Disulfide Bond Design of Miniproteins

Miniproteins, also known as peptide therapeutics or constrained peptides, represent a promising class of molecules with diverse biomedical applications. These molecules are characterized by their compact size, high stability, and specific target binding, making them valuable platforms for drug development and biotechnological innovation. However, the inherent flexibility and susceptibility to degradation pose challenges in harnessing their full potential. At CD ComputaBio, we specialize in leveraging the power of artificial intelligence to revolutionize the design of disulfide bonds in miniproteins. Our innovative approach combines cutting-edge computational techniques with deep expertise in molecular biology to engineer miniproteins with enhanced structural stability, activity, and bioavailability.

Our Services

Fig 1: Disulfide bond geometry

Custom Disulfide Bond Design

Leveraging AI algorithms and molecular modeling simulations, we customize and optimize disulfide bond patterns to impart desired structural stability and bioactivity to the miniproteins, tailored to the intended application.

Fig 2: Our services of disulfide bond design of miniproteins

Predictive Computational Analysis

Using advanced computational tools, we conduct comprehensive structural and energetic analyses to predict the impact of proposed disulfide bond modifications on the overall stability and conformation of miniproteins.

Fig 3: Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation

In instances where precise understanding of biochemical mechanisms is essential, we employ QM/MM simulations to elucidate the electronic and energetic properties of disulfide bond formation within miniproteins.

Our Analysis Methods

Our approach relies on a synergistic blend of computational techniques and bioinformatics tools to guide the rational design of disulfide bonds in miniproteins:

  • Machine Learning Algorithms
We harness machine learning models trained on extensive miniprotein and disulfide bond databases to identify patterns and correlations, enabling the prediction of optimal disulfide bond placements for enhanced stability and activity.
  • Rosetta Software Suite
Utilizing the Rosetta suite of protein modeling and design tools, we perform detailed structural refinement and energy minimization to generate high-fidelity models of miniproteins with engineered disulfide bonds.
  • Bioinformatic Analysis
Our team conducts extensive bioinformatic analyses to leverage sequence and structural homology data, facilitating the identification of conserved motifs and optimal disulfide bond placements based on evolutionary insights.

Result Delivery

We are committed to delivering comprehensive and actionable results tailored to our clients' needs:

  • Detailed Reports
    Clients receive detailed reports outlining the rationale behind the chosen disulfide bond designs, the computational analyses performed, and the predicted structural and energetic profiles of the engineered miniproteins.
  • Visualizations and Models
    We provide 3D visualizations, molecular models, and interactive simulations to elucidate the structural features and dynamic behavior of the designed miniproteins, aiding in the interpretation of the computational predictions.

With a multi-disciplinary team comprising computational biologists, bioinformaticians, and molecular modelers, we seamlessly integrate diverse expertise to deliver comprehensive insights and solutions, ensuring rigorous and holistic design strategies. If you are interested in our services or have any questions, please feel free to contact us.


  • Dombkowski A A, Sultana K Z, Craig D B. Protein disulfide engineering[J]. FEBS letters, 2014, 588(2): 206-212.
  • Böselt L, Thürlemann M, Riniker S. Machine learning in QM/MM molecular dynamics simulations of condensed-phase systems[J]. Journal of Chemical Theory and Computation, 2021, 17(5): 2641-2658.


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