Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

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.

Book review : "Carbon Capture and Storage Emerging Legal and Regulatory Issues" edited by Ian Havercroft, Richard Macrory, Richard B Stewart, Oxford, Hart Publishing, 2011

As the international community struggles to find a cost-effective solution to mitigate climate change and reduce greenhouse gas emissions, carbon capture and storage (CCS) has emerged as a project mechanism with the potential to assist in transitioning society towards its low carbon future. Being a politically attractive option, legal regimes to promote and approve CCS have proceeded at an accelerated pace in multiple jurisdictions including the European Union and Australia. This acceleration and emphasis on the swift commercial deployment of CCS projects has left the legal community in the undesirable position of having to advise on the strengths and weaknesses of the key features of these regimes once they have been passed and become operational. This is an area where environmental law principles are tested to their very limit. On the one hand, implementation of this new technology should proceed in a precautionary manner to avoid adverse impacts on the atmosphere, local community and broader environment. On the other hand, excessive regulatory restrictions will stifle innovation and act as a barrier to the swift deployment of CCS projects around the world. Finding the balance between precaution and innovation is no easy feat. This is an area where lawyers, academics, regulators and industry representatives can benefit from the sharing of collective experiences, both positive and negative, across the jurisdictions. This exemplary book appears to have been collated with this philosophy in mind and provides an insightful addition to the global dialogue on establishing effective national and international regimes for the implementation of CCS projects...

Legal liability for carbon capture and storage in Australia : where should the losses fall?

This article presents a critical analysis of the current and proposed CCS legal frameworks across a number of jurisdictions in Australia in order to examine the legal treatment of the risks of carbon leakage from CCS operations. It does so through an analysis of the statutory obligations and liability rules established under the offshore Commonwealth and Victorian regimes, and onshore Queensland and Victorian legislative frameworks. Exposure draft legislation for CCS laws in Western Australia is also examined. In considering where the losses will fall in the event of leakage, the potential tortious and statutory liabilities of private operators and the State are addressed alongside the operation of statutory protections from liability. The current legal treatment of CCS under the new Australian Carbon Pricing Mechanism is also critiqued.

Two SusD-like proteins encoded within a polysaccharide utilisation locus of an uncultured ruminant bacteroidetes bind strongly to cellulose

We demonstrate that two characteristic Sus-like proteins encoded within a Polysaccharide Utilisation Locus (PUL) bind strongly to cellulosic substrates and interact with plant primary cell walls. This shows associations between uncultured Bacteroidetes-affiliated lineages and cellulose in the rumen, and thus presents new PUL-derived targets to pursue regarding plant biomass degradation.

Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.