Position-specific Scoring Matrices Research Articles

Computational Approach to Identify the Major Histocompatibility Complex Binding Antigenic Peptides from 'Ascariasis'

The Ascaris lumbricoides cause ‘ascariasis’ in human and its elimination is the major public concern. In this current investigation, we predicted the MHC (Major Histocompatibility Complex) class I & II binding peptides with computational approach like PSSM (Position Specific Scoring Matrices) and SVM (Support Vector Machine) algorithms. We predict the peptide binders of Cytochrome b (mitochondrion) protein from Ascaris lumbricoide sequence to MHC-I molecules are as 11mer_H2_Db, 10mer_H2_Db, 9mer_H2_Db, 8mer_H2_Db. Also study integrates prediction of peptide MHC class I binding; proteasomal C terminal cleavage and TAP transport efficiency by using sequence and properties of the amino acids. We also found the binding of peptides to different alleles by using Position Specific Scoring Matrix. Cytochrome B from Ascaris lumbricoides (365 residues long) with 357 nonamers having antigenic MHC binding peptides. PSSM based server will predict the peptide binders from sequence to MHCII molecules are as I_Ab.p, I_Ad.p, I_Ag7, which are found antigenic epitopes region in Cytochrome B from Ascaris lumbricoides. This investigation can be useful in rational vaccine design and simultaneously increase the understanding the role of the immune system against antigenic.

Prediction of MHC Binding Peptides and Antigenic Peptides from Ascaris lumbricoides

The parasitic disease Acariasis is the major concern because of its morbidity and mortality issue. In this investigation, we predicted the binding peptides of the MHC class I and MHC class II by Position Specific Scoring Matrices (PSSM) and Support Vector Machine (SVM) algorithms. We predicted the binding affinity of Cytochrome c oxidase subunit 2 (mitochondrion) having a 232 amino acids long residue sequence, which shows 224 nonamers. We predicted the peptide binding affinity to MHC I moles are as 8mer_H2_Db(The binding thresholds: 33.04,optimal score:52.494), 9mer_H2_Db(Optimal Score: 50.232, Binding Threshold: 17.96),10mer_H2_Db (The Optimal Score: 58.858, Binding Threshold: 41.32), 11mer_H2_Db(Optimal Score: 79.495, Binding Threshold: 56.96) and MHC- II molecules are as I_Ab.p, I_Ad.p, I_Ag7. We also study integrates prediction of peptide MHC class I binding; proteasomal C terminal cleavage and TAP transport efficiency by using sequence and properties of the amino acids. We also found the binding of peptides to different alleles by using Position Specific Scoring Matrix. PSSM based server will predict the peptide binders of Cytochrome c oxidase subunit 2 from Ascaris lumbricoides sequence to, which are found antigenic epitopes region in protein.

Computational Sensitive Quantitative Predictions of MHC Binding Peptides from Dracunculus medinensis

“Dracunculus medinensis” the Guinea Worm Disease (GWD) causing agent belong to the member of ‘Dracunculidae’ family. The infectious female nematode which is up to 800 mm (31 in) in length is the causative agent of the Guinea worm disease in the humans. Cyclops are the intermediate host of this infectious parasite. The healthy individual drinks or consume the contaminated water with Cyclops, which carries the infectious larvae of the Guinea Worm Disease. For the selection of the nonamers, the antigenic peptide the fragments of specific protein can be used, which can be further utilizing for the rational vaccine design and to increase the understanding the immune system response against the disease. The encouraging outcomes of the MHCII (Major Histocompatibility Complex II) analysis shows that the antigenic peptide of the Guinea Worm Disease are important determinant for the protection of the host from parasitic infection. In this study, the Position Specific Scoring Matrices (PSSM) and Support Vector Machine (SVM)-algorithms has been use for antigenic design and prediction of the binding affinity of the antigen having the 88 amino acids long residue, which shows 80 nonamers. The binding ability of the antigen to the major histocompatibility complex (MHC) class I and II molecules prediction will be helpful in the near future for specific targeted drug designing for the Guinea Worm Disease.

LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines.

Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software. LedPred is available on GitHub: https://github.com/aitgon/LedPred and Bioconductor: http://bioconductor.org/packages/release/bioc/html/LedPred.html under the MIT license. aitor.gonzalez@univ-amu.fr Supplementary data are available at Bioinformatics online.

Prediction of neddylation sites from protein sequences and sequence-derived properties.

BackgroundNeddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features.ResultsWe have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysine-centred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites.ConclusionsIn this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.

A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites.

Protein-protein interactions exist ubiquitously and play important roles in the life cycles of living cells. The interaction sites (residues) are essential to understanding the underlying mechanisms of protein-protein interactions. Previous research has demonstrated that the accurate identification of protein-protein interaction sites (PPIs) is helpful for developing new therapeutic drugs because many drugs will interact directly with those residues. Because of its significant potential in biological research and drug development, the prediction of PPIs has become an important topic in computational biology. However, a severe data imbalance exists in the PPIs prediction problem, where the number of the majority class samples (non-interacting residues) is far larger than that of the minority class samples (interacting residues). Thus, we developed a novel cascade random forests algorithm (CRF) to address the serious data imbalance that exists in the PPIs prediction problem. The proposed CRF resolves the negative effect of data imbalance by connecting multiple random forests in a cascade-like manner, each of which is trained with a balanced training subset that includes all minority samples and a subset of majority samples using an effective ensemble protocol. Based on the proposed CRF, we implemented a new sequence-based PPIs predictor, called CRF-PPI, which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Benchmark experiments on both the cross validation and independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors. The source code for CRF-PPI and the benchmark datasets are available online at http://csbio.njust.edu.cn/bioinf/CRF-PPI for free academic use.

Abstract 1090: Evolutionary, structural, and functional insights into the seven-transmembrane GPCR superfamily through NCBI's Conserved Domain Database

Abstract NCBI's Conserved Domain Database (CDD, http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) is a protein classification and annotation resource. It comprises a collection of annotated multiple sequence alignment (MSA) models representing ancient conserved protein domains, basic units of protein function and protein evolution. These MSAs are available as position specific score matrices (PSSMs) for the rapid identification of conserved domains in protein sequences via Reverse Position Specific -BLAST (RPS-BLAST), a variation of the commonly used PSI-BLAST method. CDD includes manually curated domain models that are organized into family hierarchies, which use protein 3D structure information explicitly to refine domain models and provide insights into the relationship between sequence conservation and molecular function. The domain models can be visualized and studied interactively using NCBI's CDTree/Cn3D software applications. Conserved functional annotations such as ligand-binding sites and/or catalytic active sites are highlighted on protein structures sharing significant homology to the query, thereby pointing out or inferring the underlying mechanism(s) of the protein's function. Here, we present the functionally annotated, evolutionary hierarchical classification of the seven-transmembrane G-protein coupled receptors (7TM GPCRs), which represent the largest family of membrane-bound proteins across the three kingdoms of life. GPCRs are involved in a wide range of physiological roles including tumor growth and metastasis. With the increasing availability of high-resolution crystal structures of 7TM receptors, we recently undertook a systematic comparative analysis of the highly divergent GPCRs. Orphan GPCR subfamilies, which only contain uncharacterized protein sequences, have putative functions and the location of 7TM helices annotated by inference from the molecular and cellular functions of known related proteins with available 3D structure, and/or based on the available literature. We demonstrate the utility of CDTree/Cn3D in studying the molecular evolution of protein domain families, and in accelerating the pace of GPCR drug discovery by enabling researchers to obtain valuable clues regarding as-yet-unidentified molecular functions and mechanisms. The CDTree/Cn3D software is freely available for download via the CDD website. Acknowledgement: This research was supported by the Intramural Research Program of the National Library of Medicine, NIH. Citation Format: James S. Song, Noreen R. Gonzales, Roxanne A. Yamashita, Aron Marchler-Bauer, Stephen H. Bryant. Evolutionary, structural, and functional insights into the seven-transmembrane GPCR superfamily through NCBI's Conserved Domain Database. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1090. doi:10.1158/1538-7445.AM2015-1090

Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles

Abstract Knowledge of protein structure plays a key role in the analysis of protein functions, protein binding, rational drug design, and many other related fields and applications. In this study, a novel feature extraction model based on linear predictive coding (LPC) and position-specific score matrices (PSSM) was proposed to predict structural class from protein sequences. First, the PSI-BLAST tool was employed to transform the original protein sequences into PSSMs. Then, the LPC, a signal processing tool, was applied to extract the features from PSSMs. The selected features were finally fed to a support vector machine to perform the prediction. Cross-validation tests on the four benchmark datasets Z277, Z498, 1189 and 25PDB, showed a significant leap in overall accuracy using the proposed method. Compared to existing methods, our method achieved better performance in prediction of protein structural class.

Fast Model-based Protein Homology Discovery without Alignment

The need for quick gene categorization tools is growing as more genomes are sequenced. To evaluate a newly sequenced genome, the genes must first be identified and translated into amino acid sequences, which are then categorized into structural or functional classes. Protein homology detection using sequence alignment algorithms is the most effective way for protein categorization. Discriminative approaches such as support vector machines (SVMs) and position-specific scoring matrices (PSSM) derived from PSI-BLAST have recently been used to improve alignment algorithms. However, if a fresh sequence is being aligned, alignment algorithms take time. must be compared to a large number of previously published sequences — the same is true for SVMs. Building a PSSM for the PSSM is even more time-consuming than a fresh order It would take roughly 25 hours to implement the best-performing approaches to classify the sequences on today's computers. Describing a novel genome (20, 000 genes) as belonging to one single organism. There are hundreds of classes to choose from, though. Another flaw with alignment algorithms is that they do not construct a model of the positive class, instead of measuring the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are common classification approaches for creating a positive class model, but they have poor classification performance. A model's advantage is that it may be evaluated for chemical features that are shared by all members of the class to get fresh insights into protein function and structure. We used LSTM to solve a well-known remote protein homology detection benchmark, in which a protein must be categorized as a member of the SCOP superfamily. LSTM achieves state-of-the-art classification performance while being significantly faster than other algorithms with similar classification performance. LSTM is five orders of magnitude quicker than the quickest SVM-based approaches and two orders of magnitude faster than methods that perform somewhat better in classification (which, however, have lower classification performance than LSTM). We applied LSTM to PROSITE classes and analyzed the derived patterns to test the modeling capabilities of the algorithm. Because it does not require established similarity metrics like BLOSUM or PAM matrices, LSTM is complementary to alignment-based techniques. The PROSITE motif was retrieved by LSTM in 8 out of 15 classes. In the remaining seven examples, alternative motifs are developed that, on average, outperform the PROSITE motifs in categorization.

Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination.

Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to reveal the apoptosis mechanism and understand the function of apoptosis proteins. Because of the cost in time and labor associated with large-scale wet-bench experiments, computational prediction of apoptosis proteins subcellular location becomes very important and many computational tools have been developed in the recent decades. Existing methods differ in the protein sequence representation techniques and classification algorithms adopted. In this study, we firstly introduce a sequence encoding scheme based on tri-grams computed directly from position-specific score matrices, which incorporates evolution information represented in the PSI-BLAST profile and sequence-order information. Then SVM-RFE algorithm is applied for feature selection and reduced vectors are input to a support vector machine classifier to predict subcellular location of apoptosis proteins. Jackknife tests on three widely used datasets show that our method provides the state-of-the-art performance in comparison with other existing methods.

LocFuse: Human protein–protein interaction prediction via classifier fusion using protein localization information

Protein–protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse. Biological significanceThe results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems.

Prediction of membrane transport proteins and their substrate specificities using primary sequence information.

BackgroundMembrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.ResultsSupport vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.ConclusionsOur analyses suggest that evolutionary information (i.e., the PSSM) and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins. TrSSP: The Transporter Substrate Specificity Prediction Server, a web server that implements the SVM models developed in this paper, is freely available at http://bioinfo.noble.org/TrSSP.

Genotypic Tropism Prediction from Paired Cell and Plasma Using Single and Replicate Sequences

HIV-1 tropism determination is necessary prior to CCR5 antagonist use as antiretroviral therapy. Genotypic prediction of coreceptor use is a practical alternative to phenotypic tests. Cell DNA and plasma RNA-based prediction has shown discordance in many studies. We evaluate paired cell and plasma either as single or replicate V3 sequences to assess prediction comparability. The HIV-1 partial env region was sequenced and tropism was predicted using geno2pheno and position-specific scoring matrices (PSSM). Nucleotide ambiguities at V3 were quantified and genetic distance (Protdist) was determined using BioEdit. Wilcoxon signed-rank test, t tests, and Spearman correlation were performed with Prism GraphPad5.0. Results are expressed as medians, with a level of significance of p<0.05, two tailed. Single (n=28) or replicate (n=26) paired cell/plasma sequences were obtained from 54 patients. Although the clonalfalse-positive rate (FPR) value from both compartments strongly correlated (r=0.86 p<0.0001), discordance in tropism prediction was observed in both singles and replicates using geno2pheno or PSSM. Applying clonalFPR(10%) 46% (25/54) were X4 tropic, with a plasma/cell discordance of 11% in singles and 23% in replicates. Genetic distance (p<0.0001) and clonalFPR value dispersion (p=0.003) were significantly higher among replicate sequences from cells. Discordance of viral tropism prediction is not uncommon and the use of replicates does not decrease its occurrence, but improves X4 sensitivity. Sequences from provirus had greater genetic distance and dispersion of clonalFPR values. This may suggest that DNA replicate assays may better represent the diversity of HIV-1 variants, but the clinical significance of these findings needs further evaluation.

Adaptable probabilistic mapping of short reads using position specific scoring matrices

BackgroundModern DNA sequencing methods produce vast amounts of data that often requires mapping to a reference genome. Most existing programs use the number of mismatches between the read and the genome as a measure of quality. This approach is without a statistical foundation and can for some data types result in many wrongly mapped reads. Here we present a probabilistic mapping method based on position-specific scoring matrices, which can take into account not only the quality scores of the reads but also user-specified models of evolution and data-specific biases.ResultsWe show how evolution, data-specific biases, and sequencing errors are naturally dealt with probabilistically. Our method achieves better results than Bowtie and BWA on simulated and real ancient and PAR-CLIP reads, as well as on simulated reads from the AT rich organism P. falciparum, when modeling the biases of these data. For simulated Illumina reads, the method has consistently higher sensitivity for both single-end and paired-end data. We also show that our probabilistic approach can limit the problem of random matches from short reads of contamination and that it improves the mapping of real reads from one organism (D. melanogaster) to a related genome (D. simulans).ConclusionThe presented work is an implementation of a novel approach to short read mapping where quality scores, prior mismatch probabilities and mapping qualities are handled in a statistically sound manner. The resulting implementation provides not only a tool for biologists working with low quality and/or biased sequencing data but also a demonstration of the feasibility of using a probability based alignment method on real and simulated data sets.

Protein fold recognition using geometric kernel data fusion

Motivation: Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information.Results: We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels.Availability and implementation: The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/Contact: pooyapaydar@gmail.com or yves.moreau@esat.kuleuven.beSupplementary information: Supplementary data are available at Bioinformatics online.

A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition

In biological sciences, the deciphering of a three dimensional structure of a protein sequence is considered to be an important and challenging task. The identification of protein folds from primary protein sequences is an intermediate step in discovering the three dimensional structure of a protein. This can be done by utilizing feature extraction technique to accurately extract all the relevant information followed by employing a suitable classifier to label an unknown protein. In the past, several feature extraction techniques have been developed but with limited recognition accuracy only. In this study, we have developed a feature extraction technique based on tri-grams computed directly from Position Specific Scoring Matrices. The effectiveness of the feature extraction technique has been shown on two benchmark datasets. The proposed technique exhibits up to 4.4% improvement in protein fold recognition accuracy compared to the state-of-the-art feature extraction techniques.

Identification of MHC Class, Transports Antigenic Peptides from Naja melanoleuca Long Neurotoxin 1

Long neurotoxin 1 is a dangerous protein occurs in N. melanoleuca. It is highly dangerous snake due to the quantity of venom inject in a single bite. Peptide fragments of N. Melanoleuca Long neurotoxin 1 antigen protein having 71 amino acids, which shows 63 nanomers can be used to select nanomers for use in synthetic peptide vaccine design and to increase the understanding of roles of the immune system against snake bite. We use to apply computational intelligence algorithm PSSM (Position Specific Scoring Matrices) for the Prediction of MHC class-I binding peptides, we also predict antigenicity, Solvent accessibility, polar and nonpolar residue to analyses the membrane-spanning regions (hydrophobic) or regions that are likely exposed on the surface of proteins (hydrophilic domains) that are potentially antigenic that allows potential drug targets to identify active sites, for protection of host form Snake bites and to design synthetic peptide vaccine.

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Backgroundβ-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.ResultsWe propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.ConclusionsIn this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

PREDICTION OF PROTEIN SECONDARY STRUCTURE USING PROBABILITY BASED FEATURES AND A HYBRID SYSTEM

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

Prediction of protein modification sites of gamma-carboxylation using position specific scoring matrices based evolutionary information

Gamma-carboxylation, one type of post-translational modifications, is involved in many human disease. However, very few computational methods for gamma-carboxylation site prediction are available. In this paper, we develop a novel method CarboxySVM which is based on support vector machine with radial basis function kernel to identify the gamma-carboxylation sites. In this method, we combine position specific scoring matrices (PSSM)-based evolutionary conservation scores and other sequences-derived descriptors. As a result, an accuracy of 91.2% is achieved on training dataset with fivefold cross validation, and 91.8% on the independent test dataset. It is demonstrated by empirical evaluation on benchmark datasets that our method outperforms several other modern predictors. Our model reveals that evolutionary conservation is higher in carboxylation sites, compared to non-carboxylation sites. The composition of arginine in carboxylation sites is higher than that of non-carboxylation sites. CarboxySVM can be downloaded from http://code.google.com/p/gamma-carboxylation/source/browse/trunk.