Method For Prediction Of Proteins Research Articles

Method for prediction of protein–protein interactions in yeast using genomics/proteomics information and feature selection

Protein–protein interaction (PPI) prediction is one of the main goals in the current Proteomics. This work presents a method for prediction of protein–protein interactions through a classification technique known as support vector machines. The dataset considered is a set of positive and negative examples taken from a high reliability source, from which we extracted a set of genomic features, proposing a similarity measure. From this dataset we extracted 26 proteomics/genomics features using well-known databases and datasets. Feature selection was performed to obtain the most relevant variables through a modified method derived from other feature selection methods for classification. Using the selected subset of features, we constructed a support vector classifier that obtains values of specificity and sensitivity higher than 90% in prediction of PPIs, and also providing a confidence score in interaction prediction of each pair of proteins.

A computational method for prediction of excretory proteins and application to identification of gastric cancer markers in urine.

A novel computational method for prediction of proteins excreted into urine is presented. The method is based on the identification of a list of distinguishing features between proteins found in the urine of healthy people and proteins deemed not to be urine excretory. These features are used to train a classifier to distinguish the two classes of proteins. When used in conjunction with information of which proteins are differentially expressed in diseased tissues of a specific type versus control tissues, this method can be used to predict potential urine markers for the disease. Here we report the detailed algorithm of this method and an application to identification of urine markers for gastric cancer. The performance of the trained classifier on 163 proteins was experimentally validated using antibody arrays, achieving >80% true positive rate. By applying the classifier on differentially expressed genes in gastric cancer vs normal gastric tissues, it was found that endothelial lipase (EL) was substantially suppressed in the urine samples of 21 gastric cancer patients versus 21 healthy individuals. Overall, we have demonstrated that our predictor for urine excretory proteins is highly effective and could potentially serve as a powerful tool in searches for disease biomarkers in urine in general.

Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet

Due to the complexity of Plasmodium falciparum (PF) genome, predicting mitochondrial proteins of PF is more difficult than other species. In this study, using the n-peptide composition of reduced amino acid alphabet (RAAA) obtained from structural alphabet named Protein Blocks as feature parameter, the increment of diversity (ID) is firstly developed to predict mitochondrial proteins. By choosing the 1-peptide compositions on the N-terminal regions with 20 residues as the only input vector, the prediction performance achieves 86.86% accuracy with 0.69 Mathew's correlation coefficient (MCC) by the jackknife test. Moreover, by combining with the hydropathy distribution along protein sequence and several reduced amino acid alphabets, we achieved maximum MCC 0.82 with accuracy 92% in the jackknife test by using the developed ID model. When evaluating on an independent dataset our method performs better than existing methods. The results indicate that the ID is a simple and efficient prediction method for mitochondrial proteins of malaria parasite.

The Trypanosoma brucei MitoCarta and its regulation and splicing pattern during development

It has long been known that trypanosomes regulate mitochondrial biogenesis during the life cycle of the parasite; however, the mitochondrial protein inventory (MitoCarta) and its regulation remain unknown. We present a novel computational method for genome-wide prediction of mitochondrial proteins using a support vector machine-based classifier with ∼90% prediction accuracy. Using this method, we predicted the mitochondrial localization of 468 proteins with high confidence and have experimentally verified the localization of a subset of these proteins. We then applied a recently developed parallel sequencing technology to determine the expression profiles and the splicing patterns of a total of 1065 predicted MitoCarta transcripts during the development of the parasite, and showed that 435 of the transcripts significantly changed their expressions while 630 remain unchanged in any of the three life stages analyzed. Furthermore, we identified 298 alternatively splicing events, a small subset of which could lead to dual localization of the corresponding proteins.

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.

A Novel Method for Functional Prediction of Proteins from a Protein-Protein Interaction Network

A Novel Method for Functional Prediction of Proteins from a Protein-Protein Interaction Network

An ensemble of support vector machines for predicting virulent proteins

It is important to develop a reliable system for predicting bacterial virulent proteins for finding novel drug/vaccine and for understanding virulence mechanisms in pathogens.In this work we have proposed a bacterial virulent protein prediction method based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence of a given protein. It is well known in the literature that the features extracted from the evolutionary information of a given protein are better than the features extracted from the amino acid sequence. Our method tries to fill the gap between the amino acid sequence based approaches and the evolutionary information based approaches.An extensive evaluation according to a blind testing protocol, where the parameters of the system are calculated using the training set and the system is validated in three different independent datasets, has demonstrated the validity of the proposed method.

Development of a High Performance Prediction Method for Single Spanning Membrane Proteins

Membrane proteins constitute 20-25% of open reading frames in a biological genome [1]. Previously we developed a membrane protein predictor SOSUI [2] and a signal peptide predictor SOSUIsignal [3] whose web site is visited by many researchers in the world. However, this system is not good at prediction of single spanning (TM1) membrane proteins. It is a common problem to all membrane protein prediction tools. TM1 membrane proteins occupy 30-35% of membrane proteins in a genome and have various important functions.

Membrane protein prediction methods

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.

Multipass membrane protein structure prediction using Rosetta

We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure of surface hydrophobic residues within the membrane and minimizing hydrophobic exposure outside of the membrane. Protein conformations are built up using the Rosetta fragment assembly method and evaluated using a new membrane-specific version of the Rosetta low-resolution energy function in which residue-residue and residue-environment interactions are functions of the membrane layer in addition to amino acid identity, distance, and density. We find that lower energy and more native-like structures are achieved by sequential addition of helices to a growing chain, which may mimic some aspects of helical protein biogenesis after translocation, rather than folding the whole chain simultaneously as in the Rosetta soluble protein prediction method. In tests on 12 membrane proteins for which the structure is known, between 51 and 145 residues were predicted with root-mean-square deviation <4 A from the native structure.

MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins

Currently available methods for the prediction of subcellular location of mitochondrial proteins rely largely on the presence of mitochondrial targeting signals in the protein sequences. However, a large fraction of mitochondrial proteins lack such signals, making those tools ineffective for genome-scale prediction of mitochondria-targeted proteins. Here, we propose a method for genome-scale prediction of nucleus-encoded mitochondrial proteins. The new method, MITOPRED, is based on the Pfam domain occurrence patterns and the amino acid compositional differences between mitochondrial and non-mitochondrial proteins. MITOPRED could predict mitochondrial proteins with 100% specificity at a 44% sensitivity rate and with 67% specificity at 99% sensitivity. Additionally, it was sufficiently robust to predict mitochondrial proteins across different eukaryotic species with similar accuracy. Based on Matthews correlation coefficient measure, the prediction performance of MITOPRED is clearly superior (0.73) to those of the two popular methods TargetP (0.51) and PSORT (0.53). Using this method, we predicted the nucleus-encoded mitochondrial proteins from six complete genomes (three invertebrate, two vertebrate and one plant species) and estimated the total number in each genome. In human, our method estimated the existence of 1362 mitochondrial proteins corresponding to 4.8% of the total proteome. MITOPRED program is freely accessible at http://mitopred.sdsc.edu. Source code is available on request from the authors. Training data sets are also available at http://mitopred.sdsc.edu

Structural genomics for Caenorhabditis elegans: high throughput protein expression analysis

The structural genomics initiatives have begun with the aim to create a so-called ‘basic set library’ of protein folds that will be used to improve protein prediction methods. Such a library is thought to require the determination of up to 10,000 new structures, including representative structures of several sequence variants from each protein fold. To meet this goal in a reasonable time frame and cost, automated systems must be utilized to clone and to identify the soluble recombinant proteins contained in multiple genomes. This paper presents such a system, developed using the genome of Caenorhabditis elegans (19,099 genes) as a model eukaryotic organism for structural genomics. This system successfully automates nearly all aspects of recombinant protein expression analysis including subcloning, bacterial growth, recombinant protein expression, protein purification, and scoring protein solubility.

PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins.

The beta-barrel outer membrane proteins constitute one of the two known structural classes of membrane proteins. Whereas there are several different web-based predictors for alpha-helical membrane proteins, currently there is no freely available prediction method for beta-barrel membrane proteins, at least with an acceptable level of accuracy. We present here a web server (PRED-TMBB, http://bioinformatics.biol.uoa.gr/PRED-TMBB) which is capable of predicting the transmembrane strands and the topology of beta-barrel outer membrane proteins of Gram-negative bacteria. The method is based on a Hidden Markov Model, trained according to the Conditional Maximum Likelihood criterion. The model was retrained and the training set now includes 16 non-homologous outer membrane proteins with structures known at atomic resolution. The user may submit one sequence at a time and has the option of choosing between three different decoding methods. The server reports the predicted topology of a given protein, a score indicating the probability of the protein being an outer membrane beta-barrel protein, posterior probabilities for the transmembrane strand prediction and a graphical representation of the assumed position of the transmembrane strands with respect to the lipid bilayer.