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

NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms

Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n-mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% and sensitivity about 10% with BF Tree. Performance analysis of NcPred clearly demonstrates its suitability over the contemporary in-silico nuclear protein classification research

Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs

<p>Abstract</p> <p>Background</p> <p>In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.</p> <p>Results</p> <p>The models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.</p> <p>Conclusion</p> <p>A highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.</p

A Balanced Secondary Structure Predictor

Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets

A Balanced Secondary Structure Predictor

Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets

Nuclear export signals (NESs) in Arabidopsis thaliana : development and experimental validation of a prediction tool

Rubiano Castellanos CC. Nuclear export signals (NESs) in Arabidopsis thaliana : development and experimental validation of a prediction tool. Bielefeld (Germany): Bielefeld University; 2010.It is well established that nucleo-cytoplasmic shuttling regulates not only the localization but also the activity of many proteins like transcription factors, cell cycle regulators and tumor suppressor proteins just to mention some. Also in plants the nucleo-cytoplasmic partitioning of proteins emerges as an important regulation mechanism for many plant-specific processes. One requirement for a protein to shuttle between nucleus and cytoplasm lies in its nuclear export activity. The widely used mechanism for export of proteins from the nucleus involves the receptor Exportin 1 and the presence of a nuclear export signal (NES) in the cargo protein. Given the big amount of sequence data available nowadays the possibility to use a computational tool to predict the proteins potentially containing an NES would help to facilitate the screening and experimental characterization of NES-containing proteins. However, the computational prediction of NESs is a challenging task. Currently there is only one NES prediction tool and that is unfortunately not accurate for predicting these signals in proteins of plants. In that direction, this study aimed mainly at developing a prediction method for identifying NESs in proteins from Arabidopsis and to validate its usefulness experimentally. It included also the definition of the influence of the NES protein context in the nuclear export activity of specific proteins of Arabidopsis. Three machine-learning algorithms (i.e. k-NN, SVM and Random Forests) were trained with experimentally validated NES sequences from proteins of Arabidopsis and other organisms. Two kinds of features were included, the sequence of the NESs expressed as the score obtained from an HMM profile constructed with the NES sequences of proteins from Arabidopsis, and physicochemical properties of the amino acid residues expressed as amino acid index values. The Random Forest classifier was selected among the three classifiers after evaluation of the performance by different methods. It showed to be highly accurate (accuracy values over 85 percent, classification error around 10 percent, MCC around 0.7 and area under the ROC curve around 0.90) and performed better than the other two trained classifiers. Using the Random Forest classifier around 5000 proteins from the total of protein sequences from Arabidopsis were predicted as containing NESs. A group of these proteins was selected by using Gene Ontologies (GO) and from this last group, 13 proteins were experimentally tested for nuclear export activity. 11 out of those 13 proteins showed positive interaction with the receptor Exportin 1 (XPO1a) from Arabidopsis in yeast two-hybrid assays. The proteins showing nuclear export activity include 9 transcription factors and 2 DNA metabolism-related proteins. Furthermore, it was established that the amino acid residues located between the hydrophobic residues in the NES as well as the protein structure of the regions around the NES could modify the nuclear export activity of some proteins. In conclusion, this work presents a new prediction tool for NESs in proteins of Arabidopsis based on a Random Forest classifier. The experimental validation of the nuclear export activity in a selected group of proteins is an indicative of the usefulness of the tool. From the biological point of view, the nuclear export activity observed in those proteins strongly suggest that nucleo-cytoplasmic partitioning could be involved in the regulation of their functions. For the follow up research the further characterization of the proteins showing positive nuclear export activity as well as the validation of additional predicted NES-containing proteins is envisioned. In the near future, the developed tool is going to be available as a web application to facilitate and promote its further usage

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing

BACKGROUND: The transporter associated with antigen processing (TAP) is a critical component of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. TAP transports antigenic peptides into the endoplasmic reticulum where it loads them into the binding groove of MHC class I molecules. Because peptides must first be transported by TAP in order to be presented on MHC class I, TAP binding preferences should impact significantly on T-cell epitope selection. DESCRIPTION: PRED(TAP )is a computational system that predicts peptide binding to human TAP. It uses artificial neural networks and hidden Markov models as predictive engines. Extensive testing was performed to valid the prediction models. The results showed that PRED(TAP )was both sensitive and specific and had good predictive ability (area under the receiver operating characteristic curve Aroc>0.85). CONCLUSION: PRED(TAP )can be integrated with prediction systems for MHC class I binding peptides for improved performance of in silico prediction of T-cell epitopes. PRED(TAP )is available for public use at [1]

Protein fold recognition using HMM–HMM alignment and dynamic programming

Detecting three dimensional structures of protein sequences is a challenging task in biological sciences. For this purpose, protein fold recognition has been utilized as an intermediate step which helps in classifying a novel protein sequence into one of its folds. The process of protein fold recognition encompasses feature extraction of protein sequences and feature identification through suitable classi- fiers. Several feature extractors are developed to retrieve useful information from protein sequences. These features are generally extracted by constituting protein’s sequential, physicochemical and evolutionary properties. The performance in terms of recognition accuracy has also been gradually improved over the last decade. However, it is yet to reach a well reasonable and accepted level. In this work, we first applied HMM–HMM alignment of protein sequence from HHblits to extract profile HMM (PHMM) matrix. Then we computed the distance between respective PHMM matrices using kernalized dynamic programming. We have recorded significant improvement in fold recognition over the state-of-the-art feature extractors. The improvement of recognition accuracy is in the range of 2.7–11.6% when experimented on three benchmark datasets from Structural Classification of Proteins