Predicting the Solvent Accessibility of Transmembrane Residues from Protein Sequence

Zheng, Yawen; Zhang, Fasheng; Davis, Melissa J.; Bodén, Mikael; Teasdale, Rohan D.

doi:10.1021/pr050397b

Cited by 53 publications

(65 citation statements)

References 37 publications

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“…[17][18][19] (b) three simple methods for finding sequence profiles p: Notice that some of these numbers correspond to one amino acid scale only. Such numbers are length or inverse length of sequence moment vectors m. Other scalar descriptors reflect the difference in sequence profiles calculated with two different amino acid scales, such as the angle between two sequence moments or some function involving the angle between two sequence moments.…”

Section: Introductionmentioning

confidence: 99%

Computational Design of Highly Selective Antimicrobial Peptides

Juretić

Vukičević

Ilić

et al. 2009

J. Chem. Inf. Model.

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We have created a structure-selectivity database (AMPad) of frog-derived, helical antimicrobial peptides (AMPs), in which the selectivity was determined as a therapeutic index (TI), and then used the novel concept of sequence moments to study the lengthwise asymmetry of physicochemical peptide properties. We found that the cosine of the angle between two sequence moments obtained with different hydrophobicity scales, defined as the D-descriptor, identifies highly selective peptide antibiotics. We could then use this descriptor to predict TI changes after point mutations in known AMPs, and to aid the prediction of TI for de noVo designed AMPs. In combination with an amino acid selectivity index, a motif regularity index and other statistical rules extracted from AMPad, the D-descriptor enabled construction of the AMP-Designer algorithm. A 23 residue, glycine-rich, peptide suggested by the algorithm was synthesized and the activity and selectivity tested. This peptide, adepantin 1, is less than 50% identical to any other AMP, has a potent antibacterial activity against the reference organism, E. coli, and has a significantly greater selectivity (TI > 200) than the best AMP present in the AMPad database (TI ) 125).

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Section: Introductionmentioning

confidence: 99%

Computational Design of Highly Selective Antimicrobial Peptides

Juretić

Vukičević

Ilić

et al. 2009

J. Chem. Inf. Model.

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“…[10] Here, we also applied the dataset of b-barrel TM residues utilized in Yuan work to compare the performance of our method with Yuan method (Table 5). Compared with Yuan method, the CC and MAE of our method are 0.69 and 11.9, respectively.…”

Section: Journal Of Computational Chemistrymentioning

confidence: 99%

“…For example, residues exposed to lipid environment or buried inside a protein can be identified by accessible surface area (ASA). [7][8][9][10] The value of ASA can also be used to predict protein-protein interaction hot spots, which might play an important role in drug design and protein engineering. [11] To calculate solvent accessibility of TM residue, the 3D structure of membrane protein is indispensable information.…”

Section: Introductionmentioning

confidence: 99%

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A sequence‐based computational model for the prediction of the solvent accessible surface area for α‐helix and β‐barrel transmembrane residues

Wang

et al. 2011

J Comput Chem

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Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.

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“…In this respect, Yuan et al developed a method (hereafter called the YU method) for predicting the relative solvent accessible surface area (rSASA) of transmembrane (TM) proteins. 36 Based on this numeric value, one may predict the exposure status of the TM residues. 36 Here, we reimplemented the YU method and show that the prediction accuracy of BTMX for classification as buried or exposed residues is significantly higher than the YU method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Exposure Status of Transmembrane Beta Barrel Residues From Protein Sequence

Hayat

Walter

Park

et al. 2011

J. Bioinform. Comput. Biol.

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We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar C(α)-C(β) vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at http://service.bioinformatik.uni-saarland.de/tmx-site/. Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.

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Predicting the Solvent Accessibility of Transmembrane Residues from Protein Sequence

Cited by 53 publications

References 37 publications

Computational Design of Highly Selective Antimicrobial Peptides

Computational Design of Highly Selective Antimicrobial Peptides

A sequence‐based computational model for the prediction of the solvent accessible surface area for α‐helix and β‐barrel transmembrane residues

Prediction of the Exposure Status of Transmembrane Beta Barrel Residues From Protein Sequence

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