Breaking into the Field

We present a method called ngLOC, an n-gram-based Bayesian classifier that predicts the localization of a protein sequence over ten distinct subcellular organelles. A tenfold cross-validation result shows an accuracy of 89% for sequences localized to a single organelle, and 82% for those localized to multiple organelles. An enhanced version of ngLOC was developed to estimate the subcellular proteomes of eight eukaryotic organisms: yeast, nematode, fruitfly, mosquito, zebrafish, chicken, mouse, and human. Background Subcellular or organellar proteomics has gained tremendous attention of late, owing to the role played by organelles in carrying out defined cellular processes. Several efforts have been made to catalog the complete subcellular proteomes of various model organisms (for review [1,2]), with the aim being to improve our understanding of defined cellular processes at the organellar and cellular levels. Although such efforts have generated valuable information, cataloging all subcellular proteomes is far from complete. Experimental methods can be expensive, often generating conflicting or inconclusive results because of inherent limitations in the methods [3,4]. To complicate matters, computational methods rely on these experimental data, and therefore they must be resilient to noisy or inconsistent data found in these large datasets. These dilemmas have made the task of obtaining the complete set of proteins for each subcellular organelle a highly challenging one. In this study we address the task of estimating the subcellular proteome through development of a computational method that can be used to annotate the subcellular localization of proteins on a proteomic scale. A fundamental goal of computational methods in bioinformatics research is to annotate newly discovered protein sequences with their functional information more efficiently and accurately. Protein subcellular localization prediction has become a crucial part of establishing this important goal. In this task, predictive models are inferred from experimentally annotated datasets containing subcellular localization information, with the objective being to use these models to predict the subcellular localization of a protein sequence of unknown localization. The methods developed for predicting subcellular localization have varied significantly, ranging from the seminal work by Nakai and Kanehisa [5] on PSORT, which is a rule-based system derived by considering motifs and amino acid compositions; to the pure statistics based methods of Chou and Elrod [6], which employed covariant discriminant analysis; to Published: 1 May 2007 Genome Biology 2007, 8:R68 (doi:10.1186/gb-2007-8-5-r68) Received: 7 November 2006 Revised: 19 February 2007 Accepted: 1 May 2007 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2007/8/5/R68 Genome Biology 2007, 8:R68 R68.2 Genome Biology 2007, Volume 8, Issue 5, Article R68 King and Guda http://genomebiology.com/2007/8/5/R68 the numerous methods available today, which are based on a variety of machine learning and data mining algorithms, including artifical neural networks and support vector machines (SVMs) [7,8]. All methods must choose a set of features to represent a protein in the classification system. Although the majority of methods use various facets of information derived from the sequence, others use phylogenic information [9], structure information [10], and known functional domains [11]. Some methods scan documents and annotations related to the proteins in their dataset in search of discriminative keywords that can be used as predictive indicators [12,13]. Regardless of the representation, the sequence of a protein contains virtually all of the information needed to determine the structure of the protein, which in turn determines its function. Therefore, it is theoretically possible to derive much of the information needed to resolve most protein classification problems directly from the protein sequence. Furthermore, it has been proposed that a significant relationship exists between sequence similarity and subcellular localization [14], and the majority of protein classification methods have capitalized on this assumption. In addition to different classification algorithms and protein representation models, subcellular localization prediction methods also differ in exactly what they classify. Some consider only one or a few organelles in the cell [15,16]. Others consider all of the major organelles [5,6,8,11]. Methods often limit the species being considered, such as the PSORTb classifier for gram-negative bacteria [17]. Others limit the type of proteins being considered, such as those related to apoptosis [18]. We refer the interested reader to a review by Dönnes and Höglund [19], which provides an overview of the various methods used in this vast field. High-throughput proteomic studies continue to generate an ever-increasing quantity of protein data that must be analyzed. Hence, computational methods that can accurately and efficiently elucidate these proteins with respect to their functional annotation, including subcellular localization, at the level of the proteome are urgently needed [20]. Although a variety of computational methods are available for this task, very few of them have been applied on a proteome-wide scale. The PSLT method [21], a Bayesian method that uses a combination of InterPro motifs, signaling peptides, and human transmembrane domains, was used to estimate the subcellular proteome on portions of the proteome of human, mouse, and yeast. The method of Huang and Li [22], a fuzzy k-nearest neighbors algorithm that uses dipeptide compositions obtained from the protein sequence, was used to estimate the subcellular proteome for six species over six major organelles. Despite the availability of an array of methods, most of these are not suitable for proteome-wide prediction of subcellular localization for the following reasons. First, most methods predict only a limited number of locations. Second, the scoring criteria used by most methods are limited to subsets of proteomes, such as those containing signal/target peptide sequences or those with prior structural or functional information. Third, the majority of methods predict only one subcellular location for a given protein, even though a significant number of eukaryotic proteins are known to localize in multiple subcellular organelles. Fourth, many methods exhibit a lack of a balance between sensitivity and specificity. Fifth, the datasets used to train these programs are not sufficiently robust to represent the entire proteomes, and in some cases they are outdated or altered. Finally, many methods require the use of additional information beyond the primary sequence of the protein, which is often not available on a proteome-wide scale. In this report we present ngLOC, a Bayesian classification method for predicting protein subcellular localization. Our method uses n-gram peptides derived solely from the primary structure of a protein to explore the search space of proteins. It is suitable for proteome-wide predictions, and is also capable of inferring multi-localized proteins, namely those localized to more than one subcellular location. Using the ngLOC method, we have estimated the sizes of ten subcellular proteomes from eight eukaryotic species. Results We use a naïve Bayesian approach to model the density distributions of fixed-length peptide sequences (n-grams) over ten different subcellular locations. These distributions are determined from protein sequence data that contain experimentally determined annotations of subcellular localizations. To evaluate the performance of the method, we apply a standard validation technique called tenfold cross-validation, in which sequences from each class are divided into ten parts; the model is built using nine parts, and predictions are generated and evaluated on the data contained in the remaining part. This process is repeated for all ten possible combinations. We report standard performance measures over each subcellular location, including sensitivity (recall), precision, specificity, false positive rate, Matthews correlation coefficient (MCC), and receiver operating characteristic (ROC) curves. MCC provides a measure of performance for a single class being predicted; it equals 1 for perfect predictions on that class, 0 for random assignments, and less than 0 if predictions are worse than random [23]. For a measure of the overall classifier performance, we report overall accuracy as the fraction of the data tested that were classified correctly. (All of our formulae used to measure performance are briefly explained in the Materials and methods section [see below], with details provided in Additional data file 1.) To demonstrate the usefulness of our probabilistic confidence measures, we show how these measures can be used to consider situations in which a sequence may have multiple localizations, as well as to consider alternative localizations when confidence is low. Genome Biology 2007, 8:R68 http://genomebiology.com/2007/8/5/R68 Genome Biology 2007, Volume 8, Issue 5, Article R68 King and Guda R68.3