Peptide Hormone Design Services

The rapid development of artificial intelligence, enhanced computational power, and improvements in computational modeling pipelines are profoundly transforming structural biology. Against this backdrop, CD ComputaBio offers computation-driven peptide hormone design services. We utilize cutting-edge computational technologies to address the challenges posed by the flexibility of peptide molecules, providing solutions for the design and optimization of peptide hormones.

Introduction to Peptide Hormones

Peptide hormones have been widely studied in the field of life sciences and used in therapeutic applications. Peptide hormones vary greatly in molecular weight, ranging from small molecules such as the tripeptide thyrotropin-releasing hormone (TRH) analogs to long polypeptides such as human insulin (51 residues) and parathyroid hormone (PTH). Currently, there are over 40 peptide-based drugs on the market, such as insulin, atrial natriuretic peptide (ANP), and glucagon-like peptide-1 (GLP-1) analogs. In addition, over 100 new peptide therapies are in clinical trials.

Fig. 1 Sales and structures of the top five selling GLP-1 agonists. (Xiao W, et al., 2025)

Computational Peptide Hormone Design

The experimental identification and characterization of peptide hormones face challenges such as high cost, time consumption, low stability, separation and specificity issues, and post-translational modifications. To address these problems, computational methods can complement and enhance traditional experimental approaches. For example, machine learning, a powerful bioinformatics method, can be used to predict peptide hormones. By leveraging sequence data and advanced algorithms, researchers can uncover new insights into the roles and mechanisms of these crucial biomolecules, laying the foundation for innovative therapeutic strategies.

Fig. 2 Structure analysis and performance testing of BGM0504. (Yuan J, et al., 2024)

Our Services

CD ComputaBio provides computational design services for peptide hormones, analyzing peptide-protein recognition mechanisms to design and optimize peptide drugs. We employ computation-aided rational design technologies, including molecular docking, molecular dynamics simulations, and machine learning, to simulate peptide-protein interaction interfaces and enhance binding affinity and specificity.

By Target Type

• G Protein-Coupled Receptors (GPCRs): GIPR, GCGR, GLP-1R, GLP-2R, GHSR, NPYR, etc.
• Receptor Tyrosine Kinases (RTKs): Insulin Receptor, IGF-1 Receptor, etc.
• Guanylate Cyclase Receptors: NPR-A, NPR-B, etc.
• Cytokine Receptors: GHR, EPOR, etc.

By Action Mechanism

By Workflow

Approaches to Peptide Hormone Design

• Molecular Docking - Predicting the binding modes of peptide hormones with target receptors and analyzing the interaction interfaces to provide a structural basis for peptide hormone design.
• Molecular Dynamics Simulations - Simulating the dynamic interactions between peptide hormones and receptors to reveal the mechanisms of action and guide the design to enhance stability and bioactivity.
• Machine Learning - Utilizing a large amount of peptide hormone sequence and activity data to train predictive models and predict and identify peptide hormones.
• Deep Learning - Deeply mining the features of peptide data to automatically generate novel peptide sequences and accelerate the innovative design of peptide hormones.

CD ComputaBio is committed to advancing peptide design innovation, offering rational peptide hormone design solutions to the biotechnology and pharmaceutical industries, and assisting clients in the development of novel peptides. If you are interested in our services or have any questions, please feel free to contact us.