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.

Serum bone formation marker correlation with improved osseointegration in osteoporotic rats treated with simvastatin

Objective: Simvastatin has been shown to enhance osseointegration of pure titanium implants in osteoporotic rats. This study aimed to evaluate the relationship between the serum level of bone formation markers and the osseointegration of pure titanium implants in osteoporotic rats treated with simvastatin. Materials and methods: Fifty-four female Sprague Dawley rats, aged 3 months old, were randomly divided into three groups: Sham-operated group (SHAM; n=18), ovariectomized group (OVX; n=18), and ovariectomized with Simvastatin treatment group (OVX+SIM; n=18). Fifty-six days after ovariectomy, screw-shaped titanium implants were inserted into the tibiae. Simvastatin was administered orally at 5mg/kg each day after the placement of the implant in the OVX+SIM group. The animals were sacrificed at either 28 or 84 days after implantation and the undecalcified tissue sections were processed for histological analysis. Total alkaline phosphatase (ALP), bone specific alkaline phosphatase (BALP) and bone Gla protein (BGP) were measured in all animal sera collected at the time of euthanasia and correlated with the histological assessment of osseointegration. Results: The level of ALP in the OVX group was higher than the SHAM group at day 28, with no differences between the three groups at day 84. The level of BALP in the OVX+SIM group was significantly higher than both OVX and SHAM groups at days 28 and 84. Compared with day 28, the BALP level of all three groups showed a significant decrease at day 84. There were no significant differences in BGP levels between the three groups at day 28, but at day 84 the OVX+SIM group showed significantly higher levels than both the OVX and SHAM groups. There was a significant increase in BGP levels between days 28 and 84 in the OVX+SIM group. The serum bone marker levels correlated with the histological assessment showing reduced osseointegration in the OVX compared to the SHAM group which is subsequently reversed in the OVX+SIM group.

Evaluation of silk sericin as a biomaterial: In vitro growth of human corneal limbal epithelial cells on Bombyx mori sericin membranes

Sericin and fibroin are the two major proteins in the silk fibre produced by the domesticated silkworm, Bombyx mori. Fibroin has been extensively investigated as a biomaterial. We have previously shown that fibroin can function successfully as a substratum for growing cells of the eye. Sericin has been so far neglected as a biomaterial because of suspected allergenic activity. However, this misconception has now been dispelled, and sericin’s biocompatibility is currently indisputable. Aiming at promoting sericin as a possible substratum for the growth of corneal cells in order to make tissue-engineered constructs for the restoration of the ocular surface, in this study we investigated the attachment and growth in vitro of human corneal limbal epithelial cells (HLECs) on sericin-based membranes. Sericin was isolated and regenerated from the silkworm cocoons by an aqueous procedure, manufactured into membranes, and characterized (mechanical properties, structural analysis, contact angles). Primary cell cultures from two donors were established in serum-supplemented media in the presence of murine feeder cells. Membranes made of sericin and fibroin-sericin blends were assessed in vitro as substrata for HLECs in a serum-free medium, in a cell attachment assay and in a 3-day cell growth experiment. While the mechanical characteristics of sericin were found to be inferior to those of fibroin, its ability to enhance the attachment of HLECs was significantly superior to fibroin, as revealed by the PicoGreen® assay. Evidence was also obtained that cells can grow and differentiate on these substrata.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Serum YKL-40 and Interleukin 6 Levels in Hodgkin Lymphoma

Purpose Serum levels of the inflammatory markers YKL-40 and IL-6 are increased in many conditions, including cancers. We examined serum YKL-40 and IL-6 levels in patients with Hodgkin lymphoma (HL), a tumor with strong immunologic reaction to relatively few tumor cells, especially in nodular sclerosis HL. Experimental Design We analyzed Danish and Swedish patients with incident HL (N=470) and population controls from Denmark (N= 245 for YKL-40; N= 348 for IL-6). Serum YKL-40 and IL-6 levels were determined by ELISA, and log-transformed data were analysed by linear regression, adjusting for age and sex. Results Serum levels of YKL-40 and IL-6 were increased in HL patients compared to controls (YKL-40: 3.6-fold, IL-6: 8.3-fold; both p<0.0001). In samples from pre-treatment HL patients (N=176), levels were correlated with more advanced stages (ptrend 0.0001 for YKL-40 and 0.013 for IL-6) and in those with B symptoms, but levels were similar in nodular sclerosis and mixed cellularity subtypes, by EBV status, and in younger (<45 years old) and older patients. Patients tested soon after treatment onset had significantly lower levels than pre-treatment patients, but even >6 months after treatment onset, serum YKL-40 and IL-6 levels remained significantly increased, compared to controls. In patients who died (N=12), pre-treatment levels for both YKL-40 and IL-6 were higher than in survivors, although not statistically significantly. Conclusions Serum YKL-40 and IL-6 levels were increased in untreated HL patients and those with more advanced stages but did not differ significantly by HL histology. Following treatment, serum levels were significantly lower.

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.

Identification of vitronectin as a novel insulin-like growth factor-II binding protein

We have previously reported the presence of a 70 kDa insulin-like growth factor (IGF)-II-specific binding protein in chicken serum using Western ligand blotting approaches. In order to ascertain the identity of this 70 kDa IGF-II binding species, the protein has been purified from chicken serum using a combination of ion-exchange and gel-permeation chromatography. Interestingly, amino acid sequencing of the purified protein revealed that it has the same N-terminal sequence as chicken vitronectin (VN). The protein has the ability to specifically bind IGF-II and not IGF-I as determined by ligand blotting, cross-linking and competitive binding assay approaches. In addition, the protein binds 125I-des(l-6)-IGF-II, suggesting that the interaction with IGF-II is different to those with other characterized IGF-binding proteins. Importantly, we have ascertained that both human and bovine VN also specifically bind IGF-II. These results are particularly relevant in the light of the recent report that the urokinase-type plasminogen activator receptor, a protein that also binds VN, has been shown to associate with the cation-independent mannose-6-phosphate/GF-II receptor and suggest a possible role for IGF-II in cell adhesion and invasion.