Backbone Design of Miniproteins

Miniproteins, also known as peptide therapeutics, have emerged as a promising class of molecules with significant potential across various biotechnological and pharmaceutical domains. These compact protein-like structures exhibit remarkable stability, specificity, and functionality, making them valuable candidates for drug discovery, molecular recognition, and materials science. At the heart of miniprotein engineering lies the strategic design of their backbones, which directly influences their structural stability, functional properties, and biological activities. Welcome to CD ComputaBio, where we leverage the power of artificial intelligence to revolutionize the field of miniprotein backbone design. Our expertise lies in harnessing cutting-edge computational methods and AI technologies to facilitate the efficient and precise design of miniprotein backbones for a diverse range of applications. Through our innovative services, we empower researchers and organizations to expedite the process of miniprotein engineering, enabling the development of novel therapeutics, biomaterials, and beyond.

Our Services

Fig 1: Design and engineering of miniproteins

Custom Backbone Design

By integrating client-defined structural constraints, physicochemical properties, and functional targets, our AI platform generates a diverse array of backbone designs optimized for stability, biocompatibility, and performance.

Fig 2: Self-driving laboratories to autonomously navigate the protein fitness landscape

Structural Validation and Refinement

Through advanced structure refinement algorithms and energetic analysis, we optimize the geometry, stereochemistry, and energetics of miniprotein backbones, resulting in refined designs poised for further experimental characterization.

Fig 3: Sequence-structure analysis

Sequence-Structure Analysis

Leveraging advanced bioinformatics tools and molecular dynamics simulations, we dissect the sequence-structure relationships to identify key determinants of stability, folding kinetics, and structural motifs.

Fig 4: The atom-based 3D-QSAR model visualization

Property Prediction and Optimization

Using machine learning models and quantitative structure-activity relationship (QSAR) approaches, we predict and optimize key properties of miniproteins, including solubility, aggregation propensity, and immunogenicity.

Our Analysis Methods

Our AI-aided backbone design of miniproteins relies on a suite of sophisticated computational methods and tools, each tailored to address specific aspects of the design process. Some of the key analysis methods we employ include:

  • Monte Carlo Sampling
Through Monte Carlo sampling techniques, we explore the conformational landscape of miniprotein backbones, enabling the efficient sampling of diverse backbone conformations and identification of energetically favorable states.
  • Machine Learning-Based Property Prediction
Leveraging machine learning models, we predict key physicochemical and biophysical properties of miniproteins, facilitating the optimization of their functional characteristics and drug-like attributes.
  • Energy Minimization and Force Field Calculations
We employ energy minimization algorithms and force field calculations to refine and optimize the geometries and energetics of candidate miniprotein backbones, ensuring their structural integrity and compatibility with experimental conditions.

Our Capabilities

CD ComputaBio leverages state-of-the-art AI technologies, including machine learning algorithms, deep learning architectures, and predictive modeling tools, to drive innovation in miniprotein backbone design.

Our AI-aided backbone design services are tailored to address the intricate challenges associated with optimizing miniprotein backbones. By harnessing advanced computational algorithms, molecular modeling, and machine learning techniques, we facilitate the rapid and systematic exploration of vast structural spaces, resulting in the generation of bespoke miniprotein backbones with desired characteristics. If you are interested in our services or have any questions, please feel free to contact us.


  • Ożga K, Berlicki Ł. Design and Engineering of Miniproteins[J]. ACS bio & med Chem Au, 2022, 2(4): 316-327.
  • Rapp J T, Bremer B J, Romero P A. Self-driving laboratories to autonomously navigate the protein fitness landscape[J]. Nature Chemical Engineering, 2024, 1(1): 97-107.
  • Crisan L, Borota A, Bora A, et al. Neonicotinoids Activity Against Cowpea Aphids by Computational Estimation[J]. Iranian Journal of Mathematical Chemistry, 2019, 10(1): 21-44.


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