Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein

BACKGROUND: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition. RESULTS: We compute the correlation between expression of a gene and amino acid composition of its protein. It was observed that some residues (like Ala, Gly, Arg and Val) have significant positive correlation (r > 0.20) and some other residues (Like Asp, Leu, Asn and Ser) have negative correlation (r < -0.15) with the expression of genes. A significant negative correlation (r = -0.18) was also found between length and gene expression. These observations indicate the relationship between percent composition and gene expression level. Thus, attempts have been made to develop a Support Vector Machine (SVM) based method for predicting the expression level of genes from its protein sequence. In this method the SVM is trained with proteins whose gene expression data is known in a given condition. Then trained SVM is used to predict the gene expression of other proteins of the same organism in the same condition. A correlation coefficient r = 0.70 was obtained between predicted and experimentally determined expression of genes, which improves from r = 0.70 to 0.72 when dipeptide composition was used instead of residue composition. The method was evaluated using 5-fold cross validation test. We also demonstrate that amino acid composition information along with gene expression data can be used for improving the function classification of proteins. CONCLUSION: There is a correlation between gene expression and amino acid composition that can be used to predict the expression level of genes up to a certain extent. A web server based on the above strategy has been developed for calculating the correlation between amino acid composition and gene expression and prediction of expression level . This server will allow users to study the evolution from expression data

Functional classification of G-Protein coupled receptors, based on their specific ligand coupling patterns

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them re- main as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data

Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

<p>Abstract</p> <p>Background</p> <p>It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of <it>Saccharomyces Cerevisiae</it>'s proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.</p> <p>Results</p> <p>We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.</p> <p>Conclusions</p> <p>We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of <it>Saccharomyces Cerevisiae'</it>s proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.</p

Machine learning methods for omics data integration

High-throughput technologies produce genome-scale transcriptomic and metabolomic (omics) datasets that allow for the system-level studies of complex biological processes. The limitation lies in the small number of samples versus the larger number of features represented in these datasets. Machine learning methods can help integrate these large-scale omics datasets and identify key features from each dataset. A novel class dependent feature selection method integrates the F statistic, maximum relevance binary particle swarm optimization (MRBPSO), and class dependent multi-category classification (CDMC) system. A set of highly differentially expressed genes are pre-selected using the F statistic as a filter for each dataset. MRBPSO and CDMC function as a wrapper to select desirable feature subsets for each class and classify the samples using those chosen class-dependent feature subsets. The results indicate that the class-dependent approaches can effectively identify unique biomarkers for each cancer type and improve classification accuracy compared to class independent feature selection methods. The integration of transcriptomics and metabolomics data is based on a classification framework. Compared to principal component analysis and non-negative matrix factorization based integration approaches, our proposed method achieves 20-30% higher prediction accuracies on Arabidopsis tissue development data. Metabolite-predictive genes and gene-predictive metabolites are selected from transcriptomic and metabolomic data respectively. The constructed gene-metabolite correlation network can infer the functions of unknown genes and metabolites. Tissue-specific genes and metabolites are identified by the class-dependent feature selection method. Evidence from subcellular locations, gene ontology, and biochemical pathways support the involvement of these entities in different developmental stages and tissues in Arabidopsis

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

Systems biology of energetic and atomic costs in the yeast transcriptome, proteome, and metabolome

Proteins vary in their cost to the cell and natural selection may favour the use of proteins that are cheaper to produce. We develop a novel approach to estimate the amino acid biosynthetic cost based on genome-scale metabolic models, and directly investigate the effects of biosynthetic cost on transcriptomic, proteomic and metabolomic data in _Saccharomyces cerevisiae_. We find that our systems approach to formulating biosynthetic cost produces a novel measure that explains similar levels of variation in gene expression compared with previously reported cost measures. Regardless of the measure used, the cost of amino acid synthesis is weakly associated with transcript and protein levels, independent of codon usage bias. In contrast, energetic costs explain a large proportion of variation in levels of free amino acids. In the economy of the yeast cell, there appears to be no single currency to compute the cost of amino acid synthesis, and thus a systems approach is necessary to uncover the full effects of amino acid biosynthetic cost in complex biological systems that vary with cellular and environmental conditions

Prediction of nuclear proteins using SVM and HMM models

<p>Abstract</p> <p>Background</p> <p>The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.</p> <p>Results</p> <p>All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM) based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC) of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC) and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM) based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in <it>Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster</it>, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins <url>http://www.imtech.res.in/raghava/nppred/</url>.</p> <p>Conclusion</p> <p>This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.</p

RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information

The attainment of complete map-based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. Identifying the localization of encoded proteins is the key to understanding their functional characteristics and facilitating their purification. Our proposed method, RSLpred, is an effort in this direction for genome-scale subcellular prediction of encoded rice proteins. First, the support vector machine (SVM)-based modules have been developed using traditional amino acid-, dipeptide- (i+1) and four parts-amino acid composition and achieved an overall accuracy of 81.43, 80.88 and 81.10%, respectively. Secondly, a similarity search-based module has been developed using position-specific iterated-basic local alignment search tool and achieved 68.35% accuracy. Another module developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix achieved an accuracy of 87.10%. In this study, a large number of modules have been developed using various encoding schemes like higher-order dipeptide composition, N- and C-terminal, splitted amino acid composition and the hybrid information. In order to benchmark RSLpred, it was tested on an independent set of rice proteins where it outperformed widely used prediction methods such as TargetP, Wolf-PSORT, PA-SUB, Plant-Ploc and ESLpred. To assist the plant research community, an online web tool 'RSLpred' has been developed for subcellular prediction of query rice proteins, which is freely accessible at http://www.imtech.res.in/raghava/rslpred