An overview on predicting the subcellular location of a protein

The present paper overviews the issue on predicting the subcellular location of a protein. Five measures of extracting information from the global sequence based on the Bayes discriminant algorithm are reviewed. 1) The auto-correlation functions of amino acid indices along the sequence; 2) The quasi-sequence-order approach; 3) the pseudo-amino acid composition; 4) the unified attribute vector in Hilbert space, 5) Zp parameters extracted from the Zp curve. The actual performance of the predictive accuracy is closely related to the degree of similarity between the training and testing sets or to the average degree of pairwise similarity in dataset in a cross-validated study. Many scholars considered that the current higher predictive accuracy still cannot ensure that some available algorithms are effective in practice prediction for the higher pairwise sequence identity of the datasets, but some of them declared that construction of the dataset used for developing software should base on the reality determined by the Mother Nature that some subcellular locations really contain only a minor number of proteins of which some even have a high percentage of sequence similarity. Owing to the complexity of the problem itself, some very sophisticated and special programs are needed for both constructing dataset and improving the prediction. Anyhow finding the target information in mature protein sequence and properly cooperating it with sorting signals in prediction may further improve the overall predictive accuracy and make the prediction into practice.     

Prediction of protein structural class by amino acid and polypeptide composition.

A new approach of predicting structural classes of protein domain sequences is presented in this paper. Besides the amino acid composition, the composition of several dipeptides, tripeptides, tetrapeptides, pentapeptides and hexapeptides are taken into account based on the stepwise discriminant analysis. The result of jackknife test shows that this new approach can lead to higher predictive sensitivity and specificity for reduced sequence similarity datasets. Considering the dataset PDB40-B constructed by Brenner and colleagues, 75.2% protein domain sequences are correctly assigned in the jackknife test for the four structural classes: all-alpha, all-beta, alpha/beta and alpha + beta, which is improved by 19.4% in jackknife test and 25.5% in resubstitution test, in contrast with the component-coupled algorithm using amino acid composition alone (AAC approach) for the same dataset. In the cross-validation test with dataset PDB40-J constructed by Park and colleagues, more than 80% predictive accuracy is obtained. Furthermore, for the dataset constructed by Chou and Maggiona, the accuracy of 100% and 99.7% can be easily achieved, respectively, in the resubstitution test and in the jackknife test merely taking the composition of dipeptides into account. Therefore, this new method provides an effective tool to extract valuable information from protein sequences, which can be used for the systematic analysis of small or medium size protein sequences. The computer programs used in this paper are available on request.     

Prediction of protein subcellular location using a combined feature of sequence

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.     

Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition

A new representation of protein sequence is devoted in this paper, in which each protein can be represented by a 20-dimensional (20D) vector of unit length. Inspired by the principle of superposition of state in quantum mechanics, the squares of the 20 components of the vector correspond to the amino acid composition. Using the new representation of the primary sequence and Bayes Discriminant Algorithm, the subcellular location of prokaryotic proteins was predicted. The overall predictive accuracy in the jackknife test can be 3% higher than the result of using amino acid composition directly for the database of sequence identity is less than 90%, but 5% higher when sequence identity is less than 80%. The higher predictive accuracy indicates that the current measure of extracting the information from the primary sequence is efficient. Since the subcellular location restricting a protein's possible function, the present method should also be a useful measure for the systematic analysis of genome data. The program used in this paper is available on request.     

Predicting disordered regions in proteins based on decision trees of reduced amino acid composition.

Intrinsically unstructured proteins (IUPs) are proteins lacking a fixed three dimensional structure or containing long disordered regions. IUPs play an important role in biology and disease. Identifying disordered regions in protein sequences can provide useful information on protein structure and function, and can assist high-throughput protein structure determination. In this paper we present a system for predicting disordered regions in proteins based on decision trees and reduced amino acid composition. Concise rules based on biochemical properties of amino acid side chains are generated for prediction. Coarser information extracted from the composition of amino acids can not only improve the prediction accuracy but also increase the learning efficiency. In cross-validation tests, with four groups of reduced amino acid composition, our system can achieve a recall of 80% at a 13% false positive rate for predicting disordered regions, and the overall accuracy can reach 83.4%. This prediction accuracy is comparable to most, and better than some, existing predictors. Advantages of our approach are high prediction accuracy for long disordered regions and efficiency for large-scale sequence analysis. Our software is freely available for academic use upon request.     

Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids

An algorithm of predicting the subcellular location of prokaryotic proteins is proposed in this paper. In addition to the amino acid composition, the auto-correlation functions based on the hydrophobicity profile of amino acids along the primary sequence of the query protein have been used. Consequently, the best predictive accuracy to date has been achieved. Of the 997 prokaryotic proteins in the database used here, 688 cytoplasmic, 107 extracellular and 202 periplasmic proteins, the overall predictive accuracies are as high as 97.7 and 90.4% in the resubstitution and jackknife tests, respectively, using the hydrophilicity value of Hopp and Woods. The underlying mechanism of the improvement is also discussed. This work would be useful for a systematic analysis of the great amounts of prokaryotic genome sequences. The computer programs used in this paper are available on request via email.     

Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach

Information of protein subcellular location plays an important role in molecular cell biology. Prediction of the subcellular location of proteins will help to understand their functions and interactions. In this paper, a different mode of pseudo amino acid composition was proposed to represent protein samples for predicting their subcellular localization via the following procedures: based on the optimal splice site of each protein sequence, we divided a sequence into sorting signal part and mature protein part, and extracted sequence features from each part separately. Then, the combined features were fed into the SVM classifier to perform the prediction. By the jackknife test on a benchmark dataset in which none of proteins included has more than 90% pairwise sequence identity to any other, the overall accuracies achieved by the method are 94.5% and 90.3% for prokaryotic and eukaryotic proteins, respectively. The results indicate that the prediction quality by our method is quite satisfactory. It is anticipated that the current method may serve as an alternative approach to the existing prediction methods.     

Prediction of ketoacyl synthase family using reduced amino acid alphabets

Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes’ catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.     

Combing ontologies and dipeptide composition for predicting DNA-binding proteins

Given a novel protein it is very important to know if it is a DNA-binding protein, because DNA-binding proteins participate in the fundamental role to regulate gene expression. In this work, we propose a parallel fusion between a classifier trained using the features extracted from the gene ontology database and a classifier trained using the dipeptide composition of the protein. As classifiers the support vector machine (SVM) and the 1-nearest neighbour are used. Matthews's correlation coefficient obtained by our fusion method is approximately 0.97 when the jackknife cross-validation is used; this result outperforms the best performance obtained in the literature (0.924) using the same dataset where the SVM is trained using only the Chou's pseudo amino acid based features. In this work also the area under the ROC-curve (AUC) is reported and our results show that the fusion permits to obtain a very interesting 0.995 AUC. In particular we want to stress that our fusion obtains a 5% false negative with a 0% of false positive. Matthews's correlation coefficient obtained using the single best GO-number is only 0.7211 and hence it is not possible to use the gene ontology database as a simple lookup table. Finally, we test the complementarity of the two tested feature extraction methods using the Q-statistic. We obtain the very interesting result of 0.58, which means that the features extracted from the gene ontology database and the features extracted from the amino acid sequence are partially independent and that their parallel fusion should be studied more.     

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.     

AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices

Identifying a protein's subcellular localization is an important step to understand its function. However, the involved experimental work is usually laborious, time consuming and costly. Computational prediction hence becomes valuable to reduce the inefficiency. Here we provide a method to predict protein subcellular localization by using amino acid composition and physicochemical properties. The method concatenates the information extracted from a protein's N-terminal, middle and full sequence. Each part is represented by amino acid composition, weighted amino acid composition, five-level grouping composition and five-level dipeptide composition. We divided our dataset into training and testing set. The training set is used to determine the best performing amino acid index by using five-fold cross validation, whereas the testing set acts as the independent dataset to evaluate the performance of our model. With the novel representation method, we achieve an accuracy of approximately 75% on independent dataset. We conclude that this new representation indeed performs well and is able to extract the protein sequence information. We have developed a web server for predicting protein subcellular localization. The web server is available at http://aaindexloc.bii.a-star.edu.sg .     

An alphabetic code based atomic level molecular similarity search in databases

Atomic level molecular similarity and diversity studies have gained considerable importance through their wide application in Bioinformatics and Chemo-informatics for drug design. The availability of large volumes of data on chemical compounds requires new methodologies for efficient and effective searching of its archives in less time with optimal computational power. We describe an alphabetic algorithm for similarity searching based on atom-atom bonding preference for ligands. We represented 170 cyclindependent kinase 2 inhibitors using strings of pre-defined alphabets for searching using known protein sequence alignment tools. Thus, a common pattern was extracted using this set of compounds for database searching to retrieve similar active compounds. Area under the receiver operating characteristic (ROC) curve was used for the discrimination of similar and dissimilar compounds in the databases. An average retrieval rate of about 60% is obtained in cross-validation using the home-grown dataset and the directory of useful decoys (DUD, formally known as the ZINC database) data. This will help in the effective retrieval of similar compounds using database search.     

An SVM method using evolutionary information for the identification of allergenic proteins

This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold cross-validation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems.     

Protein location prediction using atomic composition and global features of the amino acid sequence

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectively used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.     

Predicting membrane protein types using residue-pair models based on reduced similarity dataset

An algorithm to predict the membrane protein types based on the multi-residue-pair effect in the Markov model is proposed. For a newly constructed dataset of 835 membrane proteins with very low sequence similarity, the overall prediction accuracy has been achieved as high as 81.1% and 71.7% in the resubstitution and jackknife test, respectively, for a prediction of type I single-pass, type II single-pass, multi-pass membrane proteins, lipid chain-anchored and GPI-anchored membrane proteins. The improvement of about 11% in the jackknife test can be achieved compared with the component-coupled algorithm merely based on the amino acid composition (AAC approach). The improvement is also confirmed on a high similarity dataset and the other extrapolating test. The result implies that designing more incisive analysis tools, one should develop algorithms based on the representative dataset with lower sequence similarity. The present algorithm is useful to expedite the determination of the types and functions of new membrane proteins and may be useful for the systematic analysis of functional genome data in a large scale. The computer program is available on request.     

Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition

Predicting the cofactors of oxidoreductases plays an important role in inferring their catalytic mechanism. Feature extraction is a critical part in the prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present an amino acid composition distribution method for extracting useful features from primary sequence, and the k-nearest neighbor was used as the classifier. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.74%. Comparing our method with other eight feature extraction methods, the improvement of the overall prediction accuracy ranged from 3.49% to 15.74%. Our experimental results confirm that the method we proposed is very useful and may be used for other bioinformatical predictions. Interestingly, when features extracted by our method and Chou's amphiphilic pseudo-amino acid composition were combined, the overall accuracy could reach 92.53%.     

Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs

MOTIVATION: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs). RESULTS: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.     

A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search

Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.     

GNBSL: a new integrative system to predict the subcellular location for Gram-negative bacteria proteins

This paper proposes a new integrative system (GNBSL--Gram-negative bacteria subcellular localization) for subcellular localization specifized on the Gram-negative bacteria proteins. First, the system generates a position-specific frequency matrix (PSFM) and a position-specific scoring matrix (PSSM) for each protein sequence by searching the Swiss-Prot database. Then different features are extracted by four modules from the PSFM and the PSSM. The features include whole-sequence amino acid composition, N- and C-terminus amino acid composition, dipeptide composition, and segment composition. Four probabilistic neural network (PNN) classifiers are used to classify these modules. To further improve the performance, two modules trained by support vector machine (SVM) are added in this system. One module extracts the residue-couple distribution from the amino acid sequence and the other module applies a pairwise profile alignment kernel to measure the local similarity between every two sequences. Finally, an additional SVM is used to fuse the outputs from the six modules. Test on a benchmark dataset shows that the overall success rate of GNBSL is higher than those of PSORT-B, CELLO, and PSLpred. A web server GNBSL can be visited from http://166.111.24.5/webtools/GNBSL/index.htm.