DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy.

MOTIVATION: The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved. RESULTS: In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy. AVAILABILITY: The DomHR is available at http://cal.tongji.edu.cn/domain/.

Shape string: a new feature for prediction of DNA-binding residues.

Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues. We employed the new features and the position-specific scoring matrix (PSSM) for modeling and prediction. The results of a benchmark dataset showed that our approach significantly improved the accuracy of the predictor. The overall accuracy of our approach reached 85.86% with 85.02% sensitivity and 86.02% specificity. The results also demonstrated that shape string is a powerful descriptor for the prediction of DNA-binding residues. The additional two related features enhanced the predictive value.

Response to weaning and dietary L-glutamine supplementation: metabolomic analysis in piglets by gas chromatography/mass spectrometry.

A novel metabolomic method based on gas chromatography/mass spectrometry (GC-MS) was applied to determine the metabolites in the serum of piglets in response to weaning and dietary L-glutamine (Gln) supplementation. Thirty-six 21-d-old piglets were randomly assigned into three groups. One group continued to suckle from the sows (suckling group), whereas the other two groups were weaned and their diets were supplemented with 1% (w/w) Gln or isonitrogenous L-alanine, respectively, representing Gln group or control group. Serum samples were collected to characterize metabolites after a 7-d treatment. Results showed that twenty metabolites were down-regulated significantly (P<0.05) in control piglets compared with suckling ones. These data demonstrated that early weaning causes a wide range of metabolic changes across arginine and proline metabolism, aminosugar and nucleotide metabolism, galactose metabolism, glycerophospholipid metabolism, biosynthesis of unsaturated fatty acid, and fatty acid metabolism. Dietary Gln supplementation increased the levels of creatinine, D-xylose, 2-hydroxybutyric acid, palmitelaidic acid, and α-L-galactofuranose (P<0.05) in early weaned piglets, and were involved in the arginine and proline metabolism, carbohydrate metabolism, and fatty acid metabolism. A leave-one-out cross-validation of random forest analysis indicated that creatinine was the most important metabolite among the three groups. Notably, the concentration of creatinine in control piglets was decreased (P=0.00001) compared to the suckling piglets, and increased (P=0.0003) in Gln-supplemented piglets. A correlation network for weaned and suckling piglets revealed that early weaning changed the metabolic pathways, leading to the abnormality of carbohydrate metabolism, amino acid metabolism, and lipid metabolism, which could be partially improved by dietary Gln supplementation. These findings provide fresh insight into the complex metabolic changes in response to early weaning and dietary Gln supplementation in piglets.

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins.

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold.

Identification of the subcellular localization of mycobacterial proteins using localization motifs.

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model. These localization motifs are employed as features for identifying the subcellular localization of mycobacterial proteins. Our approach provides more accurate results than previous methods and was tested on an independent dataset recently obtained from an experimental study to provide a first and reasonably accurate prediction of subcellular localization. Our approach can also be used for large-scale prediction of new protein entries in the UniportKB database and of protein sequences obtained experimentally. In addition, our approach identified many local motifs involved with the subcellular localization that also interact with the environment. Thus, our method may have widespread applications both in the study of the functions of mycobacterial proteins and in the search for a potential vaccine target for designing drugs.