We have developed a biophysical method to reduce the cost for antibody experiments in the fields:
  • antibody-antigen,
  • peptide-protein interaction,
  • protein-protein interaction via data science.
Our method include:
  • modification of antibody flexible chains,
  • stepwise testing of each antibody to antigen,
  • determination of key amino acid residues,
  • range of changes in affinity.
AI
BinomLabs platform include:

How to assess the antibody potency by Experimental methods and AI methods

Fabs+Epitope
Input mutations in Fabs/Epitope
The goal of the AI ​​platform is to replace and reduce the number of intermediate in vitro experiments
InPut Data windows:
1. The fraction of non-dissociated molecules after the reaction and concentration protein-ligand complex

2. Entropy Change: T(delta)S
The case of one-dimensional normal distribution

3. Entropy Change: T(delta)S
The case of multinormal normal distribution

4. Dissociation Constant: Kd, M/L

5. Enthalpy change: delta (P), J

6. The thermal dissociation: Tm

7. Potential energy of electrostatic interaction between all amino acid residues taken in pairs: Wp, J

8. Potential energy of the lower vibrational level: Wp1, J

9. Potential energy ofthe upper vibrational level: Wp2, J

10. Step-by-step verification of the PDB file structure.

Additional calculated parameters:
OutPut/Issued Date after calculations:
Each antibody-antigen complex has its own physical parameters that determine the interaction
Main calculated parameters:
Antibody affinity describes the intensity with which a single antibody molecule binds to its specific epitope in an antigen. This means that under a given concentration of antibody and antigen, a specific number of antigen–antibody complexes are formed. Consequently, antibody affinity is one of the major properties affecting the potency of therapeutic antibodies. Binders with higher affinities may allow lower doses or longer intervals of administration during therapy. Moreover, as antibodies require sophisticated production systems and therapeutic doses, and costs of goods of antibodies are comparably high, a high affinity may affect the commercial success of a therapeutic antibody. The process of in vivo affinity maturation is described as well as strategies for in vitro affinity maturation. Finally, the relation between affinity and efficacy and the determination of antibody affinity are reviewed.
Procedure for finding suitable immunoglobulins
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STEP ONE
1.Determination of three-dimensional complex of the target protein with antibody flexible chain, which is subject to further modification. This should be at least one file with the extension of the PDB obtained by the method of X-ray diffraction analysis or using molecular docking programs.
2.To control the received data, you can choose either of two options:
  • you can use the additional structure of the PDB of antibody-antigen
  • take advantage of previously available data on the mutations performed, alanine scanning of one of the participants of the antibody-antigen complex
3.Our experts check files, adapt them for computational manipulations using Soft Development and Data Science.
STEP TWO.
Performing a verification series of calculations:
obtain data, numerically calculate the results in the form of graphs and diagrams, determine next one's:
  • key amino acid residues of antibody,
  • interaction energies of antibody-antigen complex,
  • changes in affinity and stability of antibody-antigen complex,
  • change in entropy for each replacement of the amino acid residue in the flexible chain of immunoglobulin.
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Our specialists perform the necessary calculations

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STEP THREE.

Comparative price analysis
The step-by-step analysis of each antibody modification is shown in the diagram on the left, as high-affinity antibodies are identified that will be transferred to the antibody synthesis engineering lab.

Conclusion: Experimental and engineering departments will receive preliminary information on the composition and structure of high-affinity antibodies to the antigen, eliminating ineffective antibody variants.


Development and testing of new antibodies using our biophysical software can significantly save money on laboratory research
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