Minghao Yin

Prediction of Lysine Ubiquitylation with Ensemble Classifier and Feature Selection

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.

Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.

As one of the most widespread protein post-translational modifications, phosphorylation is involved in many biological processes such as cell cycle, apoptosis. Identification of phosphorylated substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of phosphorylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of phosphorylation sites is much desirable due to their convenience and fast speed. In this paper, a new bioinformatics tool named CKSAAP_PhSite was developed that ignored the kinase information and only used the primary sequence information to predict protein phosphorylation sites. The highlight of CKSAAP_PhSite was to utilize the composition of k-spaced amino acid pairs as the encoding scheme, and then the support vector machine was used as the predictor. The performance of CKSAAP_PhSite was measured with a sensitivity of 84.81%, a specificity of 86.07% and an accuracy of 85.43% for serine, a sensitivity of 78.59%, a specificity of 82.26% and an accuracy of 80.31% for threonine as well as a sensitivity of 74.44%, a specificity of 78.03% and an accuracy of 76.21% for tyrosine. Experimental results obtained from cross validation and independent benchmark suggested that our method was very promising to predict phosphorylation sites and can be served as a useful supplement tool to the community. For public access, CKSAAP_PhSite is available at http://59.73.198.144/cksaap_phsite/.

Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences

Antifreeze proteins (AFPs) are ice-binding proteins. Accurate identification of new AFPs is important in understanding ice-protein interactions and creating novel ice-binding domains in other proteins. In this paper, an accurate method, called AFP_PSSM, has been developed for predicting antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. This is the first study in which evolutionary information in the form of PSSM profiles has been successfully used for predicting antifreeze proteins. Tested by 10-fold cross validation and independent test, the accuracy of the proposed method reaches 82.67% for the training dataset and 93.01% for the testing dataset, respectively. These results indicate that our predictor is a useful tool for predicting antifreeze proteins. A web server (AFP_PSSM) that implements the proposed predictor is freely available.

Prediction of methylation sites using the composition of K-spaced amino acid pairs.

Protein methylation is one of the most important post-translational modifications. Typically methylation occurs on arginine or lysine residues in the protein sequence. In the biological system, methylation is catalyzed by enzymes, and should be involved in modification of heavy metals, regulation of gene expression, regulation of protein function, and RNA metabolism. Thus the prediction of methylation sites plays a crucial role. As we know, traditional experiment approaches to predict the sites are accurate, but that are always labor-intensive and time-consuming. Thus, it is common to see that computational methods receive increasingly attentions due to their convenience and fast speed in recent years. In this study, we develop a computational approach to predict the performance of methylarginine and methyllysine sites. First, a new encoding schema as called the CKASSP is used in our method. Then, the support vector machine (SVM) algorithm is used as a predictor. Experimental results show that our method can obtain average prediction accuracy of 87.46%, sensitivity of 99.09%, specificity of 86.89% for arginine methylation sites, and average prediction accuracy of 88.78%, sensitivity of 93.75%, specificity of 81.79% for lysine methylation sites as well, which is better than those of other state-of-art predictors. The online service is implemented by java 1.4.2 and is freely available at http://202.198.129.219:8080/cksaap_methsite.

Identify DNA-binding proteins with optimal Chou's amino acid composition.

DNA-binding proteins play an important role in most cellular processes, such as gene regulation, recombination, repair, replication, and DNA modification. In this article, an optimal Chous pseudo amino acid composition (PseAAC) based on physicochemical characters of amino acid is proposed to represent proteins for identifying DNAbinding proteins. Six physicochemical characters of amino acids are utilized to generate the sequence features via the web server PseAAC. The optimal values of two important parameters (correlation factor δ and weighting factor w) about PseAAC are determined to get the appropriate representation of proteins, which ultimately result in better prediction performance. Experimental results on the benchmark datasets using random forest show that our method is really promising to predict DNA-binding proteins and may at least be a useful supplement tool to existing methods.

IMPROVED ARTIFICIAL BEE COLONY FOR DESIGN OF A RECONFIGURABLE ANTENNA ARRAY WITH DISCRETE PHASE SHIFTERS

Multi-beam antenna arrays have important applications in the fleld of communications and radar. The reconflgurable design problem is to flnd the element in a sector pattern main beam with side lobes. The same excitation amplitudes applied to the array with zero-phase should be in a high directivity, low side lobe pencil shaped main beam. This paper presents a new method of designing a reconflgurable antenna with quantized phase excitations using an improved artiflcial bee colony, called IABC. Compared with subsequent quantization, experimental results indicate that the performance of the discrete realization of the phase-excitation value can be improved.

Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles.

Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins’ functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.

Prediction of “Aggregation-Prone” Peptides with Hybrid Classification Approach

Protein aggregation is a biological phenomenon caused by misfolding proteins aggregation and is associated with a wide variety of diseases, such as Alzheimer’s, Parkinson’s, and prion diseases. Many studies indicate that protein aggregation is mediated by short “aggregation-prone” peptide segments. Thus, the prediction of aggregation-prone sites plays a crucial role in the research of drug targets. Compared with the labor-intensive and time-consuming experiment approaches, the computational prediction of aggregation-prone sites is much desirable due to their convenience and high efficiency. In this study, we introduce two computational approaches Aggre_Easy and Aggre_Balance for predicting aggregation residues from the sequence information; here, the protein samples are represented by the composition of k-spaced amino acid pairs (CKSAAP). And we use the hybrid classification approach to predict aggregation-prone residues, which integrates the naive Bayes classification to reduce the number of features, and two undersampling approaches EasyEnsemble and BalanceCascade to deal with samples imbalance problem. The Aggre_Easy achieves a promising performance with a sensitivity of 79.47%, a specificity of 80.70% and a MCC of 0.42; the sensitivity, specificity, and MCC of Aggre_Balance reach 70.32%, 80.70% and 0.42. Experimental results show that the performance of Aggre_Easy and Aggre_Balance predictor is better than several other state-of-the-art predictors. A user-friendly web server is built for prediction of aggregation-prone which is freely accessible to public at the website.