Bilateral similarity function: A novel and universal method for similarity analysis of biological sequences

Bilateral similarity function is designed for analyzing the similarities of biological sequences such as DNA, RNA secondary structure or protein in this paper. The defined function can perform comprehensive comparison between sequences remarkably well, both in terms of the Hamming distance of two compared sequences and the corresponding location difference. Compared with the existing methods for similarity analysis, the examination of similarities/dissimilarities illustrates that the proposed method with the computational complexity of O(N) is effective for these three kinds of biological sequences, and bears the universality for them.     

A novel neural response algorithm for protein function prediction

Background

Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.

Results

We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.

Conclusions

The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.

     

FrankSum: new feature selection method for protein function prediction

In the study of in silico functional genomics, improving the performance of protein function prediction is the ultimate goal for identifying proteins associated with defined cellular functions. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have successfully selected biologically significant features for protein function prediction. This was performed using a new feature selection method (FrankSum) that avoids data distribution assumptions, uses a data independent measurement (p-value) within the feature, identifies redundancy between features and uses an appropriate ranking criterion for feature selection. We have shown that classifiers generated from features selected by FrankSum outperforms classifiers generated from full feature sets, randomly selected features and features selected from the Wrapper method. We have also shown the features are concordant across all species and top ranking features are biologically informative. We conclude that feature selection is vital for successful protein function prediction and FrankSum is one of the feature selection methods that can be applied successfully to such a domain.     

Pore-forming protein toxins: from structure to function

Pore-forming protein toxins (PFTs) are one of Nature's most potent biological weapons. An essential feature of their toxicity is the remarkable property that PFTs can exist either in a stable water-soluble state or as an integral membrane pore. In order to convert from the water-soluble to the membrane state, the toxin must undergo large conformational changes. There are now more than a dozen PFTs for which crystal structures have been determined and the nature of the conformational changes they must undergo is beginning to be understood. Although they differ markedly in their primary, secondary, tertiary and quaternary structures, nearly all can be classified into one of two families based on the types of pores they are thought to form: alpha-PFTs or beta-PFTs. Recent work suggests a number of common features in the mechanism of membrane insertion may exist for each class.     

基于分组重量编码的蛋白质功能预测

从蛋白质序列出发,采用分组重量编码(Encoding Based on Grouped Weight,简记EBGW),并结合最近邻居算法对蛋白质功能进行预测。对酵母(Saccharomyces cerevisiae)蛋白质的1826条序列进行预测,整体预测准确率与其他基于序列信息的蛋白质功能预测方法相当。实验结果表明基于EBGW编码方案的新方法可有效地应用于蛋白质功能预测。     

A novel method for N-terminal acetylation prediction

The NetAcet method has been developed to make predictions of N-terminal acetylation sites, but more information of the data set could be utilized to improve the performance of the model. By employing a new way to extract patterns from sequences and using a sample balancing mechanism, we obtained a correlation coefficient of 0.85, and a sensitivity of 93% on an independent mammalian data set. A web server utilizing this method has been constructed and is available at http://166.111.24.5/acetylation.html.     

A novel HPLC-UV-MS method for quantitative analysis of protein glycosylation

Monoclonal antibody samples derived from transgenic plants (plantibodies) may often contain significant amounts of aglycosylated variants. Because glycosylated and non-/de-glycosylated proteins exhibit different functional and pharmacokinetic properties, accurate measurement of non- and de-glycosylated glycoprotein abundances is important. Glycosylation of plant-derived glycoproteins presents specific challenges. Here we describe a novel method to accurately measure relative and absolute amounts of non-glycosylated, de-glycosylated, and total glycosylated protein using an HPLC-UV-MS methodology. Additionally, these results were compared with glycopeptide profiling by MALDI MS. Our studies demonstrated that the quantitative aspect of HPLC-UV method was superior to MALDI MS profiling, which significantly overestimated the relative amounts of aglycosylated species in the isolated glycopeptide fractions.     

Adaptive diffusion kernel learning from biological networks for protein function prediction

Background  

Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data.     

A novel radioprotective function for the mitochondrial tumor suppressor protein Fus1

FUS1/TUSC2 is a mitochondrial tumor suppressor with activity to regulate cellular oxidative stress by maintaining balanced ROS production and mitochondrial homeostasis. Fus1 expression is inhibited by ROS, suggesting that individuals with a high level of ROS may have lower Fus1 in normal tissues and, thus, may be more prone to oxidative stress-induced side effects of cancer treatment, including radiotherapy. As the role of Fus1 in the modulation of cellular radiosensitivity is unknown, we set out to determine molecular mechanisms of Fus1 involvement in the IR response in normal tissues. Mouse whole-body irradiation methodology was employed to determine the role for Fus1 in the radiation response and explore underlying molecular mechanisms. Fus1^−/− mice were more susceptible to radiation compared with Fus1^+/+ mice, exhibiting increased mortality and accelerated apoptosis of the GI crypt epithelial cells. Following untimely reentrance into the cell cycle, the Fus1^−/− GI crypt cells died at accelerated rate via mitotic catastrophe that resulted in diminished and/or delayed crypt regeneration after irradiation. At the molecular level, dysregulated dynamics of activation of main IR response proteins (p53, NFκB, and GSK-3β), as well as key signaling pathways involved in oxidative stress response (SOD2, PRDX1, and cytochrome c), apoptosis (BAX and PARP1), cell cycle (Cyclins B1 and D1), and DNA repair (γH2AX) were found in Fus1^−/− cells after irradiation. Increased radiosensitivity of other tissues, such as immune cells and hair follicles was also detected in Fus1^−/− mice. Our findings demonstrate a previously unknown radioprotective function of the mitochondrial tumor suppressor Fus1 in normal tissues and suggest new individualized therapeutic approaches based on Fus1 expression.     

Identification of function-associated loop motifs and application to protein function prediction

MOTIVATION: The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations. RESULTS: We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST. CONTACT: boliva@imim.es SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.     

A novel sequence-based method for phosphorylation site prediction with feature selection and analysis

Phosphorylation is one of the most important post-translational modifications, and the identification of protein phosphorylation sites is particularly important for studying disease diagnosis. However, experimental detection of phosphorylation sites is labor intensive. It would be beneficial if computational methods are available to provide an extra reference for the phosphorylation sites. Here we developed a novel sequence-based method for serine, threonine, and tyrosine phosphorylation site prediction. Nearest Neighbor algorithm was employed as the prediction engine. The peptides around the phosphorylation sites with a fixed length of thirteen amino acid residues were extracted via a sliding window along the protein chains concerned. Each of such peptides was coded into a vector with 6,072 features, derived from Amino Acid Index (AAIndex) database, for the classification/detection. Incremental Feature Selection, a feature selection algorithm based on the Maximum Relevancy Minimum Redundancy (mRMR) method was used to select a compact feature set for a further improvement of the classification performance. Three predictors were established for identifying the three types of phosphorylation sites, achieving the overall accuracies of 66.64%, 66.11%% and 66.69%, respectively. These rates were obtained by rigorous jackknife cross-validation tests.     

A partially function-to-topic model for protein function prediction

Background

Proteins are a kind of macromolecules and the main component of a cell, and thus it is the most essential and versatile material of life. The research of protein functions is of great significance in decoding the secret of life. In recent years, researchers have introduced multi-label supervised topic model such as Labeled Latent Dirichlet Allocation (Labeled-LDA) into protein function prediction, which can obtain more accurate and explanatory prediction. However, the topic-label corresponding way of Labeled-LDA is associating each label (GO term) with a corresponding topic directly, which makes the latent topics to be completely degenerated, and ignores the differences between labels and latent topics.

Result

To achieve more accurate probabilistic modeling of function label, we propose a Partially Function-to-Topic Prediction (PFTP) model for introducing the local topics subset corresponding to each function label. Meanwhile, PFTP not only supports latent topics subset within a given function label but also a background topic corresponding to a ‘fake’ function label, which represents common semantic of protein function. Related definitions and the topic modeling process of PFTP are described in this paper. In a 5-fold cross validation experiment on yeast and human datasets, PFTP significantly outperforms five widely adopted methods for protein function prediction. Meanwhile, the impact of model parameters on prediction performance and the latent topics discovered by PFTP are also discussed in this paper.

Conclusion

All of the experimental results provide evidence that PFTP is effective and have potential value for predicting protein function. Based on its ability of discovering more-refined latent sub-structure of function label, we can anticipate that PFTP is a potential method to reveal a deeper biological explanation for protein functions.

     

An efficient strategy for extensive integration of diverse biological data for protein function prediction

MOTIVATION: With the increasing availability of diverse biological information, protein function prediction approaches have converged towards integration of heterogeneous data. Many adapted existing techniques, such as machine-learning and probabilistic methods, which have proven successful on specific data types. However, the impact of these approaches is hindered by a couple of factors. First, there is little comparison between existing approaches. This is in part due to a divergence in the focus adopted by different works, which makes comparison difficult or even fuzzy. Second, there seems to be over-emphasis on the use of computationally demanding machine-learning methods, which runs counter to the surge in biological data. Analogous to the success of BLAST for sequence homology search, we believe that the ability to tap escalating quantity, quality and diversity of biological data is crucial to the success of automated function prediction as a useful instrument for the advancement of proteomic research. We address these problems by: (1) providing useful comparison between some prominent methods; (2) proposing Integrated Weighted Averaging (IWA)--a scalable, efficient and flexible function prediction framework that integrates diverse information using simple weighting strategies and a local prediction method. The simplicity of the approach makes it possible to make predictions based on on-the-fly information fusion. RESULTS: In addition to its greater efficiency, IWA performs exceptionally well against existing approaches. In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration.     

A pentapeptide-based method for protein secondary structure prediction

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of the defined conformations, alpha-helices or beta-strands. The analysis of the model indicates that the essential variable is the number of pentapeptides of well-defined structure in the database. Our model is simple, does not rely on arbitrary parameters and allows the analysis in detail of the results of each chosen hypothesis.     

Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph

When searching for prospective novel peptides, it is difficult to determine the biological activity of a peptide based only on its sequence. The “trial and error” approach is generally laborious, expensive and time consuming due to the large number of different experimental setups required to cover a reasonable number of biological assays. To simulate a virtual model for Hymenoptera insects, 166 peptides were selected from the venoms and hemolymphs of wasps, bees and ants and applied to a mathematical model of multivariate analysis, with nine different chemometric components: GRAVY, aliphaticity index, number of disulfide bonds, total residues, net charge, pI value, Boman index, percentage of alpha helix, and flexibility prediction. Principal component analysis (PCA) with non-linear iterative projections by alternating least-squares (NIPALS) algorithm was performed, without including any information about the biological activity of the peptides. This analysis permitted the grouping of peptides in a way that strongly correlated to the biological function of the peptides. Six different groupings were observed, which seemed to correspond to the following groups: chemotactic peptides, mastoparans, tachykinins, kinins, antibiotic peptides, and a group of long peptides with one or two disulfide bonds and with biological activities that are not yet clearly defined. The partial overlap between the mastoparans group and the chemotactic peptides, tachykinins, kinins and antibiotic peptides in the PCA score plot may be used to explain the frequent reports in the literature about the multifunctionality of some of these peptides. The mathematical model used in the present investigation can be used to predict the biological activities of novel peptides in this system, and it may also be easily applied to other biological systems.     

A novel feature-based method for whole genome phylogenetic analysis without alignment: application to HEV genotyping and subtyping

Traditional phylogenetic analysis is based on multiple sequence alignment. With the development of worldwide genome sequencing project, more and more completely sequenced genomes become available. However, traditional sequence alignment tools are impossible to deal with large-scale genome sequence. So, the development of new algorithms to infer phylogenetic relationship without alignment from whole genome information represents a new direction of phylogenetic study in the post-genome era. In the present study, a novel algorithm based on BBC (base-base correlation) is proposed to analyze the phylogenetic relationships of HEV (Hepatitis E virus). When 48 HEV genome sequences are analyzed, the phylogenetic tree that is constructed based on BBC algorithm is well consistent with that of previous study. When compared with methods of sequence alignment, the merit of BBC algorithm appears to be more rapid in calculating evolutionary distances of whole genome sequence and not requires any human intervention, such as gene identification, parameter selection. BBC algorithm can serve as an alternative to rapidly construct phylogenetic trees and infer evolutionary relationships.