Dataset Information

Designing photoswitchable peptides using the AsLOV2 domain.

ABSTRACT: Photocontrol of functional peptides is a powerful tool for spatial and temporal control of cell signaling events. We show that the genetically encoded light-sensitive LOV2 domain of Avena Sativa phototropin 1 (AsLOV2) can be used to reversibly photomodulate the affinity of peptides for their binding partners. Sequence analysis and molecular modeling were used to embed two peptides into the J? helix of the AsLOV2 domain while maintaining AsLOV2 structure in the dark but allowing for binding to effector proteins when the J? helix unfolds in the light. Caged versions of the ipaA and SsrA peptides, LOV-ipaA and LOV-SsrA, bind their targets with 49- and 8-fold enhanced affinity in the light, respectively. These switches can be used as general tools for light-dependent colocalization, which we demonstrate with photo-activable gene transcription in yeast.

SUBMITTER: Lungu OI

PROVIDER: S-EPMC3334866 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Designing photoswitchable peptides using the AsLOV2 domain.

Lungu Oana I OI Hallett Ryan A RA Choi Eun Jung EJ Aiken Mary J MJ Hahn Klaus M KM Kuhlman Brian B

Chemistry & biology 20120401 4

Photocontrol of functional peptides is a powerful tool for spatial and temporal control of cell signaling events. We show that the genetically encoded light-sensitive LOV2 domain of Avena Sativa phototropin 1 (AsLOV2) can be used to reversibly photomodulate the affinity of peptides for their binding partners. Sequence analysis and molecular modeling were used to embed two peptides into the Jα helix of the AsLOV2 domain while maintaining AsLOV2 structure in the dark but allowing for binding to ef ...[more]

PMID: 22520757

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Project description:BackgroundCell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis.MethodsIn the present study, support vector machine (SVM)-based models have been developed for predicting and designing highly effective cell penetrating peptides. Various features like amino acid composition, dipeptide composition, binary profile of patterns, and physicochemical properties have been used as input features. The main dataset used in this study consists of 708 peptides. In addition, we have identified various motifs in cell penetrating peptides, and used these motifs for developing a hybrid prediction model. Performance of our method was evaluated on an independent dataset and also compared with that of the existing methods.ResultsIn cell penetrating peptides, certain residues (e.g. Arg, Lys, Pro, Trp, Leu, and Ala) are preferred at specific locations. Thus, it was possible to discriminate cell-penetrating peptides from non-cell penetrating peptides based on amino acid composition. All models were evaluated using five-fold cross-validation technique. We have achieved a maximum accuracy of 97.40% using the hybrid model that combines motif information and binary profile of the peptides. On independent dataset, we achieved maximum accuracy of 81.31% with MCC of 0.63.ConclusionThe present study demonstrates that features like amino acid composition, binary profile of patterns and motifs, can be used to train an SVM classifier that can predict cell penetrating peptides with higher accuracy. The hybrid model described in this study achieved more accuracy than the previous methods and thus may complement the existing methods. Based on the above study, a user-friendly web server CellPPD has been developed to help the biologists, where a user can predict and design CPPs with much ease. CellPPD web server is freely accessible at http://crdd.osdd.net/raghava/cellppd/.

Dataset Information

Designing photoswitchable peptides using the AsLOV2 domain.

Publications

Designing photoswitchable peptides using the AsLOV2 domain.

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