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Combinatorial approaches: a new tool to search for highly structured beta-hairpin peptides.

ABSTRACT: Here we present a combinatorial approach to evolve a stable beta-hairpin fold in a linear peptide. Starting with a de novo-designed linear peptide that shows a beta-hairpin structure population of around 30%, we selected four positions to build up a combinatorial library of 20(4) sequences. Deconvolution of the library using circular dichroism reduced such a sequence complexity to 36 defined sequences. Circular dichroism and NMR of these peptides resulted in the identification of two linear 14-aa-long peptides that in plain buffered solutions showed a percentage of beta-hairpin structure higher than 70%. Our results show how combinatorial approaches can be used to obtain highly structured peptide sequences that could be used as templates in which functionality can be introduced.

SUBMITTER: Pastor MT

PROVIDER: S-EPMC117354 | biostudies-literature | 2002 Jan

REPOSITORIES: biostudies-literature

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Combinatorial approaches: a new tool to search for highly structured beta-hairpin peptides.

Pastor Maria Teresa MT López de la Paz Manuela M Lacroix Emmanuel E Serrano Luis L Pérez-Payá Enrique E

Proceedings of the National Academy of Sciences of the United States of America 20020108 2

Here we present a combinatorial approach to evolve a stable beta-hairpin fold in a linear peptide. Starting with a de novo-designed linear peptide that shows a beta-hairpin structure population of around 30%, we selected four positions to build up a combinatorial library of 20(4) sequences. Deconvolution of the library using circular dichroism reduced such a sequence complexity to 36 defined sequences. Circular dichroism and NMR of these peptides resulted in the identification of two linear 14-a ...[more]

PMID: 11782528

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Project description:The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated) approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters)-each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity c in RO((N + nm) log n), where N is the size of the output patterns and (n x m) is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a beta-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1) The method recovers states previously obtained by visually analyzing free energy surfaces. (2) It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the beta-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3) The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the choice of reaction coordinates. (An abstract version of this work was presented at the 2005 Asia Pacific Bioinformatics Conference [1].).

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/.

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Combinatorial approaches: a new tool to search for highly structured beta-hairpin peptides.

Publications

Combinatorial approaches: a new tool to search for highly structured beta-hairpin peptides.

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