Modeling of Antibody Framework Regions

Creative Biolabs is an undisputed global leader in the rapidly emerging market of antibody structure modeling. With our professional scientists hammering at Creative Biolabs, we offer accurate antibody 3D-structure models with our high-quality framework regions modeling service. We are confident to deliver you the best modeling service to meet your individual requirements.

Framework Region of Antibody

Antibodies are composed of four polypeptides– two heavy chains and two light chains joined to form a "Y" shaped molecule. The variable region contained 110-130 amino acids, offers the antibody its specificity for binding antigen. This region is further divided into two regions: hypervariable (HV) and framework (FR) regions. Three hypervariable regions- HV 1, 2 and 3 exist in light and heavy chains. Four FR regions separate the HV regions, and they have relatively more stable amino acids sequences. The FR regions generate a beta-sheet structure which plays as a scaffold to hold the HV regions in position to contact antigen. Besides, the FR regions are highly conserved, with most of the variability occurring in the HV regions, also called complementarity-determining regions (CDRs).

Fig 1. The structure of an antibody variable region. The framework regions are shown in gray, the hypervariable loops are shown in red and the antigen is shown in cyan. (Marcatili, P., 2014)

Currently, antibody-based therapeutics have become increasingly powerful in the treatment of cancer and other diseases. It is an essential step to construct computational models in the antibody design process. The resulted 3D models enable researchers to explore antibody features, including antigenicity, stability, solubility, aggregation, and so on.

Framework Regions Modeling

Creative Biolabs offers high-quality framework regions modeling service. First of all, the template for the target antibody is determined via aligning the target sequence against sequences in a precurated database of antibodies derived from the Protein Data Bank (PDB). By using a Hidden Markov Model, potential template is selected via calculating the sequence similarity and identity against the target Fv FR regions, without the CDR loops.

Fig .2 Part A shows the framework RMSDs (peptide carbonyl) of potential templates for the eleven targets. Part B shows the framework RMSD (peptide carbonyl) of the top five templates for each of the targets vs. sequence similarity. (Fasnacht, M., 2014)

Following, we used three different approaches to build models based on the framework templates.

• The first method is to construct a model based on a single Fv framework template.
• The second method is to construct a model by using a chimeric template. The template was derived from the individual VL and VH templates on account of a third interface template which comprised both VL and VH domains to identify the relative spatial orientation of the individual VL and VH templates.
• The third method is to construct a model depended on five overall Fv framework templates. The model is constructed on account of a multiple sequence alignment of all five templates to the target sequence using the ability of PreciAb™ to build models based on multiple templates. PreciAb™ takes advantages of an additive distance restraint function which peaks at the equivalent distance between atoms in each template.

With our advanced framework regions modeling service, designing and engineering novel antibodies with desired properties is available. Our service will contribute greatly to the success of your project. We also provide other antibody structure modeling services. Please contact us for more information and a detailed quote.

References

1. Fasnacht, M., (2014). “Automated antibody structure prediction using Accelrys tools: Results and best practices.” Proteins: Structure, Function, and Bioinformatics, 82(8), 1583-1598.
2. Sircar, A., Kim, (2009). “RosettaAntibody: antibody variable region homology modeling server.” Nucleic acids research, 37(suppl_2), W474-W479.
3. Marcatili, P., (2014). “Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server.” Nature protocols, 9(12), 2771-2783.

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