Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Early signaling responses to divergent exercise stimuli in skeletal muscle from well-trained humans

Skeletal muscle from strength- and endurance-trained individuals represents diverse adaptive states. In this regard, AMPK-PGC-1α signaling mediates several adaptations to endurance training, while up-regulation of the Akt-TSC2-mTOR pathway may underlie increased protein synthesis after resistance exercise. We determined the effect of prior training history on signaling responses in seven strength-trained and six endurance-trained males who undertook 1 h cycling at 70% VO2peak or eight sets of five maximal repetitions of isokinetic leg extensions. Muscle biopsies were taken at rest, immediately and 3 h postexercise. AMPK phosphorylation increased after cycling in strength-trained (54%; P<0.05) but not endurance-trained subjects. Conversely, AMPK was elevated after resistance exercise in endurance- (114%; P<0.05), but not strengthtrained subjects. Akt phosphorylation increased in endurance- (50%; P<0.05), but not strengthtrained subjects after cycling but was unchanged in either group after resistance exercise. TSC2 phosphorylation was decreased (47%; P<0.05) in endurance-trained subjects following resistance exercise, but cycling had little effect on the phosphorylation state of this protein in either group. p70S6K phosphorylation increased in endurance- (118%; P<0.05), but not strength-trained subjects after resistance exercise, but was similar to rest in both groups after cycling. Similarly, phosphorylation of S6 protein, a substrate for p70 S6K, was increased immediately following resistance exercise in endurance- (129%; P<0.05), but not strength-trained subjects. In conclusion, a degree of “response plasticity” is conserved at opposite ends of the endurancehypertrophic adaptation continuum. Moreover, prior training attenuates the exercise specific signaling responses involved in single mode adaptations to training.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.