De novo design of small-molecule–induced conformational change 1. Chang & Polizzi present a general, de novo strategy to turn a static small-molecule binder into a ligand-induced conformational switch by adding a mobile “lid” domain that closes behind the ligand, inspired by natural lid mechanisms (e.g., streptavidin, Abl kinase, GPCR loops). 2. Starting scaffold: EPIC, a hyperstable 17 kDa de novo exatecan binder (Tm > 95 °C, Kd ~1.2 nM). Key design opportunity: ~50 Å2 of exatecan remains solvent-exposed in EPIC, including a charged amine that can be “captured” by a designed lid. 3. Core design concept: preserve preorganization in the original binding core (fast association) while introducing induced-fit behavior through a tethered lid. The lid must (i) remain open enough for ligand entry (negative design) and (ii) become stabilized in a closed register only when ligand is present. 4. Computational pipeline: QBITS (query-based interfacial tertiary structures) mines the PDB for recurring helical interface geometries matching a two-helix query near the ligand-exposed region; RFdiffusion extends candidate lid placements; a flexible (GGS)n linker (n=4–7) tethers lid to binder; LASErMPNN designs lid and lid–binder interface residues. 5. Key screening innovation: AlphaFold3 is used in a paired bound/unbound ranking scheme. Designs are prioritized where AF3 predicts confident lid placement only in the ligand-bound state (low interfacial predicted aligned error, iPAE) but is uncertain in the apo state (high iPAE), using ΔiPAE as a proxy for ligand-induced closure. 6. Experimental throughput: only 24 designs were built and tested; all expressed well in E. coli and retained exatecan binding (Kd < 1 µM). Lid closure was detected via (a) fluorescein-on-lid anisotropy shifts and (b) ligand-dependent protection from tryptic cleavage (limited proteolysis as a sensitive closure readout). 7. Lead designs d6.4 and d6.9 achieved sub-nanomolar affinity via lid closure: Kd ~200 pM (d6.4) and ~510 pM (d6.9). The lid adds favorable lid–ligand energetics (estimated ΔΔG contributions of ~−3.2 and −2.3 kcal/mol) and fully buries exatecan (0 Å2 exposed SASA), enabling recognition of essentially the entire ligand surface. 8. Selectivity jump: compared to EPIC, lidded designs strongly discriminate exatecan from the close analog belotecan. EPIC binds belotecan at ~290 nM, while lidded proteins bind belotecan very weakly (Kd > 10 µM), yielding >1:20,000 specificity and >100-fold improvement in specificity ratio vs the parent binder. 9. Structural validation: crystal structures of d6.4:exatecan and d6.9:exatecan closely match AF3 predictions (Cα RMSD ~0.3–0.4 Å). Designed features are observed, including a resolved salt bridge between exatecan’s amine and a designed Glu (Glu41) that helps lock lid register upon binding. 10. Kinetics engineering + biosensing: lid occlusion yields tunable kon/koff and very long residence times (inferred koff down to ~2×10^-7 s^-1; ~months for d6.4). Kinetics are rationally tuned by (i) a single interface mutation (M93E) that opens the apo ensemble and speeds kon ~30-fold while retaining very tight binding, and (ii) lid truncations (via circular permutation) that systematically increase kon. A circularly permuted/truncated variant (d6.4cpΔ11) was converted into a genetically encodable FRET sensor (mTurquoise2–binder–mVenus) with low-nanomolar apparent Kd (~1.5 nM) and robust function in saliva, urine, and serum. 💻Code: github.com/npolizzi/Qbits/re… 📜Paper: biorxiv.org/content/10.64898… #ProteinDesign #DeNovoProteinDesign #ComputationalBiology #StructuralBiology #Biosensors #DrugDiscovery #AlphaFold3 #RFDiffusion #FRET #Kinetics

Zero-shot design of drug-binding proteins via neural iterative selection−expansion 1. The work introduces NISE (neural iterative selection–expansion), a closed-loop algorithm that jointly optimizes protein sequence, protein structure, and ligand conformation—enabling true zero-shot design of small-molecule binders where prior deep-learning approaches struggled. 2. NISE alternates between two reciprocal neural conditionals: LASErMPNN samples sequences conditioned on a protein–ligand co-structure, and a co-structure predictor (RoseTTAFold-All Atom or Boltz-2) predicts the 3D protein–ligand complex from sequence + ligand identity; designs are selected by tripartite self-consistency (backbone and ligand r.m.s.d.) plus ligand confidence. 3. A key conceptual shift is treating “self-consistency” as protein–ligand self-consistency (not just foldability): many sequences can be backbone self-consistent, but far fewer place the ligand in the intended orientation; ligand self-consistency becomes a stringent computational screen for productive binding. 4. LASErMPNN is a ligand-aware heterograph message-passing model trained on PDB co-crystal structures to decode both amino-acid identity and side-chain dihedrals; it includes a pretrained ligand encoder trained on quantum-derived atom properties (for example partial charges), improving generalization to new ligands and reducing design pathologies like overpacking. 5. On exatecan (a camptothecin payload with a hydrolysis-prone lactone), NISE redesigned a four-helix bundle binder from scratch starting only from backbone + docked ligand coordinates; all 4 experimentally tested NISE designs bound exatecan (100% hit rate), with the best binder EPIC at Kd = 120 nM. 6. Against a traditional COMBS + Rosetta pipeline (16 tested designs), only 3 bound exatecan and the best was ~70-fold weaker than EPIC; analysis suggests NISE’s advantage comes from iterative “resculpting” of backbone/packing and ligand placement, not just filtering with a structure predictor. 7. The authors then perform purely in silico affinity maturation (“neural proofreading”): LASErMPNN proposes single-site substitutions that reduce sequence NLL in the bound context; two substitutions (Q51N and M97L) each improve affinity >10×, and the double mutant improves EPIC ~100× to Kd = 1.2 nM without experimental feedback. 8. X-ray structures of EPIC (2.0 Å) and EPIC(Q51N) (2.2 Å) validate the designed binding mode and explain the affinity gain: Asn51 enables deeper burial and bidentate H-bonding to the hydroxyl and lactone carbonyl, with only ~sub-Å deviations from intended placement in the pocket core. 9. Functionally, EPIC variants protect exatecan’s lactone from hydrolysis: EPIC(Q51N/M97L) keeps >99% of exatecan in the ring-closed (bioactive) form for at least 50 hours in PBS pH 7.4, and protection persists even with high human serum albumin present—supporting drug-stabilizing delivery/sponge applications. 10. Generality is shown on apixaban using NTF2 scaffolds and Boltz-2 within NISE: 5 of 6 tested designs bound tightly (83% success), with the best (APEX) reaching Kd = 80 pM—nearly 10,000-fold tighter than prior LigandMPNN/Rosetta apixaban binder design reports on the same backbone family, and approaching the native target factor Xa affinity. 💻Code: github.com/polizzilab/LASErM… ; github.com/polizzilab/NISE ; github.com/benf549/CARPdock 📜Paper: doi.org/10.1038/s41586-026-1… #ProteinDesign #ComputationalBiology #DeepLearning #StructuralBiology #DrugDiscovery #GenerativeModels #ProteinLigand #DeNovoDesign #GNN #Boltz2 #RoseTTAFoldAllAtom