🔬 Curated peptide research across design, computation, synthesis, biology, delivery, biomaterials, and therapeutic applications.
🤖 With help from Codex agents, paper discovery, metadata verification, classification, and validation are partially automated. Papers are curated from paper-read, Crossref, PubMed, arXiv, and primary sources; committed metadata snapshots make README generation reproducible offline.
🔗 Link directly to Contents, Citations
✅ What sets us apart from similar resources:
- Versatile Tags: Organize and filter papers easily.
- Easy Navigation: Internal links for quick jumps between sections and papers.
- Expert Insights: Links to expert reviews and analysis.
- Tag System: Quickly catch the paper features
- CSV Downloads: curated papers and paper-read papers.
- Automation: Use Liquid templates to generate Markdown from
CSV, making it easy to build your own paper repository. >>> [Details]
📅 Papers from the last six months, updated on 2026-10-03:
A proximity-driven and regioselective peptide bicyclization (PReP-Bicyc) approach for phage display libraries
Guoqing Jin, Yifan Shi, Shihui Fan, Lai Hoang Son Le, Wenyue Cao, Thuzar Hla Shwe, Zihan Anna Zhang, Demonta D. Coleman, Satyanarayana Nyalata, Joshua Trae Hampton and Wenshe Ray Liu
[2026-10-3] >> Nat Commun • Cyclization/Bicyclic/Phage Display
🔎 Abstract
PReP-Bicyc enables mild, phage-compatible, regioselective peptide bicyclization and screening of PD-1-binding ligands. Published as an early peer-reviewed article.
Self-Assembling Peptide-Adjuvant Conjugate (SaPAC) Platform for Precision Cancer Immunotherapy
Yang-Fan Wu, Jing-Chu Hu, Ye-Fan Hu, Wen-Jun Li, Li Rong, Ren-Hao Li, Yuan Yao, Lehan Hu, Xiao-Lei Wang, Bao-Zhong Zhang, Yicheng Lu, Shing-Fung Chow, Canhui Su, Thomas Yau, Clive Yik-Sham Chung, Jian-Dong Huang
[2026-10-2] >> Adv Sci (Weinh) • PubMed • Bioconjugation/Self-Assembly/Immunology/Oncology
🔎 Abstract
The SaPAC peptide-adjuvant conjugate platform forms nanoparticles that enhance antigen presentation and antitumor immune responses in mouse cancer models.
Computationally Guided Engineering of Short Peptides for Targeted RNA-Loaded pBAE Nanoparticles
Vladimir Stamenković, Mislav Brajković, Coral Garcia-Fernandez, Salvador Borrós, Xevi Biarnés, Cristina Fornaguera
[2026-10-2] >> ACS Biomater Sci Eng • PubMed • Delivery/Bioconjugation/mRNA
🔎 Abstract
Computationally selected short targeting peptides are conjugated to RNA-loaded polymeric nanoparticles; peptide orientation and sequence are evaluated for transfection in a human monocytic cell model.
Signaling by a tyrosine-sulfated peptide balances growth and osmotic stress response in rice
Yejin Shim, Ellen Y Rim, Jin C-Y Liao, Myeong-Je Cho, George Austin, Patrick W Carlos, Sameer S Kulkarni, Richard J Payne, Maria Florencia Ercoli, Pamela C Ronald
[2026-10-1] >> Proc Natl Acad Sci U S A • PubMed • Plant Peptides
🔎 Abstract
Genetic and transcriptomic experiments identify rice OsPSY8 sulfated-peptide signaling as a regulator of the balance between root growth and osmotic-stress adaptation.
Multi-Scale Temporal Flows for Peptide Trajectory Generation
Xichen Sun, Wentao Wei, Xiaoxi Zhang, Yuedong Yang, Jiahua Rao
[2026-10-01] >> arXiv • Flow/MD/Cyclic
🔎 Abstract
Preprint: PepTIDE models peptide trajectories using multi-scale temporal sampling and physical-time embeddings, evaluating distribution agreement, structural validity and inter-state transitions.
Silver(I)-mediated amide-to-ester transformation on unprotected peptides for protein chemical synthesis and engineering
Zhenquan Sun, Wai Yin Yau, Huajie Kang, Haiyan Zhou, Xin Liu, Zhibo Han, Hongxiang Wu, Xuechen Li
[2026-9-30] >> Sci Adv • PubMed • Ligation
🔎 Abstract
Chemoselective silver-mediated backbone activation converts unprotected peptide amides to salicylaldehyde esters for protein synthesis, demonstrated with mirror-image histones.
Positive modulation of glucagon-like peptide 1 (GLP-1) receptor by endogenous haemorphins with implications in glucose metabolism and diabetes
Farheen Badrealam Khan, Rwdah Mohamed Alameri, Yasir S Raouf, Damien Maurel, Naiem Ahmad Wani, Anatoliy Shmygol, Irfa Anwar, Heng B See, Elizabeth K M Johnstone, Elodie Dupuis, Eric Trinquet, Kevin D G Pfleger, Emilia Oueis, Mohammed Akli Ayoub
[2026-9-29] >> Br J Pharmacol • PubMed • Binding/Metabolism
🔎 Abstract
Endogenous LVV-haemorphin 7 modulates GLP-1 receptor signaling in cellular models, with functional assays and computational binding analysis supporting a peptide-dependent mechanism.
NMR-Guided Fragment Screening Identifies Privileged Noncanonical Amino Acids for mRNA Display
Abdul J Castillo, Clark A Jones, Alba C Dutra, Chelsea A Makovsky, Sandeep Lohan, Bipasana Shakya, Sara H Walters, Brian Fuglestad, Matthew C T Hartman
[2026-9-28] >> Chembiochem • PubMed • NMR/Screening/Noncanonical/mRNA
🔎 Abstract
NMR-guided fragment screening identifies noncanonical tryptophan monomers for mRNA display and tests their translation compatibility and improved MDM2-binding enrichment.
Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design
Jiada Li
[2026-09-28] >> arXiv • GitHub • Binding/Benchmark/Noncanonical
🔎 Abstract
Preprint: Mirror-Score evaluates inference-only ranking of D-peptide/L-protein complexes and demonstrates the limited affinity validity and cross-family transfer of sequence-compatibility scores.
Antimicrobial peptide databases: A detailed comparison and complementary features
Erfan Zamani, Mohammadhossein Ghazizadeh-Ahsaei, Yasaman Mahmoodi, Faramarz Mehrnejad
[2026-9-25] >> Peptides • PubMed • AMPs/Benchmark
🔎 Abstract
Compares APD, CAMP, DBAASP, dbAMP and DRAMP for annotation, activity evidence, usability and computational workflows, identifying complementary strengths and interoperability needs.
Peptide-mediated CRISPR delivery: From dish to bloodstream
Alzbeta Ressnerova, Ross C Wilson
[2026-9-24] >> Curr Opin Chem Biol • PubMed • Delivery/CPPs
🔎 Abstract
Reviews peptide-mediated CRISPR delivery from cell culture to local and systemic in vivo applications, including clearance, endosomal escape and formulation barriers.
GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing
Abdellah El Mekki, Laks V. S. Lakshmanan, Muhammad Abdul-Mageed
[2026-09-24] >> arXiv • MS/AI/Proteomics
🔎 Abstract
Preprint: GyroNovo combines decoder-error-guided fragment imputation with mass-aware attention for de novo peptide sequencing from tandem mass spectra.
Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases
Emre F Bülbül, Seounggun Bang, Kevin George, Gabriele Bianchi, Prateek Raj, Seonyong Chung, Vincent Pauline, Ramon Hochstrasser, Hannah A Minas, Walid A M Elgaher, Andreas M Kany, Anna K H Hirsch, Steven Schmitt, Dirk W Heinz, Olga V Kalinina, Dietrich Klakow, Kenan A J Bozhüyük
[2026-9-22] >> Nat Commun • PubMed • AI/Biosynthesis/ESM/ProteinMPNN
🔎 Abstract
Generative models design thiolation domains that remain functional in engineered non-ribosomal peptide synthetase assembly lines, supported by in vivo production assays.
Integrated abundance analysis and artificial intelligence-assisted screening reveal novel antihypertensive milk-derived tripeptides
Chiao-Che Chen, Yun-Jhu Hou, Hsin-Yi Lo, Mei-Li Stevens, Chien-Yun Hsiang, Tin-Yun Ho
[2026-9-22] >> Food Chem • PubMed • AI/Screening/Metabolism
🔎 Abstract
An integrated computational-experimental workflow prioritizes milk-derived tripeptides and evaluates antihypertensive activity in spontaneously hypertensive rats.
The future of peptide ADMET prediction: Leveraging AI to unlock new possibilities
Qianhui Liu, Xiaorong Tan, Feifan Xie, Defang Ouyang, Wenbin Zeng, Jie Dong
[2026-9-21] >> Drug Discov Today • PubMed • AI/Permeability/Stability
🔎 Abstract
Reviews peptide-specific ADMET prediction, highlighting modified-peptide representations, data scarcity and transferability limits rather than assuming small-molecule predictors generalize.
SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction
Wanchao Chen, Wen Li, Yanan He, Yan Yang
[2026-09-15] >> arXiv • Benchmark/PLM/ESM
🔎 Abstract
Preprint: SaltyMeta curates short salty-peptide evidence, similarity-controlled splits and protein-language-model screening, with modest held-out performance and explicit sensory-validation limitations.
Macrocyclization of native peptides through (thio)urea crosslinking of two amines
Jinyao Liu, Peiru Chen, Meilin Tang, Qiuyu Chen, Yixin Liao, Yu Liu, Shafi Ullah, Jinwu Zhao, Yubo Long, Gong Chen, Wenfang Xiong
[2026-9-2] >> Nat Commun • PubMed • Cyclization/Cyclic/Permeability
🔎 Abstract
Site-selective amine crosslinking generates native-peptide macrocycles with urea or thiourea bridges, with binding, activity and developability evaluation.
A self-assembled peptide forms α-helical nanopores for ultrasensitive biomarker profiling
Varsha Shaji, Rajeev Jain, Neethu Puthumadathil, Kalyanashis Jana, Vedasmiritha T S, Ulrich Kleinekathöfer, Krishnananda Chattopadhyay, Kozhinjampara R Mahendran
[2026-8-28] >> Nat Nanotechnol • PubMed • Self-Assembly/Noncanonical
🔎 Abstract
Peptide-based alpha-helical nanopores with engineered diameters enable single-molecule biomarker sensing and distinguish heterogeneous protein assemblies.
PathoMIC: A Benchmark for Cross-Species Antimicrobial Peptide Activity Prediction
Yeqing Lu, Xiaoyan Zhao, Fuli Feng
[2026-08-26] >> arXiv • Benchmark/AMPs
🔎 Abstract
Preprint: PathoMIC standardizes quantitative AMP activity measurements across pathogen species and evaluates few-shot and zero-shot transfer using pathogen descriptions and taxonomy.
Quantitative and interface-aware prediction of peptide–protein interactions by VITAL
Wei-Hao Chen, Qi-Wen Wang, Zhi-Yi Li, Song-Yang Li, Chen Lin and Zhi-Liang Ji
[2026-8-19] >> Nat Mach Intell • high • GitHub • paper-read • PLM/Graph/Binding/ESM
Computational design of antimicrobial peptide nanopores
Rahul Deb, Marcelo D. T. Torres, Ivo Kabelka, Jan Přibyl, Kateřina Dvořáková Bendová, Edo Vreeker, Markéta Koběrská, Gabriela Balíková Novotná, Miloš Petřík, Giovanni Maglia, Cesar de la Fuente-Nunez and Robert Vácha
[2026-8-3] >> Nat Chem Biol • high • Data and scripts • MD/AMPs/Self-Assembly
🔎 Abstract
Molecular-dynamics-guided design produces membrane-spanning antimicrobial peptide nanopores, with experimental mechanism studies and efficacy in mouse infection models.
AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction
Ziheng Zhou, Huiyu Luo, Xiaohu Zhu, Nan Wang, Xuebiao Qin, Chaoyan Zhang, Jun Yan
[2026-07-28] >> arXiv • Hugging Face • Benchmark/AMPs
🔎 Abstract
Preprint: a homology-controlled benchmark connects AMP recognition with assay-derived potency, spectrum and safety endpoints, emphasizing provenance and endpoint-specific evaluation.
An agent-guided peptide hydrogel bio-stabilizer clamps pericellular viscoelastic drift
Xiao Wei, Liqiang Zhang, Zhuo Chang, Fan Ding, Ziyan Qu, Jianghao Chen, Guangkui Xu, Wangxiao He, Wenjia Liu
[2026-7-27] >> Nat Commun • PubMed • Pipeline/Hydrogel
🔎 Abstract
A rule-based agent workflow designs ViscoClamp peptide hydrogels to stabilize glycation-driven pericellular mechanics and support bone repair in animal models.
Ferricyanide-mediated direct ligation of peptide hydrazides in neutral water
Dongyang Han, Xianglai Zhu, Guiyu Deng, Wei He, Tianyi Zhang, Huasong Ai, Guo-Chao Chu and Lei Liu
[2026-7-24] >> Nat. Synth • Ligation
🔎 Abstract
A ferricyanide-mediated route directly ligates peptide hydrazides under neutral aqueous conditions.
EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic Peptides
Aaron L. Feller, Kris Deibler, Maxim Secor
[2026-07-23] >> arXiv • Graph/Cyclic/Permeability
🔎 Abstract
Preprint: EnsembleEGNN encodes conformational ensembles with equivariant graph layers and set attention, improving cyclic peptide permeability prediction under random and chemical holdout splits.
Structural and functional basis of antinociceptive action of χ-conotoxin AoIA at the noradrenaline transporter
Oliver J. V. Belleza, Heng Zhang, Helmut Schmidhammer, Tye I. Gonzalez, Cosmin I. Ciotu, Nataša Tomašević, Carlo Martin M. Ocampo, Jomari C. Fernando, Johannes Koehbach, Paula Schwarz, Mounaf Al Makhlouf, Gabor Tajti, Simon Hasinger, Nina Kastner, Orcun Avsar, Bernhard Retzl, Kathrin Jäntsch, Yi Jiang, Roland Hellinger, Michael J. M. Fischer, K. Johan Rosengren, Christian W. Gruber, Aaron Joseph L. Villaraza, Thomas Stockner, Mariana Spetea, H. Eric Xu and Harald H. Sitte
[2026-7-21] >> Nat Struct Mol Biol • Conotoxin/Cryo-EM/NMR/Neuroscience
🔎 Abstract
Pharmacological and structural studies characterize conotoxin AoIA inhibition of the noradrenaline transporter and antinociceptive activity in a mouse inflammatory-pain model.
PeptiVerse: A unified platform for therapeutic peptide property prediction
Yinuo Zhang, Sophia Tang, Tong Chen, Elizabeth Mahood, Sophia Vincoff, Pranam Chatterjee
[2026-7-16] >> Nat Commun • high • PubMed • PLM/ESM/Noncanonical/Permeability
🔎 Abstract
PeptiVerse supports therapeutic peptide property evaluation from amino acid sequences or chemically modified peptide SMILES using pretrained representations and task-specific predictors.
Bridging stimulus-responsive and dissipative peptide assemblies to build complex interactive biomaterials
Maximilian Schuler, Ayan Chatterjee, David Y W Ng, Tanja Weil
[2026-7-15] >> Nat Rev Chem • PubMed • Self-Assembly
🔎 Abstract
Reviews stimulus-responsive and chemically fuelled peptide assemblies, and strategies for integrating dynamic assembly with biological environments.
Vilya-1: An all-atom foundation model for macrocycle structure prediction and design
Vilya Research:, Pascal Sturmfels, Milad Salem, Naozumi Hiranuma, Stephen Rettie, Xiaoliang Pan, Benjamin D. Sellers, Adam P. Moyer, Patrick J. Salveson, Ivan Anishchanka
[2026-07-10] >> arXiv • AI/Full-Atom/Cyclic/Noncanonical
🔎 Abstract
Preprint: Vilya-1 models macrocycle conformations and developability using all-atom representations across canonical and noncanonical chemistries, with additional design applications.
HFGuidedDesign: de novo design of cyclic peptide binders via structure-guided discrete diffusion
Haomeng Hu, Renjie Zhu, Ning Zhu, Chengyun Zhang, Tianfeng Shang, Chongyang Li, Jingjing Guo, Xudong Wang, Hongliang Duan
[2026-6-30] >> Chem Sci • PubMed • Diffusion/Cyclic/AF/Hongliang Duan
🔎 Abstract
Combines discrete sequence diffusion with HighFold structural guidance for target-specific cyclic peptide binder design.
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Shuan Chen, Yuna Oikawa, Yiming Zhang, Gyubok Lee, Keisuke Ozawa
[2026-06-26] >> arXiv • AI/Benchmark
🔎 Abstract
Preprint: Pepti-drift performs one-step latent refinement for predicted binding and safety objectives, while BindSafe-PepBench controls output-length confounding in evaluation.
Scalable Peptide Design via Memory-Efficient Equivariant Transformer
Rui Jiao, Xiangzhe Kong, Yinjun Jia, Yijia Zhang, Ziyi Yang, Yang Liu, Jianzhu Ma
[2026-06-23] >> arXiv • Diffusion/Full-Atom
🔎 Abstract
Preprint: a memory-efficient equivariant transformer supports scalable full-atom peptide generation within a variational-autoencoder and latent-diffusion pipeline.
CABS-flex standalone 3: an open command-line platform for protein flexibility simulation, peptide structure modeling, and protein-peptide docking
Chandran Nithin, Karol Wroblewski, Piotr Szukalo, Ayomide Fasemire, Aleksander Kuriata, Mateusz Kurcinski, Andrzej Kolinski, Sebastian Kmiecik
[2026-06-23] >> arXiv • GitHub • Docking/AI/Cyclic
🔎 Abstract
Preprint: CABS-flex standalone 3 integrates coarse-grained flexibility simulation, peptide structure modeling, flexible docking and all-atom reconstruction in a Python command-line package.
Structure, Interactions, and Assembly of Membrane-Active Antimicrobial Polypeptides
Tzong-Hsien Lee, Patrick Charchar, Marc-Antoine Sani, Dang-Huy Le, Tu C. Le, Irene Yarovsky, Frances Separovic and Marie-Isabel Aguilar
[2026-5-21] >> Chem. Rev. • high • paper-read • AMPs
PEP-EDIT: a web server for the 3D generation and interactive editing of complex peptides
Nicolas Chevrollier, Alexis Dougha, Celine Ye, Dirk Stratmann, Gautier Moroy, Julien Rey, Samuel Murail and Pierre Tufféry
[2026-5-14] >> Nucleic Acids Research • paper-read
🔎 Abstract
In recent years, the development of peptide drugs has seen significant growth. These molecules often go beyond simple linear chains composed of the standard 20 amino acids. Peptide drugs frequently incorporate non-standard amino acids, non-amino components, and can exhibit mono- or multicyclic structures, branching, and other complex topologies. Consequently, there is a growing need for accessible tools that allow researchers to easily generate and modify 1D, 2D, and 3D representations of these complex peptides, serving as a starting point for further optimization. PEP-EDIT was created to meet this need. It offers a user-friendly, interactive web interface for generating complex peptide representations from 1D BILN (Boehringer Ingelheim Line Notation) sequences, using a customizable monomer library. Building on the pyPept library, PEP-EDIT enhances its functionality with options such as pH-dependent protonation and simplified specification of conformational constraints. The platform leverages interactive 2D and 3D visualizations to guide peptide design, offers intuitive management of monomers and 3D models, and includes collaborative and interactive visualization tools. PEP-EDIT is available at https://pep-edit.rpbs.univ-paris-diderot.fr. This website is free and open to all users and there is no login requirement.
Reusability report: Meta-learning for antigen-specific T cell receptor binder identification
Fei He, Xianyu Wang and Dong Xu
[2026-5-6] >> Nat Mach Intell • GitHub • paper-read
AI-Designed Peptides as Tools for Biochemistry
Lauren Hong, Sophia Vincoff and Pranam Chatterjee
[2026-4-10] >> Biochemistry • [paper-read](https://paper.molastra.org/journal/2026/202604/AI-Designed Peptides/) • AI
📌 Papers pinned:
Quantitative and interface-aware prediction of peptide–protein interactions by VITAL
Wei-Hao Chen, Qi-Wen Wang, Zhi-Yi Li, Song-Yang Li, Chen Lin and Zhi-Liang Ji
[2026-8-19] >> Nat Mach Intell • high • GitHub • paper-read • PLM/Graph/Binding/ESM
Computational design of antimicrobial peptide nanopores
Rahul Deb, Marcelo D. T. Torres, Ivo Kabelka, Jan Přibyl, Kateřina Dvořáková Bendová, Edo Vreeker, Markéta Koběrská, Gabriela Balíková Novotná, Miloš Petřík, Giovanni Maglia, Cesar de la Fuente-Nunez and Robert Vácha
[2026-8-3] >> Nat Chem Biol • high • Data and scripts • MD/AMPs/Self-Assembly
🔎 Abstract
Molecular-dynamics-guided design produces membrane-spanning antimicrobial peptide nanopores, with experimental mechanism studies and efficacy in mouse infection models.
PeptiVerse: A unified platform for therapeutic peptide property prediction
Yinuo Zhang, Sophia Tang, Tong Chen, Elizabeth Mahood, Sophia Vincoff, Pranam Chatterjee
[2026-7-16] >> Nat Commun • high • PubMed • PLM/ESM/Noncanonical/Permeability
🔎 Abstract
PeptiVerse supports therapeutic peptide property evaluation from amino acid sequences or chemically modified peptide SMILES using pretrained representations and task-specific predictors.
BindCraft: one-shot design of functional protein binders
Martin Pacesa, Lennart Nickel, ..., Sergey Ovchinnikov, Bruno E. Correia
[2025-8-27] >> Nature • high • GitHub • 公众号 / paper-read
🔎 Abstract
BindCraft is an open-source, automated pipeline for <em>de novo</em> protein binder design, achieving experimental success rates of 10-100%. Using deep learning models like AlphaFold2, BindCraft generates high-affinity binders without the need for high-throughput screening or prior knowledge of binding sites. It has been successfully applied to challenging targets, including cell-surface receptors, allergens, and CRISPR-Cas9. In one example, the binders reduced IgE binding to birch allergens in patient samples, showcasing its potential in therapeutics, diagnostics, and biotechnology.
Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model
Jike Wang, Jianwen Feng, Yu Kang, Peichen Pan, Jingxuan Ge, Yan Wang, Mingyang Wang
[2025-3-7] >> Science Advances • high • GitHub • 公众号 • AMPs/Tingjun Hou/GPT/Chang-Yu Hsieh
PepINVENT: Generative peptide design beyond the natural amino acids
Gökçe Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He, Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola Engkvist, Leonardo De Maria
[2025] >> Chem. Sci. • GitHub • paper-read • RL/Molecular AI/AstraZeneca/Noncanonical
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Jiahan Li, Tong Chen, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma
[2024-11-26] >> ICML/arXiv • Jianzhu Ma/Flow
Accurate de Novo Design of High-Affinity Protein Binding Macrocycles Using Deep Learning
Stephen Rettie, ..., Gaurav Bhardwaj
[2024-11-18] >> bioRxiv • high • RFdiffusion/David Baker/Gaurav Bhardwaj/Cyclic
Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
Fanhao Wang, Yuzhe Wang, Laiyi Feng, Changsheng Zhang, and Luhua Lai
[2024-9-4] >> JCIM • high • GitHub • Diffusion/Luhua Lai/ColabDesign/ProteinMPNN/MD
🔎 Abstract
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein−protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein−peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.
CycPeptMP: Enhancing Membrane Permeability Prediction of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation
Jianan Li, Keisuke Yanagisawa, and Yutaka Akiyama
[2024-9-1] >> BIB • high • CycPeptMPDB • GitHub • Cyclic/Akiyama Yutaka
Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion
Osama Abdin & Philip M. Kim
[2024-6-27] >> NMI • high • data • PepFlow • Cyclic/MD/Diffusion
Full-Atom Peptide Design Based on Multi-Modal Flow Matching
Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, and Jianzhu Ma
[2024-6-2] >> arXiv • high • GitHub • Jianzhu Ma/Flow
Full-Atom Peptide Design with Geometric Latent Diffusion
Xiangzhe Kong, Yinjun Jia, Wenbing Huang, Yang Liu
[2024-2-21] >> NeurIPS/Arxive • code • Full-Atom/Diffusion
PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling
Tianlai Chen, Sarah Pertsemlidis, and Pranam Chatterjee
[2023-10-5] >> ICLR • high • Pranam Chatterjee/MLM
Improving de novo protein binder design with deep learning
Nathaniel R. Bennett, Brian Coventry, ..., David Baker
[2023-5-6] >> NC • high • GitHub • RosettaCommons/ProteinMPNN
Denovo design of modular peptide-binding proteins by superhelical matching
Kejia Wu, Hua Bai, ..., Emmanuel Derivery, Daniel Adriano Silva, David Baker
[2023-3-5] >> Nature • high • data • GitHub • David Baker
🔎 Abstract
General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1–3. Here, inspired by natural and re-engineered proteinpeptide systems4–11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.
Target structure based computational design of cyclic peptides
WANG Fanhao, LAI Luhua, ZHANG Changsheng
[2023-1-1] >> SynbioJ • high • pdf • Cyclic/MD/Luhua Lai
Design of Protein Segments and Peptides for Binding to Protein Targets
Suchetana Gupta, Noora Azadvari, and Parisa Hosseinzadeh
[2022-1-1] >> BioDesign Research • high
Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites
Parisa Hosseinzadeh, ..., David Baker
[2021-7-7] >> NC • Peptide_HDACBinders • Tencent • Cyclic/David Baker/MD/Crystal
Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations
Jovan Damjanovic, Jiayuan Miao, He Huang, Yu-Shan Lin
[2021-1-11] >> Chemical Reviews • high • Cyclic/MD
Strategies for Fine-Tuning the Conformations of Cyclic Peptides
Rasha Jwad, Daniel Weissberger, and Luke Hunter
[2020-8-5] >> Chem. Rev. • high • Cyclic
0) Benchmarks and Datasets
Benchmarks •
Datasets •
Related Resources •
Guides •
Tools
1) Reviews
Design & Generation •
Structure & Interaction •
Property & Activity •
Therapeutics & Applications •
Delivery & Biomaterials
2) Data, Representation & Analysis
Sequence & Language •
Structure & Graph •
Datasets & Benchmarks •
Experimental Characterization
3) Property & Activity
Bioactivity & Function •
Permeability & Developability •
Interaction & Binding
4) Structure & Interaction
Peptide Conformation •
Peptide-Protein Complexes •
Docking & Simulation
5) Peptide Design & Generation
Sequence-Based Design •
Structure-Based Design •
Diffusion & Flow •
Reinforcement Learning •
Classical & Fragment-Based
6) Synthesis & Chemical Modification
Solid-Phase & Solution Synthesis •
Ligation & Cyclization •
Noncanonical & Conjugated Peptides •
Biosynthesis & Biocatalysis
7) Biology & Mechanisms
Signaling & Regulation
8) Delivery & Biomaterials
Delivery & Formulation •
Self-Assembly & Hydrogels •
Materials & Biosensing
9) Applications & Tools
Software & Webservers •
Screening & Discovery •
Therapeutics & Translation •
Protein Binders •
Chemical Biology & Modalities •
Oncology
Benchmark papers are curated in Data, Representation & Analysis alongside dataset papers.
| Resource | Scope | Link |
|---|---|---|
| Protein-peptide docking benchmark | Deep-learning and focused-docking evaluation | benchmarking_2023 |
| CPSet | Protein-cyclic peptide complex benchmark | CPSet |
| LEADS-PEP / DockThor | Flexible protein-peptide docking | DockThor |
| Dataset | Description | Link |
|---|---|---|
| CycPeptMPDB | Experimentally measured cyclic peptide membrane permeability. | CycPeptMPDB |
| State of Peptides 2026 | Open reference dataset of 156 peptide and peptide-adjacent compounds with regulatory status, category, route, half-life, molecular weight, CAS, and PubChem/DrugBank/Wikidata cross-references (CSV/JSON, CC BY 4.0). | State of Peptides 2026 |
- RCSB PDB: experimentally determined structures, including peptide complexes.
- HELM Web Editor: representations of chemically modified peptides and other complex polymers.
- Biopython Bio.PDB tutorial: PDB/mmCIF parsing and structural analysis.
- RDKit in Python: chemical representations and molecular analysis.
| Task | Resource |
|---|---|
| Peptide notation | HELM Online, HELM documentation |
| Structure processing | pdb-tools, Biopython, BioPandas, RDKit |
| Interaction analysis | Protein-Ligand Interaction Profiler (PLIP) |
| Molecular weight | Peptide Molecular Weight Calculator: average mass, monoisotopic mass, formula and m/z for standard amino acid sequences, with acetylation, amidation and disulfide options. |
| Charge estimates | Peptide Net Charge Calculator: Henderson-Hasselbalch net charge over pH 0-14, estimated pI and GRAVY using EMBOSS pKa values, with terminal modification options. |
AI-Designed Peptides as Tools for Biochemistry
Lauren Hong, Sophia Vincoff and Pranam Chatterjee
[2026-4-10] >> Biochemistry • [paper-read](https://paper.molastra.org/journal/2026/202604/AI-Designed Peptides/) • AI
The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation
Wenjing Hu, Yuan Sun, Ting Li, Mark Williamson, Xiaoying Hu and Maolin Wang
[2026-4-1] >> The Innovation Drug Discovery • high • paper-read • Diffusion/MD/AI
🔎 Abstract
Targeted peptide therapeutics offer a potent solution for undruggable intracellular targets, and this review summarizes computation-driven peptide drug design from predictive modeling to generative AI and clinical translation.
AI-Driven Antimicrobial Peptide Discovery: Mining and Generation
Paulina Szymczak, Wojciech Zarzecki, Jiejing Wang, Yiqian Duan, Jun Wang, Luis Pedro Coelho, Cesar de la Fuente-Nunez and Ewa Szczurek
[2025-6-3] >> Acc. Chem. Res. • high • paper-read • AMPs
Artificial intelligence in peptide-based drug design
Zhai Silong, Tiantao Liu, Shaolong Lin, Dan Li, Huanxiang Liu, Xiaojun Yao and Tingjun Hou
[2025-2-1] >> Drug Discovery Today • GitHub • Tingjun Hou
Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning
Daria de Raffele and Ioana M. Ilie
[2023-11-7] >> Chem. Commun. • Cyclic/MD
Target structure based computational design of cyclic peptides
WANG Fanhao, LAI Luhua, ZHANG Changsheng
[2023-1-1] >> SynbioJ • high • pdf • Cyclic/MD/Luhua Lai
Design of Protein Segments and Peptides for Binding to Protein Targets
Suchetana Gupta, Noora Azadvari, and Parisa Hosseinzadeh
[2022-1-1] >> BioDesign Research • high
Peptide-protein docking: from physics-based models to generative intelligence
Kai Ling, Shu Li, Zicong Zhang, Woong-Hee Shin and Daisuke Kihara
[2026] >> Chem. Commun. • high • paper-read • Docking/Diffusion
🔎 Abstract
We review the evolution of peptide–protein docking methods from traditional physics-based approaches to modern AlphaFold-inspired and diffusion-based frameworks. Their impact, remaining limitations, and open challenges are discussed.
A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond
Pengzhen Jia, Fuhao Zhang, Chaojin Wu and Min Li
[2024-3-31] >> BIB
Modelling peptide–protein complexes: docking, simulations and machine learning
Arup Mondal, Liwei Chang and Alberto Perez
[2022-8-26] >> QRB Discovery • Docking/MD
Peptide-based inhibitors of protein-protein interactions: biophysical, structural and cellular consequences of introducing a constraint
Hongshuang Wang, Robert S. Dawber, Peiyu Zhang, Martin Walko, Andrew J. Wilson and Xiaohui Wang
[2021] >> Chem. Sci. • paper-read • Cyclic
🔎 Abstract
This review summarizes the influence of inserting constraints on biophysical, conformational, structural and cellular behaviour for peptides targeting α-helix mediated protein–protein interactions.
Strategies for Fine-Tuning the Conformations of Cyclic Peptides
Rasha Jwad, Daniel Weissberger, and Luke Hunter
[2020-8-5] >> Chem. Rev. • high • Cyclic
Antimicrobial peptide databases: A detailed comparison and complementary features
Erfan Zamani, Mohammadhossein Ghazizadeh-Ahsaei, Yasaman Mahmoodi, Faramarz Mehrnejad
[2026-9-25] >> Peptides • PubMed • AMPs/Benchmark
🔎 Abstract
Compares APD, CAMP, DBAASP, dbAMP and DRAMP for annotation, activity evidence, usability and computational workflows, identifying complementary strengths and interoperability needs.
The future of peptide ADMET prediction: Leveraging AI to unlock new possibilities
Qianhui Liu, Xiaorong Tan, Feifan Xie, Defang Ouyang, Wenbin Zeng, Jie Dong
[2026-9-21] >> Drug Discov Today • PubMed • AI/Permeability/Stability
🔎 Abstract
Reviews peptide-specific ADMET prediction, highlighting modified-peptide representations, data scarcity and transferability limits rather than assuming small-molecule predictors generalize.
Structure, Interactions, and Assembly of Membrane-Active Antimicrobial Polypeptides
Tzong-Hsien Lee, Patrick Charchar, Marc-Antoine Sani, Dang-Huy Le, Tu C. Le, Irene Yarovsky, Frances Separovic and Marie-Isabel Aguilar
[2026-5-21] >> Chem. Rev. • high • paper-read • AMPs
Machine learning for antimicrobial peptide identification and design
Fangping Wan, Felix Wong, James J. Collins & Cesar de la Fuente-Nunez
[2024-2-26] >> Nat Rev Bioeng • AMPs
Recent Advances in Peptide Linkers of Antibody-Drug Conjugates
Lu Yang, Jiahui Ma, Ben Liu, Yangbing Li, Yaping Ma, Hao Chen and Zhijian Han
[2025-9-2] >> Journal of Medicinal Chemistry • paper-read
🔎 Abstract
Antibody–drug conjugates (ADCs) represent a promising class of cancer therapeutics. This innovative molecular design perfectly integrates the targeting and extended half-life of antibodies with the cytotoxicity of small molecules, enabling the selective delivery of payloads to cancer cells. The linker molecule is crucial to the efficacy of an ADC. Although ADC linkers can be cleavable or noncleavable, most approved ADCs utilize cleavable peptide linkers. These linkers, cleaved by enzymes such as cathepsin, plasmin, or legumain, balance the stability of ADCs in the circulatory system with selective release of the cytotoxic payload in tumors. Linker chemistry has thus become a highly important and integral part of the ADC development. In this perspective, we elucidate the role of peptide linkers in the ADC development, highlight advancements in peptide linkers, and provide insights on future directions for ADC linker designs.
Recent Advances in Augmenting the Therapeutic Efficacy of Peptide-Drug Conjugates
Jiahui Ma, Xuedan Wang, Yonghua Hu, Jianping Ma, Yaping Ma, Hao Chen and Zhijian Han
[2025-4-23] >> J. Med. Chem. • paper-read • PDCs
Converting peptides into drugs targeting intracellular protein–protein interactions
Grégoire J.B. Philippe, David J. Craik and Sónia T. Henriques
[2021-6-1] >> Drug Discov Today
Trends in peptide drug discovery
Markus Muttenthaler, Glenn F. King, David J. Adams and Paul F. Alewood
[2021-4-1] >> Nature Reviews Drug Discovery • high
A Global Review on Short Peptides: Frontiers and Perspectives
Vasso Apostolopoulos, Joanna Bojarska, ...
[2021-1-15] >> Molecules
Peptide-mediated CRISPR delivery: From dish to bloodstream
Alzbeta Ressnerova, Ross C Wilson
[2026-9-24] >> Curr Opin Chem Biol • PubMed • Delivery/CPPs
🔎 Abstract
Reviews peptide-mediated CRISPR delivery from cell culture to local and systemic in vivo applications, including clearance, endosomal escape and formulation barriers.
Bridging stimulus-responsive and dissipative peptide assemblies to build complex interactive biomaterials
Maximilian Schuler, Ayan Chatterjee, David Y W Ng, Tanja Weil
[2026-7-15] >> Nat Rev Chem • PubMed • Self-Assembly
🔎 Abstract
Reviews stimulus-responsive and chemically fuelled peptide assemblies, and strategies for integrating dynamic assembly with biological environments.
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation
Leyao Wang, Rishab Pulugurta, Pranay Vure, Yinuo Zhang, Aastha Pal, and Pranam Chatterjee
[2024-10-28] >> arXiv • Pranam Chatterjee/MLM/Noncanonical
EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic Peptides
Aaron L. Feller, Kris Deibler, Maxim Secor
[2026-07-23] >> arXiv • Graph/Cyclic/Permeability
🔎 Abstract
Preprint: EnsembleEGNN encodes conformational ensembles with equivariant graph layers and set attention, improving cyclic peptide permeability prediction under random and chemical holdout splits.
Propedia v2.3: A novel representation approach for the peptide-protein interaction database using graph-based structural signatures
Pedro Martins, Diego Mariano, Frederico Chaves Carvalho, Luana Luiza Bastos, Lucas Moraes, Vivian Paixão and Raquel Cardoso de Melo-Minardi
[2023-2-16] >> Front. Bioinform. • SI • propedia • Graph
SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction
Wanchao Chen, Wen Li, Yanan He, Yan Yang
[2026-09-15] >> arXiv • Benchmark/PLM/ESM
🔎 Abstract
Preprint: SaltyMeta curates short salty-peptide evidence, similarity-controlled splits and protein-language-model screening, with modest held-out performance and explicit sensory-validation limitations.
PathoMIC: A Benchmark for Cross-Species Antimicrobial Peptide Activity Prediction
Yeqing Lu, Xiaoyan Zhao, Fuli Feng
[2026-08-26] >> arXiv • Benchmark/AMPs
🔎 Abstract
Preprint: PathoMIC standardizes quantitative AMP activity measurements across pathogen species and evaluates few-shot and zero-shot transfer using pathogen descriptions and taxonomy.
AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction
Ziheng Zhou, Huiyu Luo, Xiaohu Zhu, Nan Wang, Xuebiao Qin, Chaoyan Zhang, Jun Yan
[2026-07-28] >> arXiv • Hugging Face • Benchmark/AMPs
🔎 Abstract
Preprint: a homology-controlled benchmark connects AMP recognition with assay-derived potency, spectrum and safety endpoints, emphasizing provenance and endpoint-specific evaluation.
Advancements in Nanobody Epitope Prediction: A Comparative Study of AlphaFold2Multimer vs AlphaFold3
Floriane Eshak and Anne Goupil-Lamy
[2025-2-10] >> J. Chem. Inf. Model. • AF/Benchmark
Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein–Cyclic Peptide Complexes
Huifeng Zhao, Dejun Jiang, Chao Shen, Jintu Zhang, Xujun Zhang, Xiaorui Wang, Dou Nie, Tingjun Hou and Yu Kang
[2024-3-14] >> J. Chem. Inf. Model. • CPSet • Cyclic/Docking/Benchmark/Tingjun Hou
Predicting Protein–Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking
Sudhanshu Shanker and Michel F. Sanner
[2023-5-11] >> J. Chem. Inf. Model. • GitHub • Docking/AF/Benchmark
CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides
Jianan Li, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Masahito Ohue and Yutaka Akiyama
[2023-3-17] >> Journal of Chemical Information and Modeling • CycPeptMPDB • Cyclic/Permeability/Akiyama Yutaka
Benchmarking AlphaFold2 on peptide structure prediction
Eli Fritz McDonald, Taylor Jones, Lars Plate, Jens Meiler and Alican Gulsevin
[2023-1] >> Structure • Weixin • AF/Benchmark
Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes
Gaoqi Weng, Junbo Gao, Zhe Wang, Ercheng Wang, Xueping Hu, Xiaojun Yao, Dongsheng Cao and Tingjun Hou
[2020-4-23] >> J. Chem. Theory Comput. • high • pepset • Docking/Benchmark/Tingjun Hou
Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein–Peptide Data Set
Karina B. Santos, Isabella A. Guedes, Ana L. M. Karl and Laurent E. Dardenne
[2020-1-10] >> J. Chem. Inf. Model. • DockThor • Docking/Benchmark
GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing
Abdellah El Mekki, Laks V. S. Lakshmanan, Muhammad Abdul-Mageed
[2026-09-24] >> arXiv • MS/AI/Proteomics
🔎 Abstract
Preprint: GyroNovo combines decoder-error-guided fragment imputation with mass-aware attention for de novo peptide sequencing from tandem mass spectra.
Deep learning reveals antibiotics in the archaeal proteome
Marcelo D. T. Torres, Fangping Wan and Cesar de la Fuente-Nunez
[2025-8-12] >> Nat Microbiol • high • GitLab • paper-read • AMPs
🔎 Abstract
Antimicrobial resistance is one of the greatest threats facing humanity, making the need for new antibiotics more critical than ever. While most antibiotics originate from bacteria and fungi, archaea offer a largely untapped reservoir for antibiotic discovery. In this study, we leveraged deep learning to systematically explore the archaeome, uncovering promising candidates for combating antimicrobial resistance. By mining 233 archaeal proteomes, we identified 12,623 molecules with potential antimicrobial activity. These peptide compounds, termed archaeasins, have unique compositional features that differentiate them from traditional antimicrobial peptides, including a distinct amino acid profile. We synthesized 80 archaeasins, 93% of which showed antimicrobial activity in vitro against Acinetobacter baumannii , Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa , Staphylococcus aureus and Enterococcus spp. Notably, in vivo validation identified archaeasin-73 as a lead candidate, significantly reducing A. baumannii loads in mouse infection models, with effectiveness comparable to that of established antibiotics such as polymyxin B. Our findings highlight the potential of archaea as a resource for developing next-generation antibiotics.
pLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides
Nandan Kumar, Zhenjiao Du and Yonghui Li
[2025-1-29] >> JCIM • PLM/CPPs
Peptide-Aware Chemical Language Model Successfully Predicts Membrane Diffusion of Cyclic Peptides
Aaron L. Feller, Claus O. Wilke
[2024-11-21] >> bioRxiv • Cyclic
Discovery of a Series of Macrocycles as Potent Inhibitors of Leishmania Infantum
Federico Riu, Larissa Alena Ruppitsch, Duc Duy Vo, Richard S. Hong, Mohit Tyagi, An Matheeussen, Sarah Hendrickx, Vasanthanathan Poongavanam, Guy Caljon, Ahmad Y. Sheikh, Peter Sjö, and Jan Kihlberg
[2024-10-8] >> J. Med. Chem. • high • 公众号 • Cyclic
Beware of extreme calculated lipophilicity when designing cyclic peptides
Vasanthanathan Poongavanam, Duc Duy Vo & Jan Kihlberg
[2024-9-19] >> Nat. Chem. Biol. • SI • 公众号 • Cyclic/cLogP
CycPeptMP: Enhancing Membrane Permeability Prediction of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation
Jianan Li, Keisuke Yanagisawa, and Yutaka Akiyama
[2024-9-1] >> BIB • high • CycPeptMPDB • GitHub • Cyclic/Akiyama Yutaka
Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design
Jiada Li
[2026-09-28] >> arXiv • GitHub • Binding/Benchmark/Noncanonical
🔎 Abstract
Preprint: Mirror-Score evaluates inference-only ranking of D-peptide/L-protein complexes and demonstrates the limited affinity validity and cross-family transfer of sequence-compatibility scores.
Quantitative and interface-aware prediction of peptide–protein interactions by VITAL
Wei-Hao Chen, Qi-Wen Wang, Zhi-Yi Li, Song-Yang Li, Chen Lin and Zhi-Liang Ji
[2026-8-19] >> Nat Mach Intell • high • GitHub • paper-read • PLM/Graph/Binding/ESM
Reusability report: Meta-learning for antigen-specific T cell receptor binder identification
Fei He, Xianyu Wang and Dong Xu
[2026-5-6] >> Nat Mach Intell • GitHub • paper-read
An interaction-derived graph learning framework for scoring protein-peptide complexes
Huanyu Tao, Xiaoyu Wang and Sheng-You Huang
[2025-10-23] >> Nat Mach Intell • high • paper-read • Graph
Vilya-1: An all-atom foundation model for macrocycle structure prediction and design
Vilya Research:, Pascal Sturmfels, Milad Salem, Naozumi Hiranuma, Stephen Rettie, Xiaoliang Pan, Benjamin D. Sellers, Adam P. Moyer, Patrick J. Salveson, Ivan Anishchanka
[2026-07-10] >> arXiv • AI/Full-Atom/Cyclic/Noncanonical
🔎 Abstract
Preprint: Vilya-1 models macrocycle conformations and developability using all-atom representations across canonical and noncanonical chemistries, with additional design applications.
Predicting 3D Structures of Lasso Peptides
Xingyu Ouyang, Xinchun Ran, Han Xu, Yi-Lei Zhao, A. James Link, Zhongyue Yang
[2024-10-14] >> ChemRxiv • LassoPred/Web • Lasso/AF/ESM/MD
🔎 Abstract
这篇文章围绕 LassoPred 工具展开，解决了现有工具无法准确预测 套索肽（Lasso peptides, LaPs） 结构的挑战。套索肽以其 绳结状拓扑结构 和 异肽键 特性，使传统的结构预测工具（如 AlphaFold 和 ESMfold）难以处理。
Structure prediction of linear and cyclic peptides using CABS-flex
Aleksandra Badaczewska-Dawid, Karol Wróblewski, Mateusz Kurcinski & Sebastian Kmiecik
[2024-2-1] >> BIB • Cyclic
Deep-learning-based prediction framework for protein-peptide interactions with structure generation pipeline
Jingxuan Ge, Dejun Jiang, ..., Chang-Yu Hsieh, Tingjun Hou
[2024-6-19] >> Cell Rep. Phys. Sci. • zenodo • ITN • AF/Tingjun Hou
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints
Chenhao Zhang, Chengyun Zhang, Tianfeng Shang, Ning Zhu, Xinyi Wu, Hongliang Duan
[2024-5-5] >> BIB • HighFold • Hongliang Duan/Cyclic/AF
Ranking Peptide Binders by Affinity with AlphaFold
Liwei Chang and Alberto Perez
[2022-11-21] >> Angew • AF
Harnessing protein folding neural networks for peptide–protein docking
Tomer Tsaban, Julia K. Varga, Orly Avraham, Ziv Ben-Aharon, Alisa Khramushin & Ora Schueler-Furman
[2021-11-10] >> NC • GitHub • AF/Docking
Multi-Scale Temporal Flows for Peptide Trajectory Generation
Xichen Sun, Wentao Wei, Xiaoxi Zhang, Yuedong Yang, Jiahua Rao
[2026-10-01] >> arXiv • Flow/MD/Cyclic
🔎 Abstract
Preprint: PepTIDE models peptide trajectories using multi-scale temporal sampling and physical-time embeddings, evaluating distribution agreement, structural validity and inter-state transitions.
Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion
Osama Abdin & Philip M. Kim
[2024-6-27] >> NMI • high • data • PepFlow • Cyclic/MD/Diffusion
Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations
Jovan Damjanovic, Jiayuan Miao, He Huang, Yu-Shan Lin
[2021-1-11] >> Chemical Reviews • high • Cyclic/MD
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Shuan Chen, Yuna Oikawa, Yiming Zhang, Gyubok Lee, Keisuke Ozawa
[2026-06-26] >> arXiv • AI/Benchmark
🔎 Abstract
Preprint: Pepti-drift performs one-step latent refinement for predicted binding and safety objectives, while BindSafe-PepBench controls output-length confounding in evaluation.
DLFea4AMPGen de novo design of antimicrobial peptides by integrating features learned from deep learning models
Han Gao, Feifei Guan, Boyu Luo, Dongdong Zhang, Wei Liu, Yuying Shen, Lingxi Fan, Guoshun Xu, Yuan Wang, Tao Tu, Ningfeng Wu, Bin Yao, Huiying Luo, Yue Teng, Jian Tian and Huoqing Huang
[2025-10-15] >> Nat Commun • high • GitHub • paper-read • AMPs
Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model
Jike Wang, Jianwen Feng, Yu Kang, Peichen Pan, Jingxuan Ge, Yan Wang, Mingyang Wang
[2025-3-7] >> Science Advances • high • GitHub • 公众号 • AMPs/Tingjun Hou/GPT/Chang-Yu Hsieh
Improving Inverse Folding for Peptide Design with Diversity-Regularized Direct Preference Optimization
Ryan Park, Darren J. Hsu, C. Brian Roland, Chen Tessler, Maria Korshunova, Shie Mannor, Olivia Viessmann, Bruno Trentini
[2024-10-25] >> arXiv • ProteinMPNN/Nvidia
Computational design of antimicrobial peptide nanopores
Rahul Deb, Marcelo D. T. Torres, Ivo Kabelka, Jan Přibyl, Kateřina Dvořáková Bendová, Edo Vreeker, Markéta Koběrská, Gabriela Balíková Novotná, Miloš Petřík, Giovanni Maglia, Cesar de la Fuente-Nunez and Robert Vácha
[2026-8-3] >> Nat Chem Biol • high • Data and scripts • MD/AMPs/Self-Assembly
🔎 Abstract
Molecular-dynamics-guided design produces membrane-spanning antimicrobial peptide nanopores, with experimental mechanism studies and efficacy in mouse infection models.
Structure-based design of macrocyclic peptides to generate functional antibodies against G protein-coupled receptors
Marie-Edith Nepveu-Traversy, Malihe Hassanzadeh, Laurent Bruneau-Cossette, Élie Besserer-Offroy, Rebecca Brouillette, Sandra Morissette, Hassan Traboulsi, Karyn Kirby, Alexandre Murza, Jean-Michel Longpré, Billy Breton, Fernand-Pierre Gendron, Simon Gaudreau, Pierre-Luc Boudreault and Philippe Sarret
[2025-12-12] >> Nat Commun • paper-read • Cyclic
Peptide design through binding interface mimicry with PepMimic
Xiangzhe Kong, Rui Jiao, Haowei Lin, Ruihan Guo, Wenbing Huang, Wei-Ying Ma, Zihua Wang, Yang Liu and Jianzhu Ma
[2025-10-1] >> Nat. Biomed. Eng • high • GitHub • paper-read
Design of linear and cyclic peptide binders of different lengths from protein sequence information
Qiuzhen Li, Efstathios Nikolaos Vlachos, Patrick Bryant
[2024-10-12] >> Arxive • zenodo • EvoBind • Cyclic/Patrick Bryant
Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling
Suhaas Bhat, Kalyan Palepu, ..., Pranam Chatterjee
[2024-7-22] >> Arxive • zenodo • huggingface • Pipeline
🔎 Abstract
针对难以成药的蛋白质设计结合剂是药物开发中的难题，尤其是无序或构象不稳定的蛋白。我们提出了一种通用算法框架，利用目标蛋白的氨基酸序列设计短链线性多肽。通过对ESM-2蛋白语言模型的潜在空间进行高斯扰动生成多肽候选序列，并通过基于CLIP的对比学习架构筛选靶向选择性。最终创建了Peptide Prioritization via CLIP（PepPrCLIP）管道，并在实验中验证了这些多肽的有效性，既可作为抑制剂，也可通过与E3泛素连接酶融合降解多种蛋白靶标。该策略无需稳定的三级结构，能够靶向无序和难以成药的蛋白质，如转录因子和融合致癌蛋白。
Peptide binder design with inverse folding and protein structure prediction
Patrick Bryant and Arne Elofsson
[2023-10-25] >> Commun Chem • GitLab • paper-read • Patrick Bryant
🔎 Abstract
The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with ≥80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD ≤ 2 Å) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples.
PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling
Tianlai Chen, Sarah Pertsemlidis, and Pranam Chatterjee
[2023-10-5] >> ICLR • high • Pranam Chatterjee/MLM
Improving de novo protein binder design with deep learning
Nathaniel R. Bennett, Brian Coventry, ..., David Baker
[2023-5-6] >> NC • high • GitHub • RosettaCommons/ProteinMPNN
Denovo design of modular peptide-binding proteins by superhelical matching
Kejia Wu, Hua Bai, ..., Emmanuel Derivery, Daniel Adriano Silva, David Baker
[2023-3-5] >> Nature • high • data • GitHub • David Baker
🔎 Abstract
General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1–3. Here, inspired by natural and re-engineered proteinpeptide systems4–11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.
HFGuidedDesign: de novo design of cyclic peptide binders via structure-guided discrete diffusion
Haomeng Hu, Renjie Zhu, Ning Zhu, Chengyun Zhang, Tianfeng Shang, Chongyang Li, Jingjing Guo, Xudong Wang, Hongliang Duan
[2026-6-30] >> Chem Sci • PubMed • Diffusion/Cyclic/AF/Hongliang Duan
🔎 Abstract
Combines discrete sequence diffusion with HighFold structural guidance for target-specific cyclic peptide binder design.
Scalable Peptide Design via Memory-Efficient Equivariant Transformer
Rui Jiao, Xiangzhe Kong, Yinjun Jia, Yijia Zhang, Ziyi Yang, Yang Liu, Jianzhu Ma
[2026-06-23] >> arXiv • Diffusion/Full-Atom
🔎 Abstract
Preprint: a memory-efficient equivariant transformer supports scalable full-atom peptide generation within a variational-autoencoder and latent-diffusion pipeline.
Generative latent diffusion language modeling yields anti-infective synthetic peptides
Marcelo D.T. Torres, Leo Tianlai Chen, Fangping Wan, Pranam Chatterjee and Cesar de la Fuente-Nunez
[2025-10] >> Cell Biomaterials • high • GitHub • paper-read • AMPs/Diffusion
UniMoMo: Unified generative modeling of 3D molecules for de novo binder design
Kong, Xiangzhe, Zishen Zhang, Ziting Zhang, Rui Jiao, Jianzhu Ma, Kai Liu, Wenbing Huang and Yang Liu
[2025-3-25] >> arXiv • Yang Liu/Diffusion/Full-Atom
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
Sophia Tang, Yinuo Zhang and Pranam Chatterjee
[2024-12-23] >> arXiv • HuggingFace • paper-read • Diffusion/Pranam Chatterjee
🔎 Abstract
We present PepTune, a multi-objective discrete diffusion model for simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with a novel bond-dependent masking schedule and invalid loss function. To guide the diffusion process, we introduce Monte Carlo Tree Guidance (MCTG), an inference-time multi-objective guidance algorithm that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTG integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically-modified peptides simultaneously optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTG for masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Jiahan Li, Tong Chen, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma
[2024-11-26] >> ICML/arXiv • Jianzhu Ma/Flow
Accurate de Novo Design of High-Affinity Protein Binding Macrocycles Using Deep Learning
Stephen Rettie, ..., Gaurav Bhardwaj
[2024-11-18] >> bioRxiv • high • RFdiffusion/David Baker/Gaurav Bhardwaj/Cyclic
Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
Fanhao Wang, Yuzhe Wang, Laiyi Feng, Changsheng Zhang, and Luhua Lai
[2024-9-4] >> JCIM • high • GitHub • Diffusion/Luhua Lai/ColabDesign/ProteinMPNN/MD
🔎 Abstract
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein−protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein−peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.
Full-Atom Peptide Design Based on Multi-Modal Flow Matching
Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, and Jianzhu Ma
[2024-6-2] >> arXiv • high • GitHub • Jianzhu Ma/Flow
PPFlow: Target-Aware Peptide Design with Torsional Flow Matching
Lin, Haitao, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang and Stan Z. Li
[2024-3-8] >> ICML • Stan Z. Li/Flow
Full-Atom Peptide Design with Geometric Latent Diffusion
Xiangzhe Kong, Yinjun Jia, Wenbing Huang, Yang Liu
[2024-2-21] >> NeurIPS/Arxive • code • Full-Atom/Diffusion
Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
Ruihan Dong, Qiushi Cao and Chen Song
[2025-9-12] >> Advanced Science • high • GitHub • paper-read • AMPs/RL
🔎 Abstract
In the post‐antibiotic era, antimicrobial peptides (AMPs) are considered ideal drug candidates because of their lower likelihood of inducing resistance. Computational models provide an efficient way to design novel AMPs. However, current optimization and generation approaches are tailored for specific application scenarios, which hinders the ease of use. To address this challenge, a novel AMP design model named AMPainter is proposed. Based on deep reinforcement learning, AMPainter integrates optimization and generation tasks in a unified framework. AMPainter is applied to three types of peptides, including known AMPs, signal peptides (SPs), and random sequences. AMPainter outperforms ten related models in enhancing the activity of known AMPs on the predicted antimicrobial potency and diversity. Several AMPs demonstrate a 128‐fold decrease in their actual minimal inhibitory concentrations (MICs). AMPainter evolves effective AMPs from membrane‐active SPs with an experimental success rate of 80%. In terms of generation, de novo designed AMP from an inactive random sequence achieves an average MIC of 2.88 µM against four bacteria. In vitro MICs of peptides along the virtual evolutionary path match the predicted scores. Therefore, AMPainter can significantly improve the antimicrobial potency of various peptides, expand the AMP sequence space, and discover novel antimicrobial agents.
PepThink-R1: An LLM-based Framework for Interpretable Cyclic Peptide Optimization
Ruheng Wang, Hang Zhang, Trieu Nguyen, Shasha Feng, Hao-Wei Pang, Xiang Yu, Li Xiao and Peter Zhiping Zhang
[2025-8-20] >> arXiv • paper-read • RL/Cyclic
🔎 Abstract
Designing therapeutic peptides with tailored properties is hindered by the vastness of sequence space, limited experimental data, and poor interpretability of current generative models. To address these challenges, we introduce PepThink-R1, a generative framework that integrates large language models (LLMs) with chain-of-thought (CoT) supervised fine-tuning and reinforcement learning (RL). Unlike prior approaches, PepThink-R1 explicitly reasons about monomer-level modifications during sequence generation, enabling interpretable design choices while optimizing for multiple pharmacological properties. Guided by a tailored reward function balancing chemical validity and property improvements, the model autonomously explores diverse sequence variants. We demonstrate that PepThink-R1 generates cyclic peptides with significantly enhanced lipophilicity, stability, and exposure, outperforming existing general LLMs (e.g., GPT-5) and domain-specific baseline in both optimization success and interpretability. To our knowledge, this is the first LLM-based peptide design framework that combines explicit reasoning with RL-driven property control, marking a step toward reliable and transparent peptide optimization for therapeutic discovery.
Reinforcement Learning-Based Target-Specific De Novo Design of Cyclic Peptide Binders
Fanhao Wang, Tiantian Zhang, Jintao Zhu, Xiaoling Zhang, Changsheng Zhang and Luhua Lai
[2025-8-18] >> Journal of Medicinal Chemistry • GitHub • paper-read • RL/Cyclic
🔎 Abstract
Cyclic peptides are promising therapeutic agents for challenging targets, especially protein–protein interactions. However, computationally designing cyclic peptide binders remains challenging. Here, we present CYC_BUILDER, a reinforcement learning-based framework that assembles peptide fragments and performs efficient cyclization via head-to-tail amide or disulfide bonds, which uses a Monte Carlo Tree Search to guide fragment selection, peptide growth, and structure refinement. We show that CYC_BUILDER was able to successfully regenerate native binding sequences and poses for known cyclic peptide–protein complexes. We have applied CYC_BUILDER to generate cyclic peptide binders for TNFα and found that the design results outperformed those from AfCycDesign and Anchor Extension in binding energy, structural diversity, and efficiency. We experimentally tested the activity of nine designed peptides, and four of them demonstrated potent binding and cellular activity. CYC_BUILDER offers a powerful tool for cyclic peptide discovery with broad applications in therapeutics and synthetic biology.
PepINVENT: Generative peptide design beyond the natural amino acids
Gökçe Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He, Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola Engkvist, Leonardo De Maria
[2025] >> Chem. Sci. • GitHub • paper-read • RL/Molecular AI/AstraZeneca/Noncanonical
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides
Qian Wang, Xiaotong Hu, Zhiqiang Wei, Hao Lu , Hao Liu
[2024-8-27] >> BIB • MondTDSRL • RL/MD
HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer
Xiaopeng Xu, Chencheng Xu, Wenjia He, Lesong Wei, Haoyang Li, Juexiao Zhou, Ruochi Zhang, Yu Wang, Yuanpeng Xiong, Xin Gao
[2024-6-12] >> Bioinformatics • Github • GPT/HELM/Cyclic/RL
De Novo Design of Cyclic Peptide Binders Based on Fragment Docking and Assembling
Zhang, Changsheng, Fanhao Wang, Tiantian Zhang, Yang Yang, Liying Wang, Xiaoling Zhang and Luhua Lai
[2025-4-14] >> JCIM • Cyclic/Luhua Lai/Docking
Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites
Parisa Hosseinzadeh, ..., David Baker
[2021-7-7] >> NC • Peptide_HDACBinders • Tencent • Cyclic/David Baker/MD/Crystal
Amino acid composition drives aggregation during peptide synthesis
Bálint Tamás, Marvin Alberts, Teodoro Laino and Nina Hartrampf
[2026-3-20] >> Nat. Chem. • GitHub • paper-read • SPPS
🔎 Abstract
Peptide aggregation is a long-standing challenge in chemical peptide synthesis, limiting its efficiency and reliability. Although data-driven methods have enhanced our understanding of many sequence-based phenomena, no comprehensive approach addresses so-called non-random difficult couplings (generally linked to aggregation) during solid-phase peptide synthesis. Here we leverage existing peptide synthesis datasets, supplemented with further experimental data, to build a predictive model that deciphers the role of individual amino acids in triggering aggregation. We first identified and experimentally validated composition-dependent aggregation as a stronger predictor than sequence-based patterns. This insight enabled the development of a composition vector representation, allowing insights into the aggregation propensities of individual amino acids. Applying an ensemble of trained models, we predicted the aggregation properties of peptides and recommended the optimized use of aggregation-reducing tools. By elucidating each individual amino acid’s influence, this method holds the potential to accelerate synthesis optimization through existing data, offering a robust framework for understanding and controlling peptide aggregation.
A proximity-driven and regioselective peptide bicyclization (PReP-Bicyc) approach for phage display libraries
Guoqing Jin, Yifan Shi, Shihui Fan, Lai Hoang Son Le, Wenyue Cao, Thuzar Hla Shwe, Zihan Anna Zhang, Demonta D. Coleman, Satyanarayana Nyalata, Joshua Trae Hampton and Wenshe Ray Liu
[2026-10-3] >> Nat Commun • Cyclization/Bicyclic/Phage Display
🔎 Abstract
PReP-Bicyc enables mild, phage-compatible, regioselective peptide bicyclization and screening of PD-1-binding ligands. Published as an early peer-reviewed article.
Silver(I)-mediated amide-to-ester transformation on unprotected peptides for protein chemical synthesis and engineering
Zhenquan Sun, Wai Yin Yau, Huajie Kang, Haiyan Zhou, Xin Liu, Zhibo Han, Hongxiang Wu, Xuechen Li
[2026-9-30] >> Sci Adv • PubMed • Ligation
🔎 Abstract
Chemoselective silver-mediated backbone activation converts unprotected peptide amides to salicylaldehyde esters for protein synthesis, demonstrated with mirror-image histones.
Macrocyclization of native peptides through (thio)urea crosslinking of two amines
Jinyao Liu, Peiru Chen, Meilin Tang, Qiuyu Chen, Yixin Liao, Yu Liu, Shafi Ullah, Jinwu Zhao, Yubo Long, Gong Chen, Wenfang Xiong
[2026-9-2] >> Nat Commun • PubMed • Cyclization/Cyclic/Permeability
🔎 Abstract
Site-selective amine crosslinking generates native-peptide macrocycles with urea or thiourea bridges, with binding, activity and developability evaluation.
Ferricyanide-mediated direct ligation of peptide hydrazides in neutral water
Dongyang Han, Xianglai Zhu, Guiyu Deng, Wei He, Tianyi Zhang, Huasong Ai, Guo-Chao Chu and Lei Liu
[2026-7-24] >> Nat. Synth • Ligation
🔎 Abstract
A ferricyanide-mediated route directly ligates peptide hydrazides under neutral aqueous conditions.
Automated Rapid Synthesis of High-Purity Head-to-Tail Cyclic Peptides via a Diaminonicotinic Acid Scaffold
Feng Wan, Chengrui Hu, Pei Xie, Xingxing Yang, Xin He, Yourong Pan, Zuozhou Ning and Chengxi Li
[2025-12-22] >> J. Am. Chem. Soc. • paper-read • Cyclic/Cyclization/SPPS
Genetically encoded discovery of perfluoroaryl macrocycles that bind to albumin and exhibit extended circulation in vivo
Jeffrey Y. K. Wong, Arunika I. Ekanayake, Serhii Kharchenko, Steven E. Kirberger, Ryan Qiu, Payam Kelich, Susmita Sarkar, Jiaqian Li, Kleinberg X. Fernandez, Edgar R. Alvizo-Paez, Jiayuan Miao, Shiva Kalhor-Monfared, J. Dwyer John, Hongsuk Kang, Hwanho Choi, John M. Nuss, John C. Vederas, Yu-Shan Lin, Matthew S. Macauley, Lela Vukovic, William C. K. Pomerantz and Ratmir Derda
[2023-9-13] >> Nat Commun • paper-read • Cyclic
🔎 Abstract
Peptide-based therapeutics have gained attention as promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. In this paper, we report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine S N Ar chemistry, with decafluoro-diphenylsulfone ( DFS ). Testing of the binding of the selected peptides to albumin identified SICRFFC as the lead sequence. We replaced DFS with isosteric pentafluorophenyl sulfide ( PFS ) and the PFS -SICRFFCGG exhibited K D = 4–6 µM towards human serum albumin. When injected in mice, the concentration of the PFS -SICRFFCGG in plasma was indistinguishable from the reference peptide, SA-21. More importantly, a conjugate of PFS -SICRFFCGG and peptide apelin-17 analogue (N 3 -PEG 6 -NMe17A2) showed retention in circulation similar to SA-21; in contrast, apelin-17 analogue was cleared from the circulation after 2 min. The PFS -SICRFFC is the smallest known peptide macrocycle with a significant affinity for human albumin and substantial in vivo circulation half-life. It is a productive starting point for future development of compact macrocycles with extended half-life in vivo.
Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases
Emre F Bülbül, Seounggun Bang, Kevin George, Gabriele Bianchi, Prateek Raj, Seonyong Chung, Vincent Pauline, Ramon Hochstrasser, Hannah A Minas, Walid A M Elgaher, Andreas M Kany, Anna K H Hirsch, Steven Schmitt, Dirk W Heinz, Olga V Kalinina, Dietrich Klakow, Kenan A J Bozhüyük
[2026-9-22] >> Nat Commun • PubMed • AI/Biosynthesis/ESM/ProteinMPNN
🔎 Abstract
Generative models design thiolation domains that remain functional in engineered non-ribosomal peptide synthetase assembly lines, supported by in vivo production assays.
Signaling by a tyrosine-sulfated peptide balances growth and osmotic stress response in rice
Yejin Shim, Ellen Y Rim, Jin C-Y Liao, Myeong-Je Cho, George Austin, Patrick W Carlos, Sameer S Kulkarni, Richard J Payne, Maria Florencia Ercoli, Pamela C Ronald
[2026-10-1] >> Proc Natl Acad Sci U S A • PubMed • Plant Peptides
🔎 Abstract
Genetic and transcriptomic experiments identify rice OsPSY8 sulfated-peptide signaling as a regulator of the balance between root growth and osmotic-stress adaptation.
Positive modulation of glucagon-like peptide 1 (GLP-1) receptor by endogenous haemorphins with implications in glucose metabolism and diabetes
Farheen Badrealam Khan, Rwdah Mohamed Alameri, Yasir S Raouf, Damien Maurel, Naiem Ahmad Wani, Anatoliy Shmygol, Irfa Anwar, Heng B See, Elizabeth K M Johnstone, Elodie Dupuis, Eric Trinquet, Kevin D G Pfleger, Emilia Oueis, Mohammed Akli Ayoub
[2026-9-29] >> Br J Pharmacol • PubMed • Binding/Metabolism
🔎 Abstract
Endogenous LVV-haemorphin 7 modulates GLP-1 receptor signaling in cellular models, with functional assays and computational binding analysis supporting a peptide-dependent mechanism.
Structural and functional basis of antinociceptive action of χ-conotoxin AoIA at the noradrenaline transporter
Oliver J. V. Belleza, Heng Zhang, Helmut Schmidhammer, Tye I. Gonzalez, Cosmin I. Ciotu, Nataša Tomašević, Carlo Martin M. Ocampo, Jomari C. Fernando, Johannes Koehbach, Paula Schwarz, Mounaf Al Makhlouf, Gabor Tajti, Simon Hasinger, Nina Kastner, Orcun Avsar, Bernhard Retzl, Kathrin Jäntsch, Yi Jiang, Roland Hellinger, Michael J. M. Fischer, K. Johan Rosengren, Christian W. Gruber, Aaron Joseph L. Villaraza, Thomas Stockner, Mariana Spetea, H. Eric Xu and Harald H. Sitte
[2026-7-21] >> Nat Struct Mol Biol • Conotoxin/Cryo-EM/NMR/Neuroscience
🔎 Abstract
Pharmacological and structural studies characterize conotoxin AoIA inhibition of the noradrenaline transporter and antinociceptive activity in a mouse inflammatory-pain model.
Computationally Guided Engineering of Short Peptides for Targeted RNA-Loaded pBAE Nanoparticles
Vladimir Stamenković, Mislav Brajković, Coral Garcia-Fernandez, Salvador Borrós, Xevi Biarnés, Cristina Fornaguera
[2026-10-2] >> ACS Biomater Sci Eng • PubMed • Delivery/Bioconjugation/mRNA
🔎 Abstract
Computationally selected short targeting peptides are conjugated to RNA-loaded polymeric nanoparticles; peptide orientation and sequence are evaluated for transfection in a human monocytic cell model.
An agent-guided peptide hydrogel bio-stabilizer clamps pericellular viscoelastic drift
Xiao Wei, Liqiang Zhang, Zhuo Chang, Fan Ding, Ziyan Qu, Jianghao Chen, Guangkui Xu, Wangxiao He, Wenjia Liu
[2026-7-27] >> Nat Commun • PubMed • Pipeline/Hydrogel
🔎 Abstract
A rule-based agent workflow designs ViscoClamp peptide hydrogels to stabilize glycation-driven pericellular mechanics and support bone repair in animal models.
A self-assembled peptide forms α-helical nanopores for ultrasensitive biomarker profiling
Varsha Shaji, Rajeev Jain, Neethu Puthumadathil, Kalyanashis Jana, Vedasmiritha T S, Ulrich Kleinekathöfer, Krishnananda Chattopadhyay, Kozhinjampara R Mahendran
[2026-8-28] >> Nat Nanotechnol • PubMed • Self-Assembly/Noncanonical
🔎 Abstract
Peptide-based alpha-helical nanopores with engineered diameters enable single-molecule biomarker sensing and distinguish heterogeneous protein assemblies.
PeptiVerse: A unified platform for therapeutic peptide property prediction
Yinuo Zhang, Sophia Tang, Tong Chen, Elizabeth Mahood, Sophia Vincoff, Pranam Chatterjee
[2026-7-16] >> Nat Commun • high • PubMed • PLM/ESM/Noncanonical/Permeability
🔎 Abstract
PeptiVerse supports therapeutic peptide property evaluation from amino acid sequences or chemically modified peptide SMILES using pretrained representations and task-specific predictors.
CABS-flex standalone 3: an open command-line platform for protein flexibility simulation, peptide structure modeling, and protein-peptide docking
Chandran Nithin, Karol Wroblewski, Piotr Szukalo, Ayomide Fasemire, Aleksander Kuriata, Mateusz Kurcinski, Andrzej Kolinski, Sebastian Kmiecik
[2026-06-23] >> arXiv • GitHub • Docking/AI/Cyclic
🔎 Abstract
Preprint: CABS-flex standalone 3 integrates coarse-grained flexibility simulation, peptide structure modeling, flexible docking and all-atom reconstruction in a Python command-line package.
PEP-EDIT: a web server for the 3D generation and interactive editing of complex peptides
Nicolas Chevrollier, Alexis Dougha, Celine Ye, Dirk Stratmann, Gautier Moroy, Julien Rey, Samuel Murail and Pierre Tufféry
[2026-5-14] >> Nucleic Acids Research • paper-read
🔎 Abstract
In recent years, the development of peptide drugs has seen significant growth. These molecules often go beyond simple linear chains composed of the standard 20 amino acids. Peptide drugs frequently incorporate non-standard amino acids, non-amino components, and can exhibit mono- or multicyclic structures, branching, and other complex topologies. Consequently, there is a growing need for accessible tools that allow researchers to easily generate and modify 1D, 2D, and 3D representations of these complex peptides, serving as a starting point for further optimization. PEP-EDIT was created to meet this need. It offers a user-friendly, interactive web interface for generating complex peptide representations from 1D BILN (Boehringer Ingelheim Line Notation) sequences, using a customizable monomer library. Building on the pyPept library, PEP-EDIT enhances its functionality with options such as pH-dependent protonation and simplified specification of conformational constraints. The platform leverages interactive 2D and 3D visualizations to guide peptide design, offers intuitive management of monomers and 3D models, and includes collaborative and interactive visualization tools. PEP-EDIT is available at https://pep-edit.rpbs.univ-paris-diderot.fr. This website is free and open to all users and there is no login requirement.
FakeRotLib: Expedient Noncanonical Amino Acid Parametrization in Rosetta
Eric W. Bell, Benjamin P. Brown and Jens Meiler
[2025-8-11] >> J. Chem. Inf. Model. • GitHub • paper-read • RosettaCommons/Noncanonical
cyclicpeptide: a Python package for cyclic peptide drug design
Liu Yang, Suqi Cao, Lei Liu, Ruixin Zhu and Dingfeng Wu
[2025-1-9] >> Briefings in Bioinformatics • GitHub • paper-read • Cyclic
🔎 Abstract
The unique cyclic structure of cyclic peptides grants them remarkable stability and bioactivity, making them powerful candidates for treating various diseases. However, the lack of standardized tools for cyclic peptide data has hindered their potential in today’s artificial intelligence–driven efficient drug design landscape. To bridge this gap, here we introduce a Python package named cyclicpeptide specifically for cyclic peptide drug design. This package provides standardized tools such as Structure2Sequence, Sequence2Structure, and format transformation to process, convert, and standardize cyclic peptide structure and sequence data. Additionally, it includes GraphAlignment for cyclic peptide–specific alignment and search and PropertyAnalysis to enhance the understanding of their drug-like properties and potential applications. This comprehensive suite of tools aims to streamline the integration of cyclic peptides into modern drug discovery pipelines, accelerating the development of cyclic peptide–based therapeutics.
NMR-Guided Fragment Screening Identifies Privileged Noncanonical Amino Acids for mRNA Display
Abdul J Castillo, Clark A Jones, Alba C Dutra, Chelsea A Makovsky, Sandeep Lohan, Bipasana Shakya, Sara H Walters, Brian Fuglestad, Matthew C T Hartman
[2026-9-28] >> Chembiochem • PubMed • NMR/Screening/Noncanonical/mRNA
🔎 Abstract
NMR-guided fragment screening identifies noncanonical tryptophan monomers for mRNA display and tests their translation compatibility and improved MDM2-binding enrichment.
Integrated abundance analysis and artificial intelligence-assisted screening reveal novel antihypertensive milk-derived tripeptides
Chiao-Che Chen, Yun-Jhu Hou, Hsin-Yi Lo, Mei-Li Stevens, Chien-Yun Hsiang, Tin-Yun Ho
[2026-9-22] >> Food Chem • PubMed • AI/Screening/Metabolism
🔎 Abstract
An integrated computational-experimental workflow prioritizes milk-derived tripeptides and evaluates antihypertensive activity in spontaneously hypertensive rats.
High-Throughput Identification and Characterization of LptDE-Binding Bicycle Peptides Using Phage Display and Cryo-EM
Shenaz Allyjaun, Emily Dunbar, Steven W. Hardwick, Sarah Newell, Finn Holding, Catherine E Rowland, Megan A. St. Denis, Simone Pellegrino, Gustavo Arruda Bezerra, Nikolaos Bournakas, Dimitri Y. Chirgadze, Lee Cooper, Giulia Paris, Nick Lewis, Peter Brown, Michael J. Skynner, Michael J Dawson, Paul Beswick, Julia Hubbard, Bert van den Berg and Hector Newman
[2025-10-6] >> Journal of Medicinal Chemistry • paper-read • Cyclic/Bicyclic/Phage Display/Cryo-EM/Binding
🔎 Abstract
The lipopolysaccharide (LPS) transport (Lpt) system in Gram-negative bacteria maintains the integrity of the asymmetric bacterial outer membrane (OM). LPS biogenesis systems are essential in most Gram-negative bacteria, with LptDE responsible for the delivery of LPS to the outer leaflet of the OM. As an externally accessible, essential protein, LptDE offers a promising target for inhibitor development without the need for cellular penetration. However, there are no direct inhibitors of E. coli LptDE, and drug discovery is made challenging since it is a membrane target without a conventional active site. Here, the bicycle phage display platform was used in combination with cryogenic-electron microscopy (cryo-EM) and surface plasmon resonance to identify and map bicyclic peptide binders to Shigella flexneri LptDE (SfLptDE). Four distinct epitopes with unique bicycle molecule binding motifs were identified across the SfLptD β-barrel. This method represents a streamlined workflow for the identification and prioritization of hit molecules against LptDE.
Prohormone cleavage prediction uncovers a non-incretin anti-obesity peptide
Laetitia Coassolo, Niels B. Danneskiold-Samsøe, Quennie Nguyen, Amanda Wiggenhorn, Meng Zhao, David Cheng-Hao Wang, David Toomer, et al.
[2025-3-5] >> Nature
A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries
Arup Mondal, Bhumika Singh, Roland H. Felkner, Anna De Falco, GVT Swapna, Gaetano T. Montelione, Monica J. Roth, and Alberto Perez
[2024-6-10] >> Angew • high • AF
Validation of a New Methodology to Create Oral Drugs beyond the Rule of 5 for Intracellular Tough Targets
Atsushi Ohta, Mikimasa Tanada, Shojiro Shinohara, Yuya Morita, Kazuhiko Nakano, Yusuke Yamagishi, Ryusuke Takano, Shiori Kariyuki, Takeo Iida, Atsushi Matsuo, Kazuhisa Ozeki, Takashi Emura, Yuuji Sakurai, Koji Takano, Atsuko Higashida, Miki Kojima, Terushige Muraoka, Ryuuichi Takeyama, Tatsuya Kato, Kaori Kimura, Kotaro Ogawa, Kazuhiro Ohara, Shota Tanaka, Yasufumi Kikuchi, Nozomi Hisada, Ryuji Hayashi, Yoshikazu Nishimura, Kenichi Nomura, Tatsuhiko Tachibana, Machiko Irie, Hatsuo Kawada, Takuya Torizawa, Naoaki Murao, Tomoya Kotake, Masahiko Tanaka, Shiho Ishikawa, Taiji Miyake, Minoru Tamiya, Masako Arai, Aya Chiyoda, Sho Akai, Hitoshi Sase, Shino Kuramoto, Toshiya Ito, Takuya Shiraishi, Tetsuo Kojima and Hitoshi Iikura
[2023-10-24] >> J. Am. Chem. Soc. • paper-read • Cyclic
Development of Orally Bioavailable Peptides Targeting an Intracellular Protein: From a Hit to a Clinical KRAS Inhibitor
Mikimasa Tanada, Minoru Tamiya, Atsushi Matsuo, Aya Chiyoda, Koji Takano, Toshiya Ito, Machiko Irie, Tomoya Kotake, Ryuuichi Takeyama, Hatsuo Kawada, Ryuji Hayashi, Shiho Ishikawa, Kenichi Nomura, Noriyuki Furuichi, Yuya Morita, Mirai Kage, Satoshi Hashimoto, Keiji Nii, Hitoshi Sase, Kazuhiro Ohara, Atsushi Ohta, Shino Kuramoto, Yoshikazu Nishimura, Hitoshi Iikura and Takuya Shiraishi
[2023-7-18] >> J. Am. Chem. Soc. • high • paper-read • Cyclic
BindCraft: one-shot design of functional protein binders
Martin Pacesa, Lennart Nickel, ..., Sergey Ovchinnikov, Bruno E. Correia
[2025-8-27] >> Nature • high • GitHub • 公众号 / paper-read
🔎 Abstract
BindCraft is an open-source, automated pipeline for <em>de novo</em> protein binder design, achieving experimental success rates of 10-100%. Using deep learning models like AlphaFold2, BindCraft generates high-affinity binders without the need for high-throughput screening or prior knowledge of binding sites. It has been successfully applied to challenging targets, including cell-surface receptors, allergens, and CRISPR-Cas9. In one example, the binders reduced IgE binding to birch allergens in patient samples, showcasing its potential in therapeutics, diagnostics, and biotechnology.
A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy
Bohan Ma, Donghua Liu, Zhe Wang, Dize Zhang, Yanlin Jian, et. al.
[2024-6-5] >> JMC • 公众号 • PROTAC
The RaPID Platform for the Discovery of Pseudo-Natural Macrocyclic Peptides
Yuki Goto & Hiroaki Suga
[2021-9-10] >> Acc. Chem. Res. • RaPID/Cyclic/Hiroaki Suga/mRNA
Self-Assembling Peptide-Adjuvant Conjugate (SaPAC) Platform for Precision Cancer Immunotherapy
Yang-Fan Wu, Jing-Chu Hu, Ye-Fan Hu, Wen-Jun Li, Li Rong, Ren-Hao Li, Yuan Yao, Lehan Hu, Xiao-Lei Wang, Bao-Zhong Zhang, Yicheng Lu, Shing-Fung Chow, Canhui Su, Thomas Yau, Clive Yik-Sham Chung, Jian-Dong Huang
[2026-10-2] >> Adv Sci (Weinh) • PubMed • Bioconjugation/Self-Assembly/Immunology/Oncology
🔎 Abstract
The SaPAC peptide-adjuvant conjugate platform forms nanoparticles that enhance antigen presentation and antitumor immune responses in mouse cancer models.
Contributions and suggestions are warmly welcome! Community Values, Guiding Principles, and Commitments for the Responsible Development of AI for Peptide Design
@article{zhaiArtificialIntelligencePeptidebased2025,
title = {Artificial Intelligence in Peptide-Based Drug Design},
author = {Zhai, Silong and Liu, Tiantao and Lin, Shaolong and Li, Dan and Liu, Huanxiang and Yao, Xiaojun and Hou, Tingjun},
date = {2025-02-01},
journaltitle = {Drug Discovery Today},
volume = {30},
number = {2},
pages = {104300},
issn = {1359-6446},
doi = {10.1016/j.drudis.2025.104300}
}