Prediction of protein submitochondria locations based on data fusion of various features of sequences

Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi

doi:10.1016/j.jtbi.2010.10.026

Cited by 48 publications

(26 citation statements)

References 89 publications

(116 reference statements)

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“…Arguably the best assessment of progress in this field is offered by the CASP project (http:// predictioncenter.org/). A summary of extensive research efforts in the area of protein folding may also be found in Chou and Zhang (1995), Chou and Shen(2008) and Zakeri et al (2011).…”

Section: Discussionmentioning

confidence: 99%

Two-intermediate model to characterize the structure of fast-folding proteins

Roterman

Konieczny

Jurkowski

et al. 2011

Journal of Theoretical Biology

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This paper introduces a new model that enables researchers to conduct protein folding simulations. A two-step in silico process is used in the course of structural analysis of a set of fast-folding proteins. The model assumes an early stage (ES) that depends solely on the backbone conformation, as described by its geometrical properties--specifically, by the V-angle between two sequential peptide bond planes (which determines the radius of curvature, also called R-radius, according to a second-degree polynomial form). The agreement between the structure under consideration and the assumed model is measured in terms of the magnitude of dispersion of both parameters with respect to idealized values. The second step, called late-stage folding (LS), is based on the "fuzzy oil drop" model, which involves an external hydrophobic force field described by a three-dimensional Gauss function. The degree of conformance between the structure under consideration and its idealized model is expressed quantitatively by means of the Kullback-Leibler entropy, which is a measure of disparity between the observed and expected hydrophobicity distributions. A set of proteins, representative of the fast-folding group - specifically, cold shock proteins - is shown to agree with the proposed model.

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Section: Discussionmentioning

confidence: 99%

Two-intermediate model to characterize the structure of fast-folding proteins

Roterman

Konieczny

Jurkowski

et al. 2011

Journal of Theoretical Biology

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“…A protein sequence can be summarized by its known functional domains. This representative model for a protein sequence was first introduced in (Cai et al, 2002, 2003) and is also considered for protein fold classification (Shen and Chou, 2009), protein structural recognition (Chou and Cai, 2004), protein subcellular location prediction (Cai et al, 2002) and prediction of protein submitochondria locations (Zakeri et al, 2011). In fact, fold information is a useful clue in determining a protein’s tertiary structure, which can facilitate the identification of its function.…”

Section: Methodsmentioning

confidence: 99%

Protein fold recognition using geometric kernel data fusion

et al. 2014

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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.

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“…However, as elucidated by Chou and Shen (2008) and demonstrated in Chou and Shen (2007), among the three cross-validation methods, the jackknife test is deemed the most objective one (Feng 2002) and can always yield a unique result for a given benchmark data set; hence, it has been increasingly used by investigators to examine the accuracy of various predictors (Zhou 1998;Zhou and Assa-Munt 2001;Zhou and Doctor 2003;Zhou et al 2007;Jiang et al 2008a, b;Li and Li 2008b;Lin 2008;Lin et al 2008;Zhang and Fang 2008;Bi et al 2011;Ding et al 2011;Hayat and Khan 2011;Hu et al 2011;Joshi and Sekharan 2010;Kandaswamy et al 2010;Kandaswamy et al 2011;Lin and Ding 2011;Liu et al 2010;Zakeri et al 2011). During the jackknife test process, each protein is singled out in turn as a test sample; the remaining proteins are used as a training set to calculate the test sample's membership and predict the class.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Fan

2011

Amino Acids

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Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.

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Prediction of protein submitochondria locations based on data fusion of various features of sequences

Cited by 48 publications

References 89 publications

Two-intermediate model to characterize the structure of fast-folding proteins

Two-intermediate model to characterize the structure of fast-folding proteins

Protein fold recognition using geometric kernel data fusion

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

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