Figure 1. Stability analysis of dipeptides within CBD3 and
pharmacophore model on average cluster center ofA1R2 dipeptide and sample of
matched hits. (A) Largest cluster of MD snapshots with RMSD less than 1
Å for dipeptides A1R2,
R2S3,
S3R4 and
R4L5 , also shown is average percentage
of cluster size in 3 independent simulations. (B) RMSD relative to
cluster center and times the amino acid side chains are free of contacts
(3.8 Å for hydrogen bond donors and 4 Å for hydrophobic side chains)
within 3 ns windows in a representative MD trajectory of CBD3 peptide.
Gray regions highlight regions for which RMSD of snapshots are less than
1 Å from cluster center, and side chains are 80% or more free of
contacts within 3 ns windows. (C) Pharmacophores radiuses used:
guanidine group, 0.78 Å for positive ion and 1 Å for hydrogen bond
donor; 0.78 Å for other hydrogen bond donors, and 1.0 Å for hydrophobic
atoms. (D) Sample of compounds that matched all pharmacophores using
ZincPharmer.
TheA1R2 structural motif shown inFigure 1A , defined as the conformation having the largest 1 Å
radius cluster of snapshots in our MDS, encompassed about 44% of our
total runs. Furthermore, since previously reported cell-based
experiments also involved the cell-penetrating tat sequence conjugated
to CRMP2 derived peptides, we repeated the MDS on the full
tat-ARSRLA sequence, obtaining the same structural motif as for
CBD3 (Figure S1 ). The robustness of theA1R2 motif led us to use it as
the basis for the design of the pharmacophore model shown inFigure 1C . We entered this design in the open access server
ZincPharmer
(http://zincpharmer.csb.pitt.edu/)
to search for suitable compounds among more than 27 million commercially
available compounds from the ZINC database (Sterling & Irwin, 2015),
obtaining ~200 hits. Based on availability and manual
curation, we selected 77 compounds for experimental validation
(Figure 1D ). Figure S2 shows the full list of
compounds.