CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization 1. CreoPep is a generative deep learning platform that enables target-specific peptide design by combining masked language modeling with progressive masking and energy-based screening—offering a scalable alternative to traditional mutagenesis approaches. 2. The framework focuses on conotoxins—peptides from cone snail venom known for their high affinity to ion channels like α7 nAChRs—and achieves submicromolar potency in designed variants, confirmed via electrophysiological assays. 3. CreoPep introduces a Progressive Masking (PM) strategy that gradually increases noise during training and denoises step-by-step during generation, improving contextual learning of sequence-function relationships beyond static masking schemes. 4. The model integrates multiple objectives—label prediction, conditional generation, and optimization—and uses subtype/potency annotations and auxiliary peptide inputs to guide design, allowing functional specificity without structural input. 5. CreoPep incorporates an energy-guided data augmentation pipeline powered by FoldX. Iterative ∆∆G screening of generated peptides filters high-affinity variants, producing an 8.8x expansion of the initial conotoxin dataset and enhancing diversity and performance. 6. With temperature-controlled multinomial sampling, CreoPep balances the generation of potent peptides with exploration of diverse sequences. Model outputs retain essential features like net charge, hydropathy, and disulfide patterns while accessing new chemical space. 7. Structural modeling via AlphaFold3 and FoldX confirms that top candidates like CP\_α7\_1 and CP\_α7\_6 bind the α7 nAChR with unique modes—one preserving canonical disulfide bridges, the other succeeding despite lacking them. 8. Electrophysiological validation on hα7 nAChRs showed that 7 out of 13 CreoPep-designed peptides achieved >50% inhibition at 10 μM, and two (CP\_α7\_1 and CP\_α7\_6) reached submicromolar IC50s (\~400–500 nM). 9. Sequence and structural analysis identified both conserved residues essential for activity (e.g. R7, W10, R11) and mutable positions that tolerate optimization, validating CreoPep’s capacity to extract functional design rules. 10. CreoPep offers a generalizable peptide engineering platform with potential applications across synthetic biology, drug discovery, and personalized therapeutics. Future directions include non-natural amino acid incorporation and multitarget design. 💻Code: github.com/gc-js/CreoPep 📜Paper: arxiv.org/abs/2505.02887 #PeptideDesign #DeepLearning #Conotoxin #Therapeutics #MaskedLanguageModel #DrugDiscovery #CreoPep #ProteinDesign #Bioinformatics #TargetSpecificDesign

CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization 1. CreoPep is a generative deep learning platform that enables target-specific peptide design by combining masked language modeling with progressive masking and energy-based screening—offering a scalable alternative to traditional mutagenesis approaches. 2. The framework focuses on conotoxins—peptides from cone snail venom known for their high affinity to ion channels like α7 nAChRs—and achieves submicromolar potency in designed variants, confirmed via electrophysiological assays. 3. CreoPep introduces a Progressive Masking (PM) strategy that gradually increases noise during training and denoises step-by-step during generation, improving contextual learning of sequence-function relationships beyond static masking schemes. 4. The model integrates multiple objectives—label prediction, conditional generation, and optimization—and uses subtype/potency annotations and auxiliary peptide inputs to guide design, allowing functional specificity without structural input. 5. CreoPep incorporates an energy-guided data augmentation pipeline powered by FoldX. Iterative ∆∆G screening of generated peptides filters high-affinity variants, producing an 8.8x expansion of the initial conotoxin dataset and enhancing diversity and performance. 6. With temperature-controlled multinomial sampling, CreoPep balances the generation of potent peptides with exploration of diverse sequences. Model outputs retain essential features like net charge, hydropathy, and disulfide patterns while accessing new chemical space. 7. Structural modeling via AlphaFold3 and FoldX confirms that top candidates like CP\_α7\_1 and CP\_α7\_6 bind the α7 nAChR with unique modes—one preserving canonical disulfide bridges, the other succeeding despite lacking them. 8. Electrophysiological validation on hα7 nAChRs showed that 7 out of 13 CreoPep-designed peptides achieved >50% inhibition at 10 μM, and two (CP\_α7\_1 and CP\_α7\_6) reached submicromolar IC50s (\~400–500 nM). 9. Sequence and structural analysis identified both conserved residues essential for activity (e.g. R7, W10, R11) and mutable positions that tolerate optimization, validating CreoPep’s capacity to extract functional design rules. 10. CreoPep offers a generalizable peptide engineering platform with potential applications across synthetic biology, drug discovery, and personalized therapeutics. Future directions include non-natural amino acid incorporation and multitarget design. 💻Code: github.com/gc-js/CreoPep 📜Paper: arxiv.org/abs/2505.02887 #PeptideDesign #DeepLearning #Conotoxin #Therapeutics #MaskedLanguageModel #DrugDiscovery #CreoPep #ProteinDesign #Bioinformatics #TargetSpecificDesign