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Structure-based Target Identification Service
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Structure-based Target Identification Service

Target identification is one of the key challenges in peptide drug development. Accurate identification of disease-related target structures is crucial for developing efficient and specific peptide drugs. CD ComputaBio, with its deep expertise and advanced computational platform, provides structure-based peptide target identification services to help you overcome the challenges of target identification and accelerate the process of innovative drug discovery.

Introduction to Structure-based Peptide Target Identification

With the increasing number of macromolecular structures stored online in specialized databases, such as the Protein Data Bank (PDB), the three-dimensional characteristics of proteins are playing an increasingly important role in computational methods. Structure-based peptide target identification utilizes known three-dimensional structures of target proteins, employing techniques like molecular docking and molecular dynamics simulations, to predict the binding strength between peptide ligands and assumed macromolecules, thereby identifying potential targets.

Fig. 1 Discrimination of interacting (n = 1481) and non-interacting (n = 5694) proteins.Fig. 1 Discrimination of interacting (n = 1481) and non-interacting (n = 5694) proteins. (Bryant P, et al., 2022)

Our Services

In today's booming era of computational biology, CD ComputaBio, as a pioneer in this field, is committed to providing cutting-edge structure-based peptide target identification services to clients worldwide. This service, supported by a professional team of computational biologists, is capable of offering customized solutions to efficiently assist clients in identifying peptide targets.

Workflow of Structure-based Peptide Target Identification

Database Construction

Collect and integrate biomacromolecular structural data from key protein databases to provide a reliable foundation for subsequent structure-based target screening.

Initial Target Screening

Utilize methods such as molecular docking to simulate the interaction modes and binding affinities between peptide drugs and proteins, quickly narrowing the target range and screening out potential candidate targets.

Refined Target Screening

Through molecular dynamics simulations and binding free energy calculations, conduct in-depth analysis of candidate targets and peptide drug interaction modes and stabilities, identifying potential targets for peptide drugs.

Approaches to Structure-based Peptide Target Identification

  • Reverse Pharmacophore Modeling

By comparing pharmacophore models or testing a single drug against multiple pharmacophores, macromolecules targeted by compounds can be screened out, which provides an efficient and effective strategy for peptide target identification.

  • Inverse Virtual Screening

Docking peptide drugs with a large number of protein structures to identify the most likely interacting partners. This approach is particularly useful when the mechanism of action of peptide drugs is unknown or when exploring their multi-target effects.

Why Choose Us?

Advanced Computational Platform

Utilizing high-performance computing clusters and professional software platforms to handle complex molecular simulations and large-scale data analysis.

Efficient Project Management

By developing detailed project plans, arranging timelines reasonably, and optimizing service processes, we ensure timely project delivery.

Excellent Customer Service

Maintaining close communication with clients, providing regular updates on project progress and key results, and offering professional consultation and technical support.

CD ComputaBio consistently upholds the principles of "Innovation, Expertise, and Efficiency," and is committed to providing clients with high-quality structure-based peptide target identification services. Our team of experts is dedicated to addressing your inquiries and offering professional advice. Please feel free to contact us for further information.

References:

  1. Bryant, P.; et al. Improved prediction of protein-protein interactions using AlphaFold2[J]. Nature communications. 2022, 13(1): 1265.
For research use only. Not intended for any clinical use.
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