Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Abstract

Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein-protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA-protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on N-alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA-protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein-protein interactions, demonstrating the method's utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

Figure 1

Fragments used for the creation of virtual tripeptoid libraries. A list of 37 primary amines, along with T2AA, was used to create a combinatorial virtual library of peptoid-based compounds. N* indicates the location of the −NH₂ group, which is the position of substitution into the tripeptoid backbone. Blue labels indicate fragments that were used in the initial screen of tripeptoids containing a combinatorial set of 20 primary amines.

Figure 2

Glide docking-based frequency with which fragments appeared in the three substitution positions on a tripeptoid backbone. A set of 20 fragments, in addition to hydrogen, were virtually combinatorially incorporated into a tripeptoid backbone and were screened against four different crystal structures of PCNA (PDB IDs: 1AXC, 3VKX, 1U7B, and 2ZVK) in silico using the Glide SP and XP docking algorithms. The frequency that respective fragments appeared in positions R₁, R₂ or R₃ (lower right) were tallied for the top 50 hits from each run involving a different crystal structure. The percentage of the cumulative total of substitution frequency (out of a possible 200) at a given position is shown above the stacked columns. Hydrogen as a substituent is labeled as “Gly”.

Figure 3

Synthesis of T2AA- or non-T2AA-containing tripeptoids. (a) C₂H₃BrO₂ (1 M in DMF, 20 equiv), DIC (19 equiv), 1 h, 35 °C; (b) H₂N-R₃ (1 M in DMF), 2 h, RT; (c) H₂N-R₂ (1 M in DMF), 2 h, RT; (d) H₂N-R₁ (1 M in DMF), 2 h, RT; (e) T2AA (19.5 equiv), DIEA (39 equiv), DMF, 16 h, RT; and (f) TFA/TIS/H₂O (95:2.5:2.5), 1 or 3 h, RT.

Figure 4

Dose response curves of four of the top hit peptoid-based compounds from the fluorescent polarization screen. Peptoid-based ligands were subjected to two-fold dose response analysis. Solutions were plated in duplicates of four, and error bars represent the standard error around the mean. T2AA (1 mM) in dimethyl sulfoxide (DMSO) serves as the positive control, and DMSO alone serves as the negative control. Data for all remaining compounds in Table 1 can be found in the Supporting Information, Figure S7.

Figure 5

Average structures of the final 50 frames of MDs simulations. The final 50 simulation trajectory frames for each analyzed peptoid ligand were averaged using VMD, and the resulting structures visualized using Pymol. The hydrophobic pocket (orange) and PIPM glutamine binding site (blue) are highlighted in each. The whole structure of the PCNA monomer (based on PDB ID: 1VYJ) is shown in the bottom right for comparative purposes.

Figure 6

PCA of PCNA topology variance. The final 100 frames of each PCNA–ligand MD trajectory were aligned based on the position of the PCNA Cα backbone atoms. A PCA of the aligned trajectories shows differential clustering of PCNA conformations (residues 1–257) when in complex with either a peptoid-based ligand or a PIPM-containing peptide. Principal components 1–3 (PC1, PC2, and PC3) for each structure were clustered and plotted along with the proportion of variance for each principal component.

Figure 7

Peptoid inhibitors disrupt key PCNA–PIPM interactions. (Top) Competitive peptoid inhibitors, such as NLys-NPip-NBal (yellow sticks), are projected to bind at the PIPM-binding site on PCNA (gray surface; PDB ID: 1VYJ), which overlaps with the PL peptide (green cartoon and sticks). (Bottom left) NLys-NPip-NBal overlays key contact points between the PL peptide’s PIPM and PCNA. Spheres represent PIPM amino acid residues. Colors indicate direct disruption of key (red) or nonkey (orange) residues and nondisruption of key (yellow) or nonkey (white) residues. (Bottom right) Results from Pedley, et al. (2014) demonstrating that residues 1, 4, 7, and 8 of the PL peptide’s PIPM act as anchoring residues. ** Changes in surface accessible surface area were calculated with ANCHOR, measuring the differences between bound and unbound forms of the PL peptide.

Figure 8

PCNA inhibitors synergism with doxorubicin. T2AA, T2AA-NEal-NTyr, and NLys-NPip-NBal synergized with doxorubicin both in A549 and MDA-MB-231. These data used a clonogenic assay format with cell counts normalized to the plating efficiency value as 100%.

Figure 9

PCNA inhibitors’ effect on noncancer cell growth in combination with DNA-damage agents. None of the PCNA antagonists tested showed the ability to enhance either doxorubicin (top) or cisplatin (bottom) effects on cell growth in HEK293 cells. These studies used the (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (MTT) proliferation assay with the standard dosing described for each DNA-damaging agent.