TESE: generating specific protein structure test set ensembles

Sirocco, Francesco G.; Tosatto, Silvio C. E.

doi:10.1093/bioinformatics/btn488

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…Thus, the aggregation potential of (i,j) can be related to its energy. The energy parameters were re-calibrated for PASTA 2.0 on a larger dataset derived from TESE (20) (see Supplementary Material for details).…”

Section: Methodsmentioning

confidence: 99%

PASTA 2.0: an improved server for protein aggregation prediction

Walsh

Seno

Tosatto

et al. 2014

Nucleic Acids Research

409

326

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The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

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Section: Methodsmentioning

confidence: 99%

PASTA 2.0: an improved server for protein aggregation prediction

Walsh

Seno

Tosatto

et al. 2014

Nucleic Acids Research

409

326

View full text Add to dashboard Cite

show abstract

“…These data are downloaded from the website (http://protein.bio.unipd.it/repetita/) of the previous study (Marsella et al , 2009). Marsella took an initial set of 32 proteins with solenoid repeats and used the TESE server (Sirocco, 2008) to find more protein domains belonging to the same solenoid folds as the initial set. By limiting the maximal residual structural similarity according to the CATH classification (Pearl et al , 2003), TESE allows the user to generate ad hoc non-redundant sets of proteins with known structure.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Solenoid and non-solenoid protein recognition using stationary wavelet packet transform

Nguyen

Huang

2010

Bioinformatics

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Motivation: Solenoid proteins are emerging as a protein class with properties intermediate between structured and intrinsically unstructured proteins. Containing repeating structural units, solenoid proteins are expected to share sequence similarities. However, in many cases, the sequence similarities are weak and non-detectable. Moreover, solenoids can be degenerated and widely vary in the number of units. So that it is difficult to detect them. Recently, several solenoid repeats detection methods have been proposed, such as self-alignment of the sequence, spectral analysis and discrete Fourier transform of sequence. Although these methods have shown good performance on certain data sets, they often fail to detect repeats with weak similarities. In this article, we propose a new approach to recognize solenoid repeats and non-solenoid proteins using stationary wavelet packet transform (SWPT). Our method associates with three advantages: (i) naturally representing five main factors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavelet features that can capture hidden components from solenoid sequence similarities and distinguish them from global proteins; (iii) obtaining statistics features that capture repeating motifs of solenoid proteins.Results: Our method analyzes the characteristics of amino acid sequence in both spectral and temporal domains using SWPT. Both global and local information of proteins are captured by SWPT coefficients. We obtain and integrate wavelet-based features and statistics-based features of amino acid sequence to improve the classification task. Our proposed method is evaluated by comparing to state-of-the-art methods such as HHrepID and REPETITA. The experimental results show that our algorithm consistently outperforms them in areas under ROC curve. At the same false positive rate, the sensitivity of our WAVELET method is higher than other methods.Availability: http://www.naaan.org/anvo/Software/Software.htmContact: anphuocnhu.vo@mavs.uta.edu

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“…It is because template selection step is employed to screen them against the target and their mutual comparison may exclude the actually closest and reliable hit. This scoring scheme was used by several groups including CaspIta [31] and COMA [25] in CASP8, ATOME2_CBS [32] and FAMSD [33] in CASP9, and ATOME2_CBS [32] and CASPita [31] in CASP10.…”

Section: Sequence Identitymentioning

confidence: 99%

“…However in another case, if an actually correct template is evolutionarily closer to the target and has lesser coverage span, its selection again becomes a questionable dilemma for the person. This measure has been used by several algorithms including BAKER-GINZU and PRO-SP3-TASSER [34] in CASP8, Firestar [35] and GSmetadisorder [36] in CASP9 and CASPita [31] and HHPred [10] in CASP10.…”

Section: Coveragesmentioning

confidence: 99%

Non-Linear and Misleading Template Scoring Criteria: Root Cause of Protein Modelling Inaccuracies

Runthala¹

2015

Curr Synthetic Sys Biol

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Template based protein modelling is currently the most accurate as well as trustworthy method for predicting the correct protein conformations to bridge the constantly increasing gap between the number of experimentally solved protein structures and the count of protein sequences. Our best knowledge based prediction algorithms employing the templates are not highly proficient of consistently selecting the best scoring template(s) to construct a highly accurate protein model. Mutually contrary nature of generic and currently employed template assessment and selection scores further makes this essential modelling step a very tricky and fluky business. Precisely, the article briefly investigates and justifies the impact of fundamentally allowed degree of freedom of a template selection measure on the accuracy of constructed protein models. Several logical guidelines, normally overlooked in a protein modelling task, are analyzed and should be routinely considered. A more reliable and robust scoring measure is thus mandatorily required to select the best possible available template for constructing the most accurate target conformation.

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TESE: generating specific protein structure test set ensembles

Abstract: The TESE server is available for non-commercial use at URL: http://protein.bio.unipd.it/tese/.

Cited by 10 publications

References 10 publications

PASTA 2.0: an improved server for protein aggregation prediction

PASTA 2.0: an improved server for protein aggregation prediction

Solenoid and non-solenoid protein recognition using stationary wavelet packet transform

Non-Linear and Misleading Template Scoring Criteria: Root Cause of Protein Modelling Inaccuracies

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