Limits...
Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23

View Article: PubMed Central - PubMed

ABSTRACT

Background: Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity.

Results: We developed and applied a hierarchical clustering protocol based on DRP structural similarity, followed by two post-processing steps, to classify 806 unique DRP structures into 81 clusters. The 20 most populated clusters comprised 85% of all DRPs. Representative scaffolds were selected from each of these clusters; the representatives were structurally distinct from one another, but similar to other DRPs in their respective clusters. To demonstrate the utility of the clusters, phage libraries were constructed for three of the representative scaffolds and panned against interleukin-23. One library produced a peptide that bound to this target with an IC50 of 3.3 μM.

Conclusions: Most DRP clusters contained members that were diverse in sequence, host organism, and interacting proteins, indicating that cluster members were functionally diverse despite having similar structure. Only 20 peptide scaffolds accounted for most of the natural DRP structural diversity, providing suitable starting points for seeding phage display experiments. Through selection of the scaffold surface to vary in phage display, libraries can be designed that present sequence diversity in architecturally distinct, biologically relevant combinations of secondary structures. We supported this hypothesis with a proof-of-concept experiment in which three phage libraries were constructed and panned against the IL-23 target, resulting in a single-digit μM hit and suggesting that a collection of libraries based on the full set of 20 scaffolds increases the potential to identify efficiently peptide binders to a protein target in a drug discovery program.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-1350-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


Protocol details. a Pipeline workflow. b Example of hierarchical clustering using toy data, portrayed as a tree where the leaves are DRPs and each inner node represents a cluster containing all DRPs in the sub-tree rooted at that node. Numbers at the branch point are the values of the distance metric when calculated across the two sub-trees that are being merged at the inner node. The red line is the empirically selected cutoff (here, 0.7); all sub-trees to the right of this cutoff represent the final clusters
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5120537&req=5

Fig1: Protocol details. a Pipeline workflow. b Example of hierarchical clustering using toy data, portrayed as a tree where the leaves are DRPs and each inner node represents a cluster containing all DRPs in the sub-tree rooted at that node. Numbers at the branch point are the values of the distance metric when calculated across the two sub-trees that are being merged at the inner node. The red line is the empirically selected cutoff (here, 0.7); all sub-trees to the right of this cutoff represent the final clusters

Mentions: Our computational pipeline consisted of five steps: (i) filtering, (ii) hierarchical clustering using native overlap as the distance metric, (iii) reclustering knottins using disulfide distance as the distance metric, (iv) re-assigning longer singletons, and (v) re-assigning shorter singletons (Fig. 1a; Methods).Fig. 1


Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23
Protocol details. a Pipeline workflow. b Example of hierarchical clustering using toy data, portrayed as a tree where the leaves are DRPs and each inner node represents a cluster containing all DRPs in the sub-tree rooted at that node. Numbers at the branch point are the values of the distance metric when calculated across the two sub-trees that are being merged at the inner node. The red line is the empirically selected cutoff (here, 0.7); all sub-trees to the right of this cutoff represent the final clusters
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120537&req=5

Fig1: Protocol details. a Pipeline workflow. b Example of hierarchical clustering using toy data, portrayed as a tree where the leaves are DRPs and each inner node represents a cluster containing all DRPs in the sub-tree rooted at that node. Numbers at the branch point are the values of the distance metric when calculated across the two sub-trees that are being merged at the inner node. The red line is the empirically selected cutoff (here, 0.7); all sub-trees to the right of this cutoff represent the final clusters
Mentions: Our computational pipeline consisted of five steps: (i) filtering, (ii) hierarchical clustering using native overlap as the distance metric, (iii) reclustering knottins using disulfide distance as the distance metric, (iv) re-assigning longer singletons, and (v) re-assigning shorter singletons (Fig. 1a; Methods).Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity.

Results: We developed and applied a hierarchical clustering protocol based on DRP structural similarity, followed by two post-processing steps, to classify 806 unique DRP structures into 81 clusters. The 20 most populated clusters comprised 85% of all DRPs. Representative scaffolds were selected from each of these clusters; the representatives were structurally distinct from one another, but similar to other DRPs in their respective clusters. To demonstrate the utility of the clusters, phage libraries were constructed for three of the representative scaffolds and panned against interleukin-23. One library produced a peptide that bound to this target with an IC50 of 3.3 μM.

Conclusions: Most DRP clusters contained members that were diverse in sequence, host organism, and interacting proteins, indicating that cluster members were functionally diverse despite having similar structure. Only 20 peptide scaffolds accounted for most of the natural DRP structural diversity, providing suitable starting points for seeding phage display experiments. Through selection of the scaffold surface to vary in phage display, libraries can be designed that present sequence diversity in architecturally distinct, biologically relevant combinations of secondary structures. We supported this hypothesis with a proof-of-concept experiment in which three phage libraries were constructed and panned against the IL-23 target, resulting in a single-digit μM hit and suggesting that a collection of libraries based on the full set of 20 scaffolds increases the potential to identify efficiently peptide binders to a protein target in a drug discovery program.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-1350-9) contains supplementary material, which is available to authorized users.

No MeSH data available.