Application of protein grey incidence degree measure to predict protein quaternary structural types

Computational Prediction of Ubiquitination Proteins Using Evolutionary Profiles and Functional Domain Annotation

Background:

Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis, and localization. Identification of ubiquitination proteins is of fundamental importance for understanding the molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well-studied model organisms.

Objective:

To reduce experimental costs, computational methods have been introduced to predict ubiquitination sites, but the accuracy is unsatisfactory. If it can be predicted whether a protein can be ubiquitinated or not, it will help in predicting ubiquitination sites. However, all the computational methods so far can only predict ubiquitination sites.

Methods:

In this study, the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction has been developed. The method extracts features from sequence conservation information through a grey system model, as well as functional domain annotation and subcellular localization.

Results:

Together with the feature analysis and application of the relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 90.13%, with Matthew’s correlation coefficient of 80.34%. The predicted results on an independent test data achieved 87.71% as accuracy and 75.43% of Matthew’s correlation coefficient, better than the prediction from the best ubiquitination site prediction tool available.

Conclusion:

Our study may guide experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The code is available at: https://github.com/Chunhuixu/UBIPredic_QWRCHX

osFP: a web server for predicting the oligomeric states of fluorescent proteins

Background

Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence.

Results

After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment.

Conclusion

osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract

Quad-PRE: a hybrid method to predict protein quaternary structure attributes

The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively.

Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform

In vivo, some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition.

An empirical study on the matrix-based protein representations and their combination with sequence-based approaches

Many domains have a stake in the development of reliable systems for automatic protein classification. Of particular interest in recent studies of automatic protein classification is the exploration of new methods for extracting features from a protein that enhance classification for specific problems. These methods have proven very useful in one or two domains, but they have failed to generalize well across several domains (i.e. classification problems). In this paper, we evaluate several feature extraction approaches for representing proteins with the aim of sequence-based protein classification. Several protein representations are evaluated, those starting from: the position specific scoring matrix (PSSM) of the proteins; the amino-acid sequence; a matrix representation of the protein, of dimension (length of the protein) ×20, obtained using the substitution matrices for representing each amino-acid as a vector. A valuable result is that a texture descriptor can be extracted from the PSSM protein representation which improves the performance of standard descriptors based on the PSSM representation. Experimentally, we develop our systems by comparing several protein descriptors on nine different datasets. Each descriptor is used to train a support vector machine (SVM) or an ensemble of SVM. Although different stand-alone descriptors work well on some datasets (but not on others), we have discovered that fusion among classifiers trained using different descriptors obtains a good performance across all the tested datasets. Matlab code/Datasets used in the proposed paper are available at http://www.bias.csr.unibo.it\nanni\PSSM.rar.

Self-similarity analysis of eubacteria genome based on weighted graph

We introduce a weighted graph model to investigate the self-similarity characteristics of eubacteria genomes. The regular treating in similarity comparison about genome is to discover the evolution distance among different genomes. Few people focus their attention on the overall statistical characteristics of each gene compared with other genes in the same genome. In our model, each genome is attributed to a weighted graph, whose topology describes the similarity relationship among genes in the same genome. Based on the related weighted graph theory, we extract some quantified statistical variables from the topology, and give the distribution of some variables derived from the largest social structure in the topology. The 23 eubacteria recently studied by Sorimachi and Okayasu are markedly classified into two different groups by their double logarithmic point-plots describing the similarity relationship among genes of the largest social structure in genome. The results show that the proposed model may provide us with some new sights to understand the structures and evolution patterns determined from the complete genomes.

Feature importance analysis in guide strand identification of microRNAs

MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence-structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine-cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism.

A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets

The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In the present paper we extend the notion of entropy and clarity to the use of different metrics and apply them in the case of the Fuzzy Polynuclotide Space (FPS). Applications of these notions on selected polynucleotides and complete genomes both in the I(12×k) space, but also using their representation in FPS are presented. Our results show that the values of fuzzy entropy/clarity are indicative of the degree of complexity necessary for the description of the polynucleotides in the FPS, although in the latter case the interpretation is slightly different than in the case of the I(12×k) hypercube. Fuzzy entropy/clarity along with the use of appropriate metrics can contribute to sequence analysis and taxonomy.

SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition

Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs. In this paper, fusing several new features into Chou's pseudo-amino acid composition (PseAAC), two support vector machine (SVM)-based ternary classifiers are developed to predict secreted proteins of Gram-negative and Gram-positive bacteria. For the two types of bacteria, the high accuracy of 94.03% and 94.36% are obtained in distinguishing classically secreted, non-classically secreted and non-secreted proteins by our method. In order to compare the practical ability of our method in identifying bacterial secreted proteins with those of six published methods, proteins in Escherichia coli and Bacillus subtilis are collected to construct the test sets of Gram-negative and Gram-positive bacteria, and the prediction results of our method are comparable to those of existing methods. When performed on two public independent data sets for predicting NCSPs, it also yields satisfactory results for Gram-negative bacterial proteins. The prediction server SecretP can be accessed at http://cic.scu.edu.cn/bioinformatics/secretPV2/index.htm.

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm

Background

Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs.

Results

In this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred.

Conclusion

The results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.

High performance set of PseAAC and sequence based descriptors for protein classification

The study of reliable automatic systems for protein classification is important for several domains, including finding novel drugs and vaccines. The last decade has seen a number of advances in the development of reliable systems for classifying proteins. Of particular interest has been the exploration of new methods for extracting features from a protein that enhance classification for a given problem. Most methods developed to date, however, have been evaluated in only one or two application areas. Methods have not been explored that generalize well across a number of application areas and datasets. The aim of this study is to find a general method, or an ensemble of methods, that works well on different protein classification datasets and problems. Towards this end, we evaluate several feature extraction approaches for representing proteins starting from their amino acid sequence as well as different feature descriptor combinations using an ensemble of classifiers (support vector machines). In our experiments, more than ten different protein descriptors are compared using nine different datasets. We develop our system using a blind testing protocol, where the parameters of the system are optimized using one dataset and then validated using the other datasets (and so on for each dataset). Although different stand-alone classifiers work well on some datasets and not on others, we have discovered that fusion among different methods obtains a good performance across all the tested datasets, especially when using the weighted sum rule. Included in our feature descriptor combinations is the introduction of two new descriptors, one based on wavelets and the other based on amino acid groups. Using our system, both outperform their standard implementations. We also consider as a baseline the simple amino acid composition (AC) and dipeptide composition (2G), since they have been widely used for protein classification. Our proposed method outperforms AC and 2G.

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.

Protein classification using texture descriptors extracted from the protein backbone image

In this work, we propose a method for protein classification that combines different texture descriptors extracted from the 2-D distance matrix obtained from the 3-D tertiary structure of a given protein. Instead of considering all atoms in the protein, the distance matrix is calculated by considering only those atoms that belong to the protein backbone. The positive results reported in this paper offer further experimental confirmation that the distance matrix contains sufficient information for describing a protein. Moreover, we show that combining features extracted from the primary structure with features extracted from the distance matrix increases the performance of our classification system. We demonstrate this finding by comparing the performance of an ensemble of classifiers that uses the combined features. The classifiers used in our experiments are support vector machines and random subspace of support vector machines. The experimental results, validated using three different datasets (protein fold recognition, DNA-binding proteins recognition, biological processes, and molecular functions recognition) along with different texture feature extraction methods (variants of local binary patterns, Radon feature transform based approaches, and Haralick descriptors) demonstrate the effectiveness of the proposed approach. Particularly interesting are the results in the classification of 27 types of structural properties: our proposed approach achieves significant improvement compared with other reported methods.