Peptide Structure Modeling Service

CD ComputaBio overcomes the limitations of traditional experimental methods by providing clients with high-quality three-dimensional peptide structure modeling services. This facilitates the design and development of novel peptide molecules, contributing to advancements in the industrial, medical, and biological fields.

Introduction to Peptide Structure Modeling

Traditional methods for analyzing the tertiary structure of peptide molecules often suffer from limitations such as high labor intensity, significant costs, and the potential for solvents used to interfere with the structure. Therefore, computational peptide modeling has emerged. Drawing on strategies from protein three-dimensional structure prediction, this approach utilizes methods like homology modeling, threading, and ab initio prediction, combined with modeling tools, to achieve accurate prediction of peptide molecule tertiary structures.

Fig.1 Structure prediction of native cyclic peptides using AfCycDesign. (Rettie S A, et al., 2023)

In Silico Tools and Servers for Peptide Structure Modeling

Tools & Servers	Brief Description	References
PepLook	An innovative in silico tool for predicting the structure, polymorphism, and stability of peptides.	Thomas et al. (2009)
PepSite	A tool for accurate prediction of peptide binding sites on protein surfaces.	Trabuco et al. (2012)
PEPstrMOD	Allowing users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allowing extended simulation of predicted peptides.	Singh et al. (2015)
PEP-FOLD4	De novo peptide structure prediction considering pH and ionic strength variation.	Rey et al. (2023)

Our Services

CD ComputaBio's peptide structure modeling services are dedicated to providing clients with comprehensive and precise three-dimensional structure prediction and conformational analysis, offering insights for peptide design and optimization. We predict various types of peptide structures for clients, including but not limited to:

By Structure

Linear Peptides Structure Modeling
Stapled Peptides Structure Modeling
Peptidomimetics Structure Modeling

Cyclic Peptides Structure Modeling
Modified Peptides Structure Modeling
Peptide-Drug Conjugates Structure Modeling

By Function

Antimicrobial Peptides Structure Modeling
Hormone Peptides Structure Modeling
Antihypertensive Peptides Structure Modeling
Anti-Cancer Peptides Structure Modeling

Cell-Penetrating Peptides Structure Modeling
Neuropeptides Structure Modeling
Antioxidant Peptides Structure Modeling
More

Methods for Peptide Structure Modeling

Homology Modeling

Utilizing homologous proteins or peptides with known structures as templates, the three-dimensional structural model of the target peptide is constructed by aligning sequence and structural information.

Threading

By aligning the target peptide sequence to a database of known protein fold structures, the optimal matching template is identified to predict the three-dimensional structure of the target peptide.

Ab Initio Prediction

Without relying on templates, the low-energy folding state of the target peptide is predicted based solely on physicochemical principles and energy minimization algorithms.

Deep Learning

Using deep learning algorithms, patterns are learned from the sequences and structural data of known peptides to achieve peptide structure prediction, such as the application of models like AlphaFold.

At CD ComputaBio, we specialize in providing advanced peptide structure modeling services tailored to meet your specific research needs. Contact us today to discuss your peptide structure modeling requirements and discover how our solutions can accelerate your research and development efforts.

References:

Rettie, S A.; et al. Cyclic peptide structure prediction and design using AlphaFold[J]. bioRxiv. 2023.
Thomas, A.; et al. PepLook: an innovative in silico tool for determination of structure, polymorphism and stability of peptides[C]//Peptides for Youth: The Proceedings of the 20th American Peptide Symposium. New York, NY: Springer New York. 2009: 459-460.
Trabuco, L G.; et al. PepSite: prediction of peptide-binding sites from protein surfaces[J]. Nucleic acids research. 2012, 40(W1): W423-W427.
Singh, S.; et al. PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues[J]. Biology direct. 2015, 10: 1-19.
Rey, J.; et al. PEP-FOLD4: A pH-dependent force field for peptide structure prediction in aqueous solution[J]. Nucleic acids research. 2023, 51(W1): W432-W437.

For research use only. Not intended for any clinical use.

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