Accelerated Identification of Cell Active KRAS Inhibitory Macrocyclic Peptides using Mixture Libraries and Automated Ligand Identification System (ALIS) Technology

Abstract

Macrocyclic peptides can disrupt previously intractable protein-protein interactions (PPIs) relevant to oncology targets such as KRAS. Early hits often lack cellular activity and require meticulous improvement of affinity, permeability, and metabolic stability to become viable leads. We have validated the use of the Automated Ligand Identification System (ALIS) to screen oncogenic KRAS^G12D (GDP) against mass-encoded mini-libraries of macrocyclic peptides and accelerate our structure-activity relationship (SAR) exploration. These mixture libraries were generated by premixing various unnatural amino acids without the need for the laborious purification of individual peptides. The affinity ranking of the peptide sequences provided SAR-rich data sets that led to the selection of novel potency-enhancing substitutions in our subsequent designs. Additional stability and permeability optimization resulted in the identification of peptide 7 that inhibited pERK activity in a pancreatic cancer cell line. More broadly, this methodology offers an efficient alternative to accelerate the fastidious hit-to-lead optimization of PPI peptide inhibitors.

Figure 1

“Accelerated” vs traditional peptide synthesis workflow. For the synthesis of N different macrocyclic peptides, the traditional solid-phase peptide synthesis (SPPS) workflow is repeated N times, effectively starting from N individual solid supports (resins), coupling individual amino acids to build the N linear peptides in N different containers. Cleavage off the resin, side chain deprotection, and cyclization steps are performed on N individual peptides, followed by separate lengthy reversed-phase preparative HPLC purification of the N singletons. In contrast, our “accelerated” workflow started from a single standard solid support and involved the coupling of equimolar mixtures of N amino acids to the growing peptide to generate the N different designed sequences as a mixture in a single container. Upon cleavage and side chain deprotection of the linear peptides off the resin, standard cyclization afforded the crude mixture. Then, a single, fast semipurification using reversed-phase flash column chromatography (FCC) afforded the final mixture library of N macrocyclic peptides that was tested in ALIS.

Figure 2

Structures and reported biochemical activities of cyclic peptides KRpep-2d and KRpep-2^, against KRAS^G12D (GDP). Standard one-letter codes for canonical amino acids are used to identify residues on KRpep-2d structure. Key binding residues Leu⁷, Ile⁹, and Asp¹² are highlighted in yellow. The only difference between the two structures is the presence of Arg¹, Arg², Arg¹⁸ and Arg¹⁹ in KRpep-2d. The same residues numbering was kept for KRpep-2 in this work.

Figure 3

Mixture Library 1 design and ACE₅₀ results. (a) Design of premixed amino acids to explore substitutions for Tyr⁸ in combination with substitutions for Ile⁹. Original Tyr at position 8 and Ile at position 9 were included to serve as internal reference. (b) Peptides were numbered as shown on matrix grid. Heatmap indicates the rank-ordered affinities for KRAS^G12D (GDP) measured in ACE₅₀ experiment, from red (weaker binders) to green (highest affinity binders). White indicates “not determined”.

Figure 4

Correlation of SOS potencies for Library 1 singletons against ACE₅₀ results for Mixture Library 1. Regression was performed on peptides binding in both the ACE and SOS assay. Peptide 1-09 emerged as the best binder of the library, with an 8-fold improvement in SOS potency over KRpep-2 (2).

Figure 5

Mixture Library 2 design and ACE₅₀ results. (a) Design of premixed amino acids to explore substitutions for Ser¹⁰ in combination with substitutions for Tyr¹¹. Original Ser at position 10 and Tyr at position 11 were included to serve as internal references. (b) Peptides were numbered as shown on the matrix grid. Heatmap indicates the rank-ordered affinities for KRAS^G12D (GDP) measured in the ACE₅₀ experiment, from red (weaker binders) to green (highest affinity binders). White indicates “not determined”.

Figure 6

Correlation of SOS potencies for Library 2 singletons against ACE₅₀ results for Mixture Library 2. Regression was performed on peptides binding both in ACE and SOS assay. No peptide showed improved potency over reference 2 (KRpep-2).

Figure 7

Mixture Libraries 3 and 4 design. (a) Design of premixed amino acids to explore substitutions for Pro⁶ in combination with substitutions for Leu⁷. Original Pro at position 6 and Leu at position 7 were included to serve as internal reference. (b) Design of premixed amino acids to explore substitutions for Pro¹³ in combination with substitutions for Val¹⁴. Original Pro at position 13 and Val at position 14 were included to serve as internal reference.

Figure 8

Mixture Libraries 3 (a) and 4 (b) ACE₅₀ results. Peptides were numbered as shown on the matrix grid. Heatmap indicates the rank-ordered affinities for KRAS measured in ACE₅₀ experiment, from red (weaker binders) to green (highest affinity binders). White indicates “not determined”.

Figure 9

Correlation of SOS potencies for Libraries 3 (a) and 4 (b) singletons against ACE₅₀ results. Regression was performed on peptides binding both in ACE and SOS assays. Peptides 3-05, 3-07, 3-09, and 4-02 showed improved potency over reference 1-09.