Oxypred: prediction and classification of oxygen-binding proteins

This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).     

Oxypred： Prediction and Classification of Oxygen-Binding Proteins

This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).     

Oxypred: Prediction and Classification of Oxygen-Binding Proteins

This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).     

Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.     

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.     

VICMpred： An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins （255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors）. First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins （http：//www.imtech.res.in/raghava/vicmpred/）.     

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/.     

Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.     

Accurate prediction of protein dihedral angles through conditional random field

Identifying local conformational changes induced by subtle differences on amino acid sequences is critical in exploring the functional variations of the proteins. In this study, we designed a computational scheme to predict the dihedral angle variations for different amino acid sequences by using conditional random field. This computational tool achieved an accuracy of 87% and 84% in 10-fold cross validation in a large data set for φ and Ψ, respectively. The prediction accuracies of φ and Ψ are positively correlated to each other for most of the 20 types of amino acids. Helical amino acids can achieve higher prediction accuracy in general, while amino acids in beet sheet have higher accuracy at specific angular regions. The prediction accuracy of φ is negatively correlated with amino acid flexibility represented by Vihinen Index. The prediction accuracy of φ can also be negatively correlated with angle distribution dispersion.     

SSPred: A prediction server based on SVM for the identification and classification of proteins involved in bacterial secretion systems

Protein secretion systems used by almost all bacteria are highly significant for the normal existence and interaction of bacteria with their host. The accumulation of genome sequence data in past few years has provided great insights into the distribution and function of these secretion systems. In this study, a support vector machine (SVM)- based method, SSPred was developed for the automated functional annotation of proteins involved in secretion systems further classifying them into five major sub-types (Type-I, Type-II, Type-III, Type-IV and Sec systems). The dataset used in this study for training and testing was obtained from KEGG and SwissProt database and was curated in order to avoid redundancy. To overcome the problem of imbalance in positive and negative dataset, an ensemble of SVM modules, each trained on a balanced subset of the training data were used. Firstly, protein sequence features like amino-acid composition (AAC), dipeptide composition (DPC) and physico-chemical composition (PCC) were used to develop the SVM-based modules that achieved an average accuracy of 84%, 85.17% and 82.59%, respectively. Secondly, a hybrid module (hybrid-I) integrating all the previously used features was developed that achieved an average accuracy of 86.12%. Another hybrid module (hybrid-II) developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix and amino-acid composition achieved a maximum average accuracy of 89.73%. On unbiased evaluation using an independent data set, SSPred showed good prediction performance in identification and classification of secretion systems. SSPred is a freely available World Wide Web server at http//www.bioinformatics.org/sspred.     

Structure based function prediction of proteins using fragment library frequency vectors

The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10.     

Prediction of neurotoxins based on their function and source

We have developed a method NTXpred for predicting neurotoxins and classifying them based on their function and origin. The dataset used in this study consists of 582 non-redundant, experimentally annotated neurotoxins obtained from Swiss-Prot. A number of modules have been developed for predicting neurotoxins using residue composition based on feed-forwarded neural network (FNN), recurrent neural network (RNN), support vector machine (SVM) and achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In addition, SVM modules have been developed for classifying neurotoxins based on their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and chordate) using amino acid composition and dipeptide composition and achieved maximum overall accuracy of 78.94% and 88.07% respectively. The overall accuracy increased to 92.10%, when the evolutionary information obtained from PSI-BLAST was combined with SVM module of source classification. We have also developed SVM modules for classifying neurotoxins based on functions using amino acid, dipeptide composition and achieved overall accuracy of 83.11%, 91.10% respectively. The overall accuracy of function classification improved to 95.11%, when PSI-BLAST output was combined with SVM module. All the modules developed in this study were evaluated using five-fold cross-validation technique. The NTXpred is available at www.imtech.res.in/raghava/ntxpred/ and mirror site at http://bioinformatics.uams.edu/mirror/ntxpred.     

Protein binding site prediction using an empirical scoring function

Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches.     

Prediction of protein solubility in Escherichia coli using logistic regression

In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli. The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previously. We tested several different implementations of logistic regression with varied results. The best implementation, which is the one we report, exhibits excellent overall prediction accuracies: 94% for the model and 87% by cross‐validation. For comparison, we also tested discriminant analysis using the same parameters, and we obtained a less accurate prediction (69% cross‐validation accuracy for the stepwise forward plus interactions model). Biotechnol. Bioeng. 2010; 105: 374–383. © 2009 Wiley Periodicals, Inc.     

Discriminative features of type I and type III secreted proteins from Gram-negative bacteria

The amino acid composition of sequences and structural attributes (α-helices, β-sheets) of C-and N-terminal fragments (50 amino acids) were compared to annotated (SWISS-PROT/ TrEMBL) type I (20 sequences) and type III (22 sequences) secreted proteins of Gram-negative bacteria. The discriminant analysis together with the stepwise forward and backward selection of variables revealed the frequencies of the residues Arg, Glu, Gly, Ile, Met, Pro, Ser, Tyr, Val as a set of strong (1-P < 0.001) predictor variables to discriminate between the sequences of type I and type III secreted proteins with a cross-validated accuracy of 98.6–100 %. The internal and external validity of discriminant analysis was confirmed by multiple (15 repeats) test-retest procedures using a randomly split original set of proteins; this validation method demonstrated an accuracy of 100 % for 191 non-selected (retest) sequences. The discriminant analysis was also applied using selected variables from the propensities for β-sheets and polarity of C-terminal fragments. This approach produced the next highest and comparable cross-validated classification accuracy for randomly selected and retest proteins (85.4–86.0 % and 82.4–84.5 %, respectively). The proposed sets of predictor variables could be used to assess the compatibility between secretion substrates and secretion pathways of Gram-negative bacteria by means of discriminant analysis.     

基于间隔二肽组分和递归特征消除法的DNA结合蛋白的鉴定

DNA结合蛋白(DNA-binding proteins,DBPs)的鉴定在原核和真核生物的基因和蛋白质功能注释研究中具有十分重要的意义.本研究首次运用间隔二肽组分(gapped-dipeptide composition,Gap DPC)结合递归特征消除法(recursive feature elimination,RFE)鉴定DBPs.首先获得待测蛋白质氨基酸序列的位置特异性得分矩阵(position specific scoring matrix,PSSM),在此基础上提取蛋白质的Gap DPC特征,通过RFE法选择最优特征,然后利用支持向量机(support vector machine,SVM)作为分类器,在蛋白质序列数据集PDB396和LB1068中进行夹克刀交叉验证(jackknife cross validation test).研究结果显示,基于PDB396和LB1068数据集,DBPs预测的准确率、Matthews相关系数、敏感性和特异性分别达到93.43%、0.86、89.04%和96.00%,以及86.33%、0.73、86.49%和86.18%,明显优于文献报道中的相关方法,为DBPs的鉴定提供了新的模型.     

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position–specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα‐Cα atoms. First, using a rigorous leave‐one‐protein‐out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state‐of‐the‐art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/ . Proteins 2016; 84:332–348. © 2016 Wiley Periodicals, Inc.