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Related Concept Videos

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Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
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Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
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Updated: Aug 26, 2025

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
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Antibody apparent solubility prediction from sequence by transfer learning.

Jiangyan Feng1, Min Jiang2, James Shih1

  • 1BioTechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, CA 92121, USA.

Iscience
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Predicting monoclonal antibody (mAb) solubility using protein language models accelerates discovery. The solPredict tool accurately forecasts mAb solubility from sequence alone, reducing experimental costs and enabling faster development of subcutaneous therapeutics.

Keywords:
BioinformaticsComponents of the immune systemComputational chemistry

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Area of Science:

  • Biotechnology
  • Protein Engineering
  • Computational Biology

Background:

  • Developing therapeutic monoclonal antibodies (mAbs) for subcutaneous delivery requires high solubility for concentrated formulations.
  • Experimental screening for mAb solubility is resource-intensive, hindering early-stage discovery.

Purpose of the Study:

  • To develop a computational strategy, solPredict, for predicting mAb apparent solubility.
  • To enable rapid, large-scale screening of mAbs based on sequence information alone.

Main Methods:

  • Utilized embeddings from pretrained protein language models to predict mAb solubility.
  • Trained and validated the solPredict model on a dataset of 220 diverse mAbs using 5-fold cross-validation.
  • Evaluated model performance on an independent test set of 40 mAbs.

Main Results:

  • solPredict demonstrated high correlation with experimental solubility on the independent test set.
  • The model accurately predicted solubility for both IgG1 and IgG4 subclasses.
  • The approach bypasses the need for 3D structure modeling and expert-designed features.

Conclusions:

  • solPredict offers a computationally efficient method for predicting mAb solubility.
  • This tool facilitates rapid, high-throughput screening during early antibody discovery.
  • The sequence-based prediction method reduces material and labor costs associated with experimental screening.