An improved classification of G-protein-coupled receptors using sequence-derived features

<p>Abstract</p> <p>Background</p> <p>G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.</p> <p>Results</p> <p>We propose a new method called PCA-GPCR, to predict GPCRs using a comprehensive set of 1497 sequence-derived features. The <it>principal component analysis </it>is first employed to reduce the dimension of the feature space to 32. Then, the resulting 32-dimensional feature vectors are fed into a simple yet powerful classification algorithm, called intimate sorting, to predict GPCRs at <it>five </it>levels. The prediction at the first level determines whether a protein is a GPCR or a non-GPCR. If it is predicted to be a GPCR, then it will be further predicted into certain <it>family</it>, <it>subfamily</it>, <it>sub-subfamily </it>and <it>subtype </it>by the classifiers at the second, third, fourth, and fifth levels, respectively. To train the classifiers applied at five levels, a non-redundant dataset is carefully constructed, which contains 3178, 1589, 4772, 4924, and 2741 protein sequences at the respective levels. Jackknife tests on this training dataset show that the overall accuracies of PCA-GPCR at five levels (from the first to the fifth) can achieve up to 99.5%, 88.8%, 80.47%, 80.3%, and 92.34%, respectively. We further perform predictions on a dataset of 1238 GPCRs at the second level, and on another two datasets of 167 and 566 GPCRs respectively at the fourth level. The overall prediction accuracies of our method are consistently higher than those of the existing methods to be compared.</p> <p>Conclusions</p> <p>The comprehensive set of 1497 features is believed to be capable of capturing information about amino acid composition, sequence order as well as various physicochemical properties of proteins. Therefore, high accuracies are achieved when predicting GPCRs at all the five levels with our proposed method.</p

A Clinical Prediction Score to Guide Referral of Elderly Dialysis Patients for Kidney Transplant Evaluation.

IntroductionDialysis patients aged ≥70 years derive improved life expectancy through kidney transplantation compared to their waitlisted counterparts, but guidelines are not clear about how to identify appropriate transplantation candidates. We developed a clinical prediction score to identify elderly dialysis patients with expected 5-year survival appropriate for kidney transplantation (&gt;5 years).MethodsIncident dialysis patients in 2006-2009 aged ≥70 were identified from the United States Renal Data System database and divided into derivation and validation cohorts. Using the derivation cohort, candidate variables with a significant crude association with 5-year all-cause mortality were included in a multivariable logistic regression model to generate a scoring system. The scoring system was tested in the validation cohort and a cohort of elderly transplant recipients.ResultsCharacteristics most predictive of 5-year mortality included age &gt;80, body mass index (BMI) &lt;18, the presence of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), immobility, and being institutionalized. Factors associated with increased 5-year survival were non-white race, a primary cause of end stage renal disease (ESRD) other than diabetes, employment within 6 months of dialysis initiation, and dialysis start via arteriovenous fistula (AVF). 5-year mortality was 47% for the lowest risk score group (3.6% of the validation cohort) and &gt;90% for the highest risk cohort (42% of the validation cohort).ConclusionThis clinical prediction score could be useful for physicians to identify potentially suitable candidates for kidney transplantation

New insight on the quark condensate beyond chiral limit

With analyzing the mass function obtained by solving Dyson-Schwinger Equations, we propose a cut-off independent definition of quark condensate beyond chiral limit. With this well-defined condensate, we then analyze the evolution of the condensate and its susceptibility with the current quark mass. The susceptibility shows a critical mass in the neighborhood of the s-quark current mass, which defines a transition boundary for internal hadron dynamics.Comment: 7 pages, 5 figure

Analysis of the Duration of Rising Tone Chorus Elements

AbstractThe duration of chorus elements is an important parameter to understand chorus excitation and to quantify the effects of nonlinear wave‐particle interactions on energetic electron dynamics. In this work, we analyze the duration of rising tone chorus elements statistically using Van Allen Probes data. We present the distribution of chorus element duration (τ) as a function of magnetic local time (MLT) and the geomagnetic activity level characterized by auroral electrojet (AE) index. We show that the typical value of τ for nightside and dawnside is about 0.12 s, smaller than that for dayside and duskside by about a factor of 2 to 4. Using a previously developed hybrid code, DAWN, we suggest that the background magnetic field inhomogeneity might be an important factor in controlling the chorus element duration. We also report that τ is larger during quiet times and shorter during moderate and active periods; this result is consistent with the MLT dependence of τ and the occurrence pattern of chorus waves at different levels of geomagnetic activity. We then investigate the correlation between τ and the frequency chirping rate (Γ). We show that, from observation, τ scales with Γ as , suggesting that statistically the frequency range of chorus elements (τΓ) should be roughly the same for different elements. These findings should be useful to the further development of a theoretical model of chorus excitation and to the quantification of nonlinear wave‐particle interactions on energetic electron dynamics

Distribution patterns of small-molecule ligands in the protein universe and implications for origin of life and drug discovery

Ligand-protein mapping was found to follow a power law and the preferential attachment principle, leading to the identification of the molecules, mostly nucleotide-containing compounds, that are likely to have evolved earliest