Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Bcl-2 family proteins regulate apoptosis, and aberrant interactions of overexpressed antiapoptotic family members such as Mcl-1 promote cell transformation, cancer survival, and resistance to chemotherapy. Discovering potent and selective Mcl-1 inhibitors that can relieve apoptotic blockades is thus a high priority for cancer research. An attractive strategy for disabling Mcl-1 involves using designer peptides to competitively engage its binding groove, mimicking the structural mechanism of action of native sensitizer BH3-only proteins. We transformed Mcl-1-binding peptides into α-helical, cell-penetrating constructs that are selectively cytotoxic to Mcl-1-dependent cancer cells. Critical to the design of effective inhibitors was our introduction of an all-hydrocarbon cross-link or "staple" that stabilizes α-helical structure, increases target binding affinity, and independently confers binding specificity for Mcl-1 over related Bcl-2 family paralogs. Two crystal structures of complexes at 1.4 Å and 1.9 Å resolution demonstrate how the hydrophobic staple induces an unanticipated structural rearrangement in Mcl-1 upon binding. Systematic sampling of staple location and iterative optimization of peptide sequence in accordance with established design principles provided peptides that target intracellular Mcl-1. This work provides proof of concept for the development of potent, selective, and cell-permeable stapled peptides for therapeutic targeting of Mcl-1 in cancer, applying a design and validation workflow applicable to a host of challenging biomedical targets.

Overexpression of anti-apoptotic Bcl-2 family proteins contributes to cancer progression and confers resistance to chemotherapy. Small molecules that target Bcl-2 are used in the clinic to treat leukemia, but tight and selective inhibitors are not available for Bcl-2 paralog Bfl-1. Guided by computational analysis, we designed variants of the native BH3 motif PUMA that are > 150-fold selective for Bfl-1 binding. The designed peptides potently trigger disruption of the mitochondrial outer membrane in cells dependent on Bfl-1, but not in cells dependent on other anti-apoptotic homologs. High-resolution crystal structures show that designed peptide FS2 binds Bfl-1 in a shifted geometry, relative to PUMA and other binding partners, due to a set of epistatic mutations. FS2 modified with an electrophile reacts with a cysteine near the peptide-binding groove to augment specificity. Designed Bfl-1 binders provide reagents for cellular profiling and leads for developing enhanced and cell-permeable peptide or small-molecule inhibitors.DOI: http://dx.doi.org/10.7554/eLife.25541.001

AbstractcGAS is an evolutionarily conserved enzyme that has a pivotal role in immune defence against infection1–3. In vertebrate animals, cGAS is activated by DNA to produce cyclic GMP–AMP (cGAMP)4,5, which leads to the expression of antimicrobial genes6,7. In bacteria, cyclic dinucleotide (CDN)-based anti-phage signalling systems (CBASS) have been discovered8–11. These systems are composed of cGAS-like enzymes and various effector proteins that kill bacteria on phage infection, thereby stopping phage spread. Of the CBASS systems reported, approximately 39% contain Cap2 and Cap3, which encode proteins with homology to ubiquitin conjugating (E1/E2) and deconjugating enzymes, respectively8,12. Although these proteins are required to prevent infection of some bacteriophages8, the mechanism by which the enzymatic activities exert an anti-phage effect is unknown. Here we show that Cap2 forms a thioester bond with the C-terminal glycine of cGAS and promotes conjugation of cGAS to target proteins in a process that resembles ubiquitin conjugation. The covalent conjugation of cGAS increases the production of cGAMP. Using a genetic screen, we found that the phage protein Vs.4 antagonized cGAS signalling by binding tightly to cGAMP (dissociation constant of approximately 30 nM) and sequestering it. A crystal structure of Vs.4 bound to cGAMP showed that Vs.4 formed a hexamer that was bound to three molecules of cGAMP. These results reveal a ubiquitin-like conjugation mechanism that regulates cGAS activity in bacteria and illustrates an arms race between bacteria and viruses through controlling CDN levels.

Understanding the relationship between protein sequence and structure well enough to rationally design novel proteins or protein complexes is a longstanding goal in protein science. The Protein Data Bank (PDB) is a key resource for defining sequence-structure relationships that has supported the development of critical resources such as rotamer libraries and backbone torsional statistics that quantify the probabilities of protein sequences adopting different structures. Here, we show that well-defined, noncontiguous structural motifs (TERMs) in the PDB can also provide rich information useful for proteinpeptide interaction prediction and design. Specifically, we show that it is possible to rapidly predict the binding energies of peptides to Bcl-2 family proteins as accurately as can be done with widely used structure-based tools, without explicit atomistic modeling. One benefit of a TERM-based approach is that prediction performance is less sensitive to the details of the input structure than are methods that evaluate energies using precise atomic coordinates. We show that protein design using TERM energies (dTERMen) can generate highly novel and diverse peptides to target anti-apoptotic proteins Bfl-1 and Mcl-1. 15 of 17 peptides designed using dTERMen bound tightly to their intended targets, and these peptides have just 15 -38% sequence identity to any known native Bcl-2 family protein ligand. Highresolution structures of four designed peptides bound to their targets provided opportunities to analyze strengths and limitations of this approach. Dramatic success designing peptides using dTERMen, which comprised going from input structure to experimental validation of high-affinity binders in approximately one month, provides strong motivation for further developing TERM-based approaches to design.