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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Ligand Binding Sites02:40

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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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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Conservation of Protein Domains Over Different Proteins02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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TransABseq: A Two-Stage Approach for Predicting Antigen-Antibody Binding Affinity Changes upon Mutation Based on

Cui-Feng Li1, Zihao Yan2, Fang Ge3

  • 1School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China.

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|May 12, 2025
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TransABseq predicts how mutations affect antigen-antibody interactions using a novel two-stage computational framework. This tool enhances understanding of antibody function in disease detection and therapy development.

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Area of Science:

  • Computational biology
  • Immunology
  • Bioinformatics

Background:

  • Antigen-antibody interactions are crucial for host defense, diagnostics, and therapeutics.
  • Antibodies' specificity and affinity are key to their clinical applications.
  • Predicting the impact of mutations on these interactions is vital for drug development.

Purpose of the Study:

  • To develop a novel computational framework, TransABseq, for predicting the effects of missense mutations on antigen-antibody interactions.
  • To enhance the accuracy and robustness of predicting mutation impacts on antibody binding.

Main Methods:

  • TransABseq employs a two-stage architecture combining protein language model embeddings, a Transformer encoder, and multiscale convolutional modules.
  • A deep feature fusion strategy integrates biochemical properties, global sequence dependencies, and hierarchical features.
  • The XGBOOST model performs quantitative prediction based on fused features.

Main Results:

  • TransABseq demonstrated strong performance in cross-validation, achieving average PCC values of 0.607-0.843 and RMSE values of 1.166-1.337 kcal/mol.
  • The model showed superior predictive accuracy on blind test data, with a PCC of 0.721 and RMSE of 0.925 kcal/mol.
  • The deep feature fusion strategy effectively revealed protein biochemical properties.

Conclusions:

  • TransABseq offers a robust and accurate computational tool for predicting mutation effects on antigen-antibody interactions.
  • The framework's innovative architecture and feature fusion enhance predictive capabilities.
  • Publicly available code and data facilitate further research in antibody engineering and drug discovery.