Search

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

As an important post-transcriptional modification, adenosine-to-inosine RNA editing generally occurs in both coding and noncoding RNA transcripts in which adenosines are converted to inosines. Accordingly, the diversification of the transcriptome can be resulted in by this modification. It is significant to accurately identify adenosine-to-inosine editing sites for further understanding their biological functions. Currently, the adenosine-to-inosine editing sites would be determined by experimental methods, unfortunately, it may be costly and time consuming. Furthermore, there are only a few existing computational prediction models in this field. Therefore, the work in this study is starting to develop other computational methods to address these problems. Given an uncharacterized RNA sequence that contains many adenosine resides, can we identify which one of them can be converted to inosine, and which one cannot? To deal with this problem, a novel predictor called iAI-DSAE is proposed in the current study. In fact, there are two key issues to address: one is ‘what feature extraction methods should be adopted to formulate the given sample sequence?’ The other is ‘what classification algorithms should be used to construct the classification model?’ For the former, a 540-dimensional feature vector is extracted to formulate the sample sequence by dinucleotide-based auto-cross covariance, pseudo dinucleotide composition, and nucleotide density methods. For the latter, we use the present more popular method i.e. deep spare autoencoder to construct the classification model. Generally, ACC and MCC are considered as the two of the most important performance indicators of a predictor. In this study, in comparison with those of predictor PAI, they are up 2.46% and 4.14%, respectively. The two other indicators, Sn and Sp, rise at certain degree also. This indicates that our predictor can be as an important complementary tool to identify adenosine-toinosine RNA editing sites. For the convenience of most experimental scientists, an easy-to-use web-server for identifying adenosine-to-inosine editing sites has been established at: http://www.jci-bioinfo.cn/iAI-DSAE, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It is important to identify adenosine-to-inosine editing sites in RNA sequences for the intensive study on RNA function and the development of new medicine. In current study, a novel predictor, called iAI-DSAE, was proposed by using three feature extraction methods including dinucleotidebased auto-cross covariance, pseudo dinucleotide composition and nucleotide density. The jackknife test results of the iAI-DSAE predictor based on deep spare auto-encoder model show that our predictor is more stable and reliable. It has not escaped our notice that the methods proposed in the current paper can be used to solve many other problems in genome analysis.

Objective: Being a kind of post-transcriptional modification (PTCM) in RNA, the 2'-Omethylation modification occurs in the processes of life development and disease formation as well. Accordingly, from the angles of both basic research and drug development, we are facing a challenging problem: given an uncharacterized RNA sequence formed by many nucleotides of A (adenine), C (cytosine), G (guanine), and U (uracil), which one can be of 2-O'-methylation modification, and which one cannot? Unfortunately, so far no computational method whatsoever has been developed to address such a problem. Method: To fill this empty area, we propose a predictor called iRNA-2methyl. It is formed by incorporating a series of sequence-coupled factors into the general PseKNC (pseudo nucleotide composition), followed by fusing 12 basic random forest classifier into four ensemble predictors, with each aimed to identify the cases of A, C, G, and U along the RNA sequence concerned, respectively. Results: Rigorous jackknife cross-validations have indicated that the success rates are very high (>93%). For the convenience of most experimental scientists, a user-friendly web-server for iRNA-2methyl has been established at http://www.jci-bioinfo.cn/iRNA-2methyl, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. Conclusion: The proposed predictor iRNA-2methyl will become a very useful bioinformatics tool for medicinal chemistry, helping to design effective drugs against the diseases related to the 2'-Omethylation modification.

Background: DNase I hypersensitive sites (DHSs) are important signs of DNA regulatory regions. Their identification in DNA sequences is significant for both the biomedical research and the discovery of new drugs. The existing experimental methods to achieve this, however, are timeconsuming and laborious, so new computational means are called for. Method: To meet this end, a novel predictive model, called iDHSs-PseTNC, was constructed by integrating the sequence-order information and the physicochemical properties of trinucleotides into the pseudo trinucleotide composition (PseTNC). In the model, the deep sparse auto-encoder was used for reconstructing the input to get a good representative of the input characteristic, and a softmax classifier was added to the top of the auto-encoder coding layer. The deep sparse auto-encoder model obtained the best classification result with each member of the training set correctly classified. Five-fold crossvalidation test results indicated that the new predictor remarkably outperformed the existing prediction methods for the same purpose. Results: In this paper, the ACC rate of iDHSs-PseTNC is slightly (0.3%) lower than that of iDHS-EL constructed by Liu et al., its MCC rate is 3.45% higher than that of iDHS-EL. And the predictor iDHSs-PseTNC achieves the highest successful rates in both Pt and Py among the existing predictors. In order to facilitate the direct derivation of the needed results by experimental scholars, an easy-to-use web-server for identifying DHSs has been established for free access at: http://www.jcibioinfo. cn/iDHSs-PseTNC, which allows for fast and accurate computation. Conclusion: The timely identification of the DHSs in DNA sequence is significant for the intensive study on DNA function and the development of new drugs. In this article, we proposed a novel method for predicting the DHSs of DNA by incorporating physicochemical properties of trinucleotides into pseudo trinucleotide composition via deep sparse auto-encoder. The results were promising enough for our predictor to be used as an analytic solution to more genomic problems.

Background: For the formation of human gametes, meiotic recombination is crucial. Meanwhile, it has played an important role in the process that generates genetic diversity for that it is a defining event in the formation of human sperm and eggs. However, the recombination isn't a random occurrence across a genome, it usually occurs in some genomic regions, the so-called “hotspots”, with higher probability, while in the so-called “coldspots” with lower probability. Research has shown that new combinations of genetic variations can be provided by recombination. Therefore, the useful insights for in-depth studying of the genome evolution process and the mechanism of recombination would be provided based on the information of the coldspots and hotspots. Currently, the recombination regions would be determined by experiments, but it's a tedious job, which generally requires precious instruments and takes a long time. So in the study the work is starting to be studied by computational predicting models to address the above problems. Method: In this paper, a new predictor, called ‘iRSpotH-TNCPseAAC’ was developed to identify the human recombination coldspots and hotspots. In the new discrete predictive model, a feature vector called ‘pseudo trinucleotide composition’ or PseTNC is proposed to formulate the given DNA segment with its sequence-order information as complete as possible. Results: In this study, based on the rigorous jackknife test the overall success rate obtained by iRSpotH- TNCPseAAC is higher than 93% in identifying human’s recombination spots, and with mean success rate is 76.07% of the concerned 18 chromosomes. It means that our predictor can become a useful complementary tool in this area. Not only that, the PseTNC method can be used to further explore many other DNA-related problems. Finally, a web- server called iRSpotH-TNCPseAAC, which has the advantages of easy operation and convenient for using, is built and freely accessible at http://www.jci-bioinfo.cn/iRSpotH-TNCPseAAC. Conclusion: To timely acquire the information of recombination spots in DNA sequence is very significant to make in-depth study on epigenetic inheritance and analyze human diseases. Furthermore, it will facilitate drug development. A certain conclusion is that the iRSpotH-TNCPseAAC predictor may become a very practical online predictive high throughput tools in identifying recombination spots.

As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named “lysine ubiquitination” because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins. In view of this, a new predictor called “iUbiq-Lys” was developed based on the evolutionary information, gray system model, as well as the general form of pseudo-amino acid composition. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a web-server, iUbiq-Lys is accessible to the public at http://www.jci-bioinfo.cn/iUbiq-Lys. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process.

Based on the analysis of some conventional fuzzy time series model,this paper proposes a new method by using a single constrained optimization to determine the interval length for improving the forecasts.The conventional fuzzy time series models are enhanced by forecast-weighted method in the forecasting.In the proposed model,which is evaluated by mean square error(MSE),fuzzy membership degrees are used to calculate forecast weights. The empirical results show that the proposed model outperforms than the conventional models.

Abstract: G-Protein-Coupled Receptors (GPCRs) are the largest of cell surface receptor, accounting for >1% of the human genome. They play a key role in cellular signaling networks that regulate various physiological processes. The functions of many of GPCRs are unknown, because they are difficult to crystallize and most of them will not dissolve in normal solvents. This difficulty has motivated and challenged the development of a computational method which can predict the classification of the families and subfamilies of GPCRs based on their primary sequence so as to help us classify drugs. In this paper the adaptive K-nearest neighbor algorithm and protein cellular automata image (CAI) is introduced. Based on the CAI, the complexity measure factors derived from each of the protein sequences concerned are adopted for its Pseudo amino acid composition. GPCRs were categorized into nine subtypes. The overall success rate in identifying GPCRs among their nine family classes was about 83.5%. The high success rate suggests that the adaptive K-nearest neighbor algorithm and protein CAI holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.

Protein phosphorylation plays a critical role in human body by altering the structural conformation of a protein, causing it to become activated/deactivated, or functional modification. Given an uncharacterized protein sequence, can we predict whether it may be phosphorylated or may not? This is no doubt a very meaningful problem for both basic research and drug development. Unfortunately, to our best knowledge, so far no high throughput bioinformatics tool whatsoever has been developed to address such a very basic but important problem due to its extremely complexity and lacking sufficient training data. Here we proposed a predictor called iPhos‐PseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous jackknife tests have indicated that very promising success rates have been achieved by iPhos‐PseEvo even for such a difficult problem. A user‐friendly web‐server for iPhos‐PseEvo has been established at http://www.jci‐bioinfo.cn/iPhos‐PseEvo, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can be used to analyze many other problems in protein science as well.

Predicting phosphorylation protein is a challenging problem, particularly when query proteins have multi‐label features meaning that they may be phosphorylated at two or more different type amino acids. In fact, human protein usually be phosphorylated at serine, threonine and tyrosine. By introducing the “multi‐label learning” approach, a novel predictor has been developed that can be used to deal with the systems containing both single‐ and multi‐label phosphorylation protein. Here we proposed a predictor called Multi‐iPPseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous cross‐validations via a set of multi‐label metrics indicate that the multi‐label phosphorylation predictor is very promising and encouraging. The current approach represents a new strategy to deal with the multi‐label biological problems, and the software is freely available for academic use at http://www.jci‐bioinfo.cn/Multi‐iPPseEvo.