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.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Distribution feeder loads classification and decomposition

Load in distribution networks is normally measured at the 11kV supply points; little or no information is known about the type of customers and their contributions to the load. This paper proposes statistical methods to decompose an unknown distribution feeder load to its customer load sector/subsector profiles. The approach used in this paper should assist electricity suppliers in economic load management, strategic planning and future network reinforcements.

Pyrolysis decomposition of mahogony seeds (Swietenia macrophylla) for alternative fuel oil

A fixed bed pyrolysis has been designed and fabricated for obtaining liquid fuel from Mahogany seeds. The major components of the system are fixed bed pyrolysis reactor, liquid condenser and liquid collectors. The Mahogany seed in particle form is pyrolysed in an externally heated 10 cm diameter and 36 cm high fixed bed reactor with nitrogen as the carrier gas. The reactor is heated by means of a biomass source cylindrical heater from 450oC to 600oC. The products are oil, char and gas. The reactor bed temperature, running time and feed particle size are considered as process parameters. A maximum liquid yield of 54wt% of biomass feed is obtained with particle size of 1.18 mm at a reactor bed temperature of 5500C with a running time of 90 minutes. The oil is found to possess favorable flash point and reasonable density and viscosity. The higher calorific value is found to be 39.9 MJ/kg which is higher than other biomass derived pyrolysis oils.

The active site residue V266 of Chlamydial HtrA is critical for substrate binding during both in vitro and in vivo conditions

HtrA is a complex, multimeric chaperone and serine protease important for the virulence and survival of many bacteria. Chlamydia trachomatis is an obligate, intracellular bacterial pathogen that is responsible for severe disease pathology. C. trachomatis HtrA (CtHtrA) has been shown to be highly expressed in laboratory models of disease. In this study, molecular modelling of CtHtrA protein active site structure identified putative S1-S3 subsite residues I242, I265, and V266. These residues were altered by site-directed mutagenesis, and these changes were shown to considerably reduce protease activity on known substrates and resulted in a narrower and distinct range of substrates compared to wild type. Bacterial two-hybrid analysis revealed that CtHtrA is able to interact in vivo with a broad range of protein sequences with high affinity. Notably, however, the interaction was significantly altered in 35 out of 69 clones when residue V266 was mutated, indicating that this residue has an important function during substrate binding.

Chronological categorization and decomposition of customer loads

The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.

Application of TG-FTIR to study SO2 evolved during the thermal decomposition of coal-derived pyrite

The thermal decomposition of the coal-derived pyrite was studied using thermogravimetry combining with Fourier-transform infrared spectroscopy (TG-FTIR) techniques to gain knowledge on the SO2 gas evolution process and formation mechanism during the thermal decomposition of the coal-derived pyrite. The results showed that the thermal decomposition of the coal-derived pyrite which started at about 400 ◦C was complete at 600 ◦C; the gas evolved can be established by combining the DTG peak, the Gram–Schmidt curve and in situ FTIR spectroscopic evolved gas analysis. It can be observed from the spectra that the pyrolysis products for the sample mainly vary in quantity, but not in species. It was proposed that the oxidation of the coal-derived pyrite started at about 400 ◦C and that pyrrhotite and hematite were formed as primary products. The SO2 released by the thermal decomposition of the coal-derived pyrite mainly occurred in the first pyrolysis stage between 410 and 470 ◦C with the maximum rate at 444 ◦C. Furthermore, the SO2 gas evolution and formation mechanism during the thermal decomposition of the coal-derived pyrite has been proposed.