be modeled as β-strands that span the entire membrane, as is consistently observed in published 16-stranded β-barrel crystal structures. Given the ambiguous results for β-strand delineation found in this study, recent publications of membrane β-barrel structures breaking with the canonical rule for an even number of β-strands, findings of β-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed