Hydrocarbon stapling and macrocyclization can transform a transient helical peptide into a drug-like molecule with enhanced proteolytic resistance, cell permeability, and target affinity. Our stapled peptide design platform combines molecular dynamics, free energy analysis, and conformational sampling with targeted in vitro validation to deliver stapled constructs with optimized helix stability, binding energetics, and developability profiles — from initial staple placement to candidate selection.
We systematically evaluate all possible i, i+4 and i, i+7 stapling pairs using helix-coil theory, MD-derived helical content, and steric compatibility scoring to identify positions that maximize helix stabilization without interfering with target binding.
We model different staple chemistries — all-hydrocarbon (S5, R8), triazole, lactam, and thioether — and evaluate linker length, stereochemistry, and flexibility to balance helix stabilization with synthetic accessibility and metabolic stability.
Using MM/PBSA, MM/GBSA, and where needed FEP, we compute the binding free energy of each stapled analog relative to the linear peptide, identifying constructs that maintain or improve target affinity while gaining stability.
Extended molecular dynamics simulations in explicit solvent assess the conformational stability of stapled peptides, local unfolding propensity, and exposure of protease cleavage sites, providing a computational proxy for serum stability.
We estimate passive membrane permeability using physicochemical descriptors, intramolecular hydrogen bond counts, and 3D polar surface area, enabling early triage of stapled peptides with better chances of cell uptake and oral bioavailability.
We work with your team or our partners to validate top computational candidates using circular dichroism, trypsin digestion, surface plasmon resonance, and cell-based assays — closing the loop between prediction and experiment.
We begin with your peptide sequence and target structure (or model). If the peptide is a known binder, we map the binding interface, identify hot-spot residues, and determine the helical register. For de novo designs, we first establish a validated binding pose.
All possible i, i+4 and i, i+7 stapling positions are evaluated for steric compatibility with the target, impact on key binding residues, and helix stabilization potential. Positions that clash with the binding interface or critical side chains are eliminated early.
For each viable staple position, we model all-hydrocarbon, triazole, lactam, and other linkers. We evaluate bridge geometry, stereochemistry, and predicted helicity to select the top 3–5 candidate staples for detailed simulation.
Explicit-solvent MD simulations (100–500 ns) are performed for each top candidate to assess helical content, staple dynamics, local unfolding, and conformational stability. We compare stapled vs. linear peptide behavior.
We compute per-residue energy decomposition and relative binding free energies (MM/PBSA, MM/GBSA) to confirm that stapling does not disrupt critical interactions and to rank analogs by predicted affinity retention or improvement.
Stapled analogs are profiled for 3D polar surface area, intramolecular hydrogen bonds, and predicted passive permeability. We rank candidates by a composite developability score to guide synthesis prioritization.
We provide a prioritized list of 3–10 stapled peptides for synthesis. CD, protease stability, and binding assays are used to validate predictions. The data feeds back into our models for iterative optimization.
| Design Challenge | Recommended Computational Pipeline | Key Outputs | Wet-Lab Confirmation |
|---|---|---|---|
| Improve helicity of a linear peptide without losing binding | Interface mapping + i,i+4/i,i+7 stapling scan + MD helicity assessment | Staple position ranking, helical content, binding site clash report | CD spectroscopy, SPR binding |
| Identify which staple chemistry best stabilizes the helix | Linker enumeration (all-hydrocarbon, triazole, lactam) + MD + free energy analysis | Linker comparison table, ΔG binding, helix persistence | CD, trypsin digestion |
| Reduce proteolytic degradation while keeping affinity | MD-based cleavage site exposure + stapled analog simulation | Predicted serum stability ranking, cleavage site masking score | Serum stability assay, LC-MS |
| Enhance cell permeability of a stapled peptide | 3D-PSA, intramolecular H-bond analysis + stapling scan | Permeability score, analog ranking | Cell uptake assay (flow cytometry) |
| Design stapled peptide mimetics of a helical interface | Hot-spot identification + stapling scan + FEP for key analogs | Prioritized designs with predicted ΔΔG and stability | SPR, ITC, functional assays |
| Rescue a peptide that aggregates or unfolds | MD unfolding analysis + stapling scan at flexible regions | Stabilizing staple positions, predicted helical content | CD, DLS, activity assay |
Design stapled helical peptides that mimic the p53 transactivation domain, with improved helicity and resistance to MDM2-mediated degradation. We optimize staple placement to maintain key hydrophobic contacts (F19, W23, L26) while enhancing cell permeability.
Stabilize helical regions of viral fusion proteins or host receptors to create protease-resistant entry inhibitors. We model stapled peptides targeting SARS-CoV-2 HR1, HIV gp41, and influenza HA.
Design stapled peptides that mimic BH3-only proteins to modulate apoptosis. We optimize stapling to maintain helical conformation and binding to anti-apoptotic BCL-2 proteins while improving pharmacokinetic properties.
Target helical interfaces in tau, α-synuclein, or APP processing. Stapling can enforce helical conformation, reduce aggregation propensity, and improve blood–brain barrier penetration potential.
Stabilize helical peptide ligands for class B GPCRs (GLP-1, GIP, PTH) to improve half-life and receptor selectivity. We model stapled analogs with enhanced proteolytic resistance.
Design azobenzene- or DTE-based staples that allow light-controlled helix formation. We model both cis and trans states and predict the impact on target binding.
Stapled peptide synthesis is resource-intensive: each analog requires solid-phase chemistry, purification, and characterization. Random or exhaustive stapling scans are impractical. Our computational workflow narrows the design space from dozens of possible staple positions and linker chemistries to a prioritized shortlist of 3–10 analogs with the highest predicted helicity, stability, and binding retention — dramatically reducing synthesis burden and accelerating lead discovery.
Objective: design 5 stapled p53 peptides with ≥80% helicity, retained MDM2 binding (Kd < 50 nM), and improved serum stability.
Objective: identify staple positions that increase helical content and proteolytic resistance without affecting GLP-1 receptor activation.
Objective: design an azobenzene-stapled peptide that reversibly controls helix formation and target binding with light.
We begin by mapping the peptide's helical interface with the target and identifying hot-spot residues that must remain unmodified. We then enumerate all possible i, i+4 and i, i+7 pairs, excluding those that sterically clash with the target or involve critical binding residues. Each remaining position is scored for helix stabilization potential using MD-derived distance distributions and helix propensity scales.
Yes. We have parameterized and validated models for all-hydrocarbon staples (S5, R8, etc.), 1,2,3-triazole staples, lactam bridges, thioether linkers, and photoswitchable azobenzene staples. We also evaluate custom linker chemistries on request.
Our MD-based helicity predictions typically correlate with experimental CD data with R² values of 0.7–0.85 for well-behaved systems. Proteolytic stability predictions are more qualitative but effectively rank analogs by predicted serum half-life. We always recommend experimental validation of top candidates.
We focus on computational design and modeling, but we collaborate with peptide synthesis partners and can coordinate the synthesis and purification of your prioritized analogs. We also work with your existing CRO or academic synthesis facility.
Excellent — we incorporate your existing CD, SPR, or stability data to calibrate our models and improve prediction accuracy for subsequent designs. This iterative loop significantly enhances the reliability of our recommendations.
Yes, but it is a more involved project. We would first identify a helical interface or epitope, design a linear peptide mimetic using hot-spot grafting or computational design, and then apply our stapling optimization workflow. Success rates for de novo designs are lower than for optimizing known binders, and we scope these projects carefully.
Share your peptide sequence and target structure. We will design a computationally guided stapling campaign that identifies the optimal staple positions, linker chemistry, and analog shortlist — so you synthesize only the best candidates.
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