Using predicted shape string to enhance the accuracy of γ-turn prediction

Zhu, Yaojuan; Li, Tonghua; Li, Dapeng; Zhang, Yun; Xiong, Wenwei; Sun, Jiangming; Tang, Zehui; Chen, Guanyan

doi:10.1007/s00726-011-0889-z

Cited by 16 publications

(12 citation statements)

References 34 publications

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“…Many studies have taken advantage of backbone conformational information (44 -46). Our previous work (47,48) demonstrated that backbone string was important for turn identification as well. The second advantage was that the backbone string was more conservative than the sequence.…”

Section: Discussionmentioning

confidence: 99%

Retrieving Backbone String Neighbors Provides Insights Into Structural Modeling of Membrane Proteins

Sun

Peng

et al. 2012

Molecular & Cellular Proteomics

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Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold. Molecular & Cellular Proteomics 11: 10.1074/mcp. M111.016808, 1-7, 2012.Determining the structures of membrane proteins remains a relatively unexplored frontier in structural biology (1). Current computational methods include de novo protein modeling and comparative modeling. Compared with de novo modeling, the comparative modeling is more successful when sequence homologies are available. However, because relatively few membrane proteins have been identified through experimentation, building membrane protein conformations remain an extremely difficult and daunting undertaking.In comparative modeling or other methods based on known protein structures, the identification of the best structure neighbor (template), if indeed any are available, is critical. The typical method of template identification relies on serial pairwise sequence alignments aided by database search engines such as FASTA (2) and BLAST (3). More sensitive methods based on multiple sequence alignments, including. PSI-BLAST (4), CLUSTALW (5), and HMMER (6), are available. MSAs have been shown to produce a greater number of potential templates and to better identify templates for sequences that have homologue relationships to other solved structures. However, when there is no significant homology found, most of the target-template pairs are evolutionarily too distant to be detected with the current threading approaches (7). If no other information about the target is known, aside from the sequence, it becomes difficult to identify possi...

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Section: Discussionmentioning

confidence: 99%

Retrieving Backbone String Neighbors Provides Insights Into Structural Modeling of Membrane Proteins

Sun

Peng

et al. 2012

Molecular & Cellular Proteomics

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“…Shape string is a one-dimensional string of symbols, which can carry more structural information than the classical secondary structure representation [27]. Typically, shape T reflects the turn structure in protein, and predicted shape T could help to identify the turns [16], [17]. The observed shape string can be freely obtained based on a sequence of known structure from the web server [12].…”

Section: Methodsmentioning

confidence: 99%

“…In our previous studies, the predicted shape string was explored as an effective feature to promote the accuracies of predicting a β–turn [16], a γ–turn [17], a unified turn model [18], a DNA-binding residue [19] and a domain boundary [20]. The shape string was also considered as a backbone string to reconstruct the modeling of membrane proteins [21].…”

Section: Introductionmentioning

confidence: 99%

NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

et al. 2013

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Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

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“…γ-turn is the second most characterized tight turn, which involves three amino acid residues and a hydrogen bond between the backbone CO (i) and the backbone NH (i+2) . The problems of γ-turn prediction can be divided into two categories: prediction of γ-turn types [12] and prediction of γ-turn/non-γ-turn [13], [14], [15]. The method proposed by our group [15], which utilized G-means metrics as the optimal criterion for SVM and predicted shape string as a new variable.…”

Section: Introductionmentioning

confidence: 99%

“…The problems of γ-turn prediction can be divided into two categories: prediction of γ-turn types [12] and prediction of γ-turn/non-γ-turn [13], [14], [15]. The method proposed by our group [15], which utilized G-means metrics as the optimal criterion for SVM and predicted shape string as a new variable. α-turn is always present on the exposed surface of the protein and contains specific information for the molecular recognition process.…”

Section: Introductionmentioning

confidence: 99%

Predicting Turns in Proteins with a Unified Model

Song

Peng

et al. 2012

PLoS ONE

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MotivationTurns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously.ResultsIn this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

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Using predicted shape string to enhance the accuracy of γ-turn prediction

Cited by 16 publications

References 34 publications

Retrieving Backbone String Neighbors Provides Insights Into Structural Modeling of Membrane Proteins

Retrieving Backbone String Neighbors Provides Insights Into Structural Modeling of Membrane Proteins

NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

Predicting Turns in Proteins with a Unified Model

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