A hybrid physics-deep learning framework for combinatorial de novo design of small-molecule binding proteins 1. The paper introduces CLAIRE (CombinatoriaL Assembly with Integrated REfinement), a hybrid workflow that aims to make de novo small-molecule binder design more reliable by combining explicit physics-based interaction scaffolding with deep-learning-guided sequence/structure optimization. 2. Core idea: instead of asking generative models to “discover” atom-level protein–ligand hydrogen bonding after the fact, CLAIRE defines high-fidelity interaction motifs up front and then searches for backbones that can accommodate those motifs with tight geometric tolerances (distances within ~0.5 Å; angles/torsions within ~10–15°). 3. Motif innovation: the authors extend BSFF (Binding Sites from Fragments) by mining the PDB for residue–fragment interactions, spatially clustering them into discrete “interaction modes,” and preferentially using statistically overrepresented modes (not necessarily those best-scoring by Rosetta), capturing preferences like pi-stacking and cation–pi that energy functions may underweight. 4. Scaffold innovation: the authors generalize motif scaffolding beyond helical bundles by using LUCS to generate thousands of reshaped de novo NTF2-like alpha–beta scaffolds with finely varied pocket geometries, mimicking how nature reuses folds by subtle geometric shifts around functional sites. 5. Combinatorial matching: motifs and scaffolds are screened at scale using Rosetta Match; buried ligand placements are kept (≤30% ligand SASA exposed), yielding high matching throughput (reported as >160 buried matches per input motif across five diverse small molecules), enabling large libraries of candidate complexes. 6. Refinement step 1 (physics, targeted): HBRefine is introduced to fix a common failure mode in small-molecule binder design—buried unsatisfied polar atoms. It (a) mutates extraneous buried polar residues to hydrophobics when favorable and (b) proposes local mutations to create new H-bonds to any unsatisfied ligand polar atoms, then repacks and accepts changes if energetically non-worse. 7. Refinement step 2 (ML physics): ProteinMPNN redesigns residues outside the binding site to restore global sequence–structure compatibility after pocket remodeling; Rosetta FastDesign then redesigns using MPNN-derived profiles, followed by filtering for both binding metrics (e.g., interface H-bonds, shape complementarity, ddG) and stability metrics (e.g., packstat, exposed hydrophobics, global polar satisfaction). 8. Quantitative takeaway (in silico): HBRefine plus ProteinMPNN increases the fraction of designs passing stringent multi-metric filters by up to ~7-fold. When compared to RosettaFold Diffusion all-atom pipelines on progesterone/estriol, CLAIRE yields higher in-silico pass rates; the diffusion designs most often fail on ligand H-bond satisfaction and interface buried unsatisfied polar atoms. 9. Experimental validation on two similar steroids: 26 designs (13 estriol, 13 progesterone) were tested. All expressed solubly; ~58% were monomeric by SEC; 31% were well-folded by 15N-HSQC. Binding by NMR chemical shift perturbations was observed for 1 estriol design and 3 progesterone designs, i.e., 4/26 binders overall (notably higher than typical sub-1% reports for fully generative workflows). 10. Structural and mechanistic support: NMR structures for A1E (apo/holo characterization) and D2P (holo) agree well with models (non-loop Cα RMSDs ~1.5 Å vs AF2). Motif-residue point mutations (e.g., A1E N43V/S45V/Y85F; D2P Y14F/T98V) reduce binding signals, supporting that the designed polar contacts are functionally important. Designed binding modes differ from human estrogen receptor binding solutions and show higher interaction density, indicating novelty rather than copying natural motifs. 💻Code: github.com/cvgalvin/CLAIRE 📜Paper: biorxiv.org/content/10.64898… #ProteinDesign #ComputationalBiology #Rosetta #ProteinMPNN #AlphaFold2 #NMR #DeNovoDesign #SmallMoleculeBinding #HybridModels #StructuralBiology

Design of facilitated dissociation enables timing of cytokine signalling | @Nature - Design a general protein strategy that controls dissociation kinetics of protein complexes by enabling facilitated dissociation through effector-responsive conformational switches - Construct switch–binder fusions by first sketching placements in PyMOL (no clash in state X, strong clash in state Y). Generate fusion backbones with RosettaFold inpainting (early designs) or Rosetta FastDesign RFdiffusion (later designs), masking noncritical residues at fusion interfaces. Optimize sequences using ProteinMPNN for both switch and binder regions - Predict structural states with AlphaFold2 (AF2, AF2-IG) for host alone, host target, and host effector. Model strained ternary intermediates with AF2-IG to ensure diverse deformation directions and favorable strain distribution - Demonstrate up to 5,700-fold acceleration of dissociation by SPR, confirm designed clashes and conformational states with X-ray crystallography, and show rapid, tunable control in applications Link: nature.com/articles/s41586-0…

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Targeting protein–ligand neosurfaces with a generalizable deep learning tool | @Nature - MaSIF-neosurf can design binders for protein-ligand complexes, targeting neosurfaces (i.e., ligand-induced structural changes on the protein surface) - Benchmark MaSIF-neosurf against RFAA on 14 ligand-induced PPI complexes with 8,907 decoys from PDBBind - Use MaSIF-search to predict buried surfaces and identify complementary surface fingerprints from a database of protein fragments (~640,000) - Construct full-length proteins with binding motifs and refining structures using the Rosetta FastDesign protocol and grafting (with a potential round of LigandMPNN optimization) - Engineer and validate binders for Bcl2–venetoclax, DB3–progesterone, and PDF1–actinonin through experimental testing Link: nature.com/articles/s41586-0…

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De novo design of miniprotein antagonists of cytokine storm inducers | @NatureComms - Use Rosetta-based binder design approach (Cao, et al., 2024) against IL-6R, GP130, and IL-1R1 - Dock 40k de novo protein scaffolds to hotspot residues (Patchdock and Rifdock), and design 2.5 million backbones to sequences (Rosetta Fastdesign) - Filter top designs based on Rosetta metrics and screen the best 100k candidates using yeast display Link: nature.com/articles/s41467-0…

Everyone studying molecular biology should track how other researchers connect AI models into relevant workflows. Here's an example for protein assembly design: De novo design of allosterically switchable protein assemblies nature.com/articles/s41586-0… Models: ProteinMPNN, RFDiffusion, AlphaFold2 -multimer, HFuse, Rosetta FastDesign, WORMS, RPXDock46

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Foldit standalone上でRosettaScriptを使ってFastDesignしたgif。movemapでbbをFalseにしているので、バックボーンを固定してロータマー探索している動画になっているはず。

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Pipeline updated! ProteinMPNN is great helpful in providing better design after FastDesign, thank all contributors!

FastDesign（FastRelax）を使う場合は、オプションで渡すことができるresfileでNATAAにして、かつ、MoveMapのbbを0に指定すると、側鎖を変えずに側鎖のrepackだけ行われて主鎖は動かないと思います。複数のchainがあって、その主鎖の相対的な位置を動かさずにするには、MoveMapでjumpを0にします。