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Related Concept Videos

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Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Related Experiment Video

Updated: Mar 21, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Trans-GP: Uncertainty-Calibrated Antibody-Antigen Binding Classification Using Protein Language Models.

Lilan Lv1,2, Xueli Meng3, Jinxiong Zhang3

  • 1Guangxi Subtropical Crops Research Institute, Laboratory for Quality and Safety Risk Assessment of Agricultural Products (Nanning), Ministry of Agriculture and Rural Affairs, Nanning 530001, China.

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Trans-GP, a new framework, accurately predicts antibody-antigen binding and quantifies prediction uncertainty. This improves confidence in data-driven antibody discovery and prioritization.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Machine learning in drug discovery

Background:

  • Predicting antibody-antigen binding is crucial for drug development.
  • Current methods lack reliable confidence estimates for data-driven predictions.

Purpose of the Study:

  • To develop a sequence-driven framework for antibody-antigen binding prediction.
  • To integrate uncertainty calibration with predictive modeling.

Main Methods:

  • Utilized frozen protein language model embeddings.
  • Employed a Gaussian process classifier for joint classification and calibration.
  • Validated on benchmark datasets: SAbDab, SKEMPI2.0, and ABbind.

Main Results:

  • Achieved competitive predictive performance across datasets.
  • Demonstrated superior uncertainty calibration compared to conventional neural networks.
  • Trans-GP provides statistically reliable probabilistic confidence estimates.

Conclusions:

  • Trans-GP offers a robust approach for antibody-antigen binding prediction.
  • The framework enhances reliability in screening and prioritizing antibody candidates.
  • Improved uncertainty quantification aids chemical information workflows.