Janette Gunter @Calypso199 - Twitter Profile

EasyNano: rapid epitope-targeted nanobody CDR design via differentiable distogram optimization with ESMFold2 1 EasyNano is a rapid pipeline for epitope-targeted nanobody CDR redesign that runs in ~10–20 minutes per target on a high-end personal workstation, aiming to make “design-to-candidate” iteration practical without GPU clusters. 2 The core idea is to optimize CDR residue logits by gradient descent through the ESMFold2-Fast distogram (a differentiable proxy), rather than trying to directly optimize ipTM (expensive and not practical as an inner-loop objective). 3 EasyNano introduces an explicit epitope-targeting objective: a dedicated CDR-to-epitope proximity loss (ELU penalty when expected CDR→epitope distance exceeds 8 Å), enabling user-specified epitope steering instead of “bind anywhere”. 4 To prevent framework pose drift during optimization, EasyNano computes a structure prior using full ESMFold2 (1.3B) on the WT framework–target complex, then constrains optimization with a CA-coordinate distogram mask prior; this anchoring is critical for stable epitope-focused design. 5 The method uses a three-stage workflow: (i) full-model structure prior (~30 s), (ii) differentiable CDR optimization with ESMFold2-Fast (~10–17 min; 60 steps; Adam; cosine temperature schedule), (iii) full ESMFold2 evaluation (~15 s per candidate) to obtain calibrated ipTM/pTM for ranking. 6 A practical insight from systematic sweeps: the wild-type logit initialization bias (β) is the key hyperparameter controlling CDR mutability. Too high (β≥5) freezes CDRs; too low (β≤1) causes chaotic drift. β≈2 (with moderate prior weight) enables meaningful, stable mutation. 7 On weak binders, EasyNano can yield large ipTM gains: Ty1/RBD improved from 0.143 to 0.702 (+0.559; 5.7σ above random CDR baseline), with 11/22 CDR mutations and reduced CDR→epitope distance (16.6 Å → 10.7 Å). 8 It also improves clinically relevant cases while respecting constraints: KN035/PD-L1 increased ipTM 0.251 → 0.459 (+0.208; 2.2σ), introducing 7/32 mutations while preserving the H3 disulfide, consistent with constrained-but-targeted optimization. 9 On already-strong binders (e.g., VHH72/RBD and VHH3/TNFα), EasyNano largely preserves ipTM (small ∆), suggesting the approach does not necessarily degrade optimized interfaces when headroom is limited. 10 De novo scenario: starting from a manually docked non-cognate framework near AQP4 loop C, CDR-only design improved ipTM 0.117 → 0.538 (4.6-fold). Multi-seed runs revealed distinct local minima; a single framework micro-tuning mutation (W116Y) stabilized the high-ipTM basin, highlighting a practical interplay between pose basins and CDR optimization. 💻Code: https://t.co/km2GymtG7J 📜Paper: https://t.co/wvTmuMvwVz #Nanobody #ProteinDesign #AntibodyEngineering #ComputationalBiology #ESMFold #DeepLearning #DifferentiableOptimization #EpitopeTargeting #Bioinformatics

BiologyAIDaily's tweet photo. EasyNano: rapid epitope-targeted nanobody CDR design via differentiable distogram optimization with ESMFold2

1 EasyNano is a rapid pipeline for epitope-targeted nanobody CDR redesign that runs in ~10–20 minutes per target on a high-end personal workstation, aiming to make “design-to-candidate” iteration practical without GPU clusters.

2 The core idea is to optimize CDR residue logits by gradient descent through the ESMFold2-Fast distogram (a differentiable proxy), rather than trying to directly optimize ipTM (expensive and not practical as an inner-loop objective).

3 EasyNano introduces an explicit epitope-targeting objective: a dedicated CDR-to-epitope proximity loss (ELU penalty when expected CDR→epitope distance exceeds 8 Å), enabling user-specified epitope steering instead of “bind anywhere”.

4 To prevent framework pose drift during optimization, EasyNano computes a structure prior using full ESMFold2 (1.3B) on the WT framework–target complex, then constrains optimization with a CA-coordinate distogram mask prior; this anchoring is critical for stable epitope-focused design.

5 The method uses a three-stage workflow: (i) full-model structure prior (~30 s), (ii) differentiable CDR optimization with ESMFold2-Fast (~10–17 min; 60 steps; Adam; cosine temperature schedule), (iii) full ESMFold2 evaluation (~15 s per candidate) to obtain calibrated ipTM/pTM for ranking.

6 A practical insight from systematic sweeps: the wild-type logit initialization bias (β) is the key hyperparameter controlling CDR mutability. Too high (β≥5) freezes CDRs; too low (β≤1) causes chaotic drift. β≈2 (with moderate prior weight) enables meaningful, stable mutation.

7 On weak binders, EasyNano can yield large ipTM gains: Ty1/RBD improved from 0.143 to 0.702 (+0.559; 5.7σ above random CDR baseline), with 11/22 CDR mutations and reduced CDR→epitope distance (16.6 Å → 10.7 Å).

8 It also improves clinically relevant cases while respecting constraints: KN035/PD-L1 increased ipTM 0.251 → 0.459 (+0.208; 2.2σ), introducing 7/32 mutations while preserving the H3 disulfide, consistent with constrained-but-targeted optimization.

9 On already-strong binders (e.g., VHH72/RBD and VHH3/TNFα), EasyNano largely preserves ipTM (small ∆), suggesting the approach does not necessarily degrade optimized interfaces when headroom is limited.

10 De novo scenario: starting from a manually docked non-cognate framework near AQP4 loop C, CDR-only design improved ipTM 0.117 → 0.538 (4.6-fold). Multi-seed runs revealed distinct local minima; a single framework micro-tuning mutation (W116Y) stabilized the high-ipTM basin, highlighting a practical interplay between pose basins and CDR optimization.

💻Code: https://t.co/km2GymtG7J
📜Paper: https://t.co/wvTmuMvwVz
#Nanobody #ProteinDesign #AntibodyEngineering #ComputationalBiology #ESMFold #DeepLearning #DifferentiableOptimization #EpitopeTargeting #Bioinformatics

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Janette Gunter

@Calypso199

4 months ago

@w_terrence Hell no! If he works for the money, it’s his. After all, think about the first millionaire and the first billionaire… their achievements were celebrated.

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