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

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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.


Cluster DRP coverage. Clusters were sorted by size from most to least populated and each cluster was assigned an index starting with 1. At each index i, the cumulative number of DRPs in that cluster and all clusters with index less than i was calculated and divided by the total number of DRPs in the dataset, resulting in the coverage. Coverage as a function of index is displayed. Coverage curves are shown after completion of successive steps of the procedure (red: initial clustering; green: knottin reclustering; purple: longer singleton post-processing; blue: shorter singleton post-processing)
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Fig3: Cluster DRP coverage. Clusters were sorted by size from most to least populated and each cluster was assigned an index starting with 1. At each index i, the cumulative number of DRPs in that cluster and all clusters with index less than i was calculated and divided by the total number of DRPs in the dataset, resulting in the coverage. Coverage as a function of index is displayed. Coverage curves are shown after completion of successive steps of the procedure (red: initial clustering; green: knottin reclustering; purple: longer singleton post-processing; blue: shorter singleton post-processing)

Mentions: Clusters containing four or more DRPs annotated with the knottin fold were given as input to the average-linkage hierarchical clustering algorithm, here using the distance between equivalent disulfide bonds as the distance metric (Methods). A disulfide distance cutoff of 2.0 Å was again selected by trial-and-error. This cutoff resulted in high structural overlap of disulfide bonds across DRPs in the knottin clusters (Fig. 2d) with a separation of ~1.8 Å between consecutive groups of bonds in the most populated cluster despite 91 members being present. The cutoff of 1.5 Å resulted in a similar separation, but here, only 64 members were in the most populated cluster (Fig. 2e), resulting in suboptimal lower coverage. The cutoff of 2.5 Å led to 131 members in the most populated cluster, but there was no clear visual separation apparent in consecutive groups of disulfide bonds (Fig. 2f). This cutoff would likely render selection of a representative scaffold problematic, as there would be no DRP in the cluster that possessed a set of disulfide bonds structurally equivalent to all other members of the cluster. The optimal cutoff of 2.0 Å reduced the number of clusters containing four or more knottins from 15 to 4 (Additional file 1: Table S2). Together with all non-knottin clusters produced in step ii, there were 176 intermediate DRP clusters (Fig. 3).Fig. 3


Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23
Cluster DRP coverage. Clusters were sorted by size from most to least populated and each cluster was assigned an index starting with 1. At each index i, the cumulative number of DRPs in that cluster and all clusters with index less than i was calculated and divided by the total number of DRPs in the dataset, resulting in the coverage. Coverage as a function of index is displayed. Coverage curves are shown after completion of successive steps of the procedure (red: initial clustering; green: knottin reclustering; purple: longer singleton post-processing; blue: shorter singleton post-processing)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Cluster DRP coverage. Clusters were sorted by size from most to least populated and each cluster was assigned an index starting with 1. At each index i, the cumulative number of DRPs in that cluster and all clusters with index less than i was calculated and divided by the total number of DRPs in the dataset, resulting in the coverage. Coverage as a function of index is displayed. Coverage curves are shown after completion of successive steps of the procedure (red: initial clustering; green: knottin reclustering; purple: longer singleton post-processing; blue: shorter singleton post-processing)
Mentions: Clusters containing four or more DRPs annotated with the knottin fold were given as input to the average-linkage hierarchical clustering algorithm, here using the distance between equivalent disulfide bonds as the distance metric (Methods). A disulfide distance cutoff of 2.0 Å was again selected by trial-and-error. This cutoff resulted in high structural overlap of disulfide bonds across DRPs in the knottin clusters (Fig. 2d) with a separation of ~1.8 Å between consecutive groups of bonds in the most populated cluster despite 91 members being present. The cutoff of 1.5 Å resulted in a similar separation, but here, only 64 members were in the most populated cluster (Fig. 2e), resulting in suboptimal lower coverage. The cutoff of 2.5 Å led to 131 members in the most populated cluster, but there was no clear visual separation apparent in consecutive groups of disulfide bonds (Fig. 2f). This cutoff would likely render selection of a representative scaffold problematic, as there would be no DRP in the cluster that possessed a set of disulfide bonds structurally equivalent to all other members of the cluster. The optimal cutoff of 2.0 Å reduced the number of clusters containing four or more knottins from 15 to 4 (Additional file 1: Table S2). Together with all non-knottin clusters produced in step ii, there were 176 intermediate DRP clusters (Fig. 3).Fig. 3

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.