Affinity Maturation

Antibody therapeutics are designed against a target protein, and are increasingly being used in cancer therapy. High affinity and selectivity are critical issues for antibody therapeutic capacity. It is critical to be able to understand how certain amino acid substitutions will change the binding energy (affinity maturation). Docking algorithms have expanded into the protein–protein domain with current standards including ZDOCK, ClusPro, Haddock, RosettaDock and several others. Common to these methods are sampling techniques such as Monte Carlo or fast-Fourier transform, which aim to generate structural conformations that can be scored with a function which estimates the energetic favorability of two docked structures. Rational engineering methods can be applied with reasonable success to optimize physicochemical characteristics of antibody drugs.

Methods for improving the binding affinity of antibody drugs

CDR rearrangement
CDR walking mutagenesis
DNA recombinant
Hot-spot mutagenesis
Parsimonious mutagenesis
Random mutagenesis by error-prone PCR

Various different mutagenesis strategies have proven useful to enhance the affinity of candidate therapeutic antibodies obtained by phage display. But it is practically unfeasible to generate all possible (combinations of) CDR residue mutants. The actual binding site usually involves multiple CDRs and exact mapping is a laborious task. Application of in silico analysis and prediction methods to antibody variable fragment (Fv) regions may be helpful. A set of algorithms, named affinity maturation after the similar process in B-cell response, attempts to determine if mutations or modifications to the binding partners have an impact on binding affinity or energetic favorability or generate mutations or sequences which increase the binding affinity of the partners.

In silico procedures
Construct models.
Dock at least the epitope fragment into the binding sites.
Combine theoretic and experimental information to make rational proposals.

Services items

Services Details Deliverables Cycle
  • DNA Shuffling
  • Library Construction and Screening
    Antibody Production and Characterization
  • In silico analysis and prediction
  • Antibody sequence and sequencing report
  • Top 1-3 of the optimized antibodies
  • Full analysis report (PDF)
Depends on the time you need to simulate and the time required for the system to reach equilibrium.

Advantages

Mature phage display platform.
Computer-guided homology modeling.
Conformational optimization methods.
Precise affinity evaluation.
Molecular docking and dynamics simulation methods.
Competitive pricing.

Why choose us?

  • Our team has extensive experience in molecular dynamics simulation of drugs and biological systems.
  • Our researchers can quickly understand the situation of the project through simple descriptions, and quickly establish a feasible research plan according to the needs of the client.
  • In addition, we cooperate with a number of universities or research institutions, and can consult industry experts for their opinions and suggestions on some complex issues.

References

  • Rodrigo Barderas, et al. Affinity maturation of antibodies assisted by in silico modeling. PNAS. 2008 105 (26) 9029-9034.
  • Jordan Graves, et al. A Review of Deep Learning Methods for Antibodies. Antibodies. 2020.

Online Inquiry

Please submit a detailed description of your project. We will provide you with a customized project plan to meet your research requests. You can also send emails directly to inquiry@protheragen.com for inquiries.

Please input "protheragen" as verification code.

Copyright © 2005-2021 [ Protheragen ] All Rights Reserved.