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Conserved Binding Sites01:49

Conserved Binding Sites

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Ligand Binding Sites02:40

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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AB-Bind: Antibody binding mutational database for computational affinity predictions.

Sarah Sirin1, James R Apgar2, Eric M Bennett2

  • 1Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.

Protein Science : a Publication of the Protein Society
|October 17, 2015
PubMed
Summary
This summary is machine-generated.

Computational tools are advancing antibody design. A new database, AB-Bind, was used to test protein scoring potentials, showing modest success in predicting antibody binding affinity changes and identifying improved binders.

Keywords:
affinity optimizationantibody affinityantibody mutagenesiscomputational affinity predictionmutational databaseprotein interface designprotein-protein interactionsscoring interface mutationsstructure-based modeling

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • Antibodies (Abs) are vital for the immune system and widely used in diagnostics and therapeutics.
  • High-affinity and specific antibodies are crucial for research and medicine, driving demand for computational design tools.

Purpose of the Study:

  • To create and utilize the Antibody-Bind (AB-Bind) database for evaluating computational methods in antibody design.
  • To assess the performance of protein scoring potentials in predicting binding free energy changes (ΔΔG) upon mutagenesis.

Main Methods:

  • Compiled the AB-Bind database with 1101 mutants and experimental ΔΔG data across 32 antibody-antigen complexes.
  • Evaluated STATIUM, FoldX, and Discovery Studio scoring potentials using numerical correlations and ROC curve analysis (AUC).
  • Assessed the ability of methods to enrich for variants with improved binding affinity.

Main Results:

  • Numerical correlations between computed and observed ΔΔG were low (r = 0.16-0.45).
  • Scoring potentials showed predictive power in classifying binder variants (highest AUC = 0.88).
  • FoldX and Discovery Studio enriched for improved binders, ranking a significant percentage in the top 5%.

Conclusions:

  • Current protein scoring potentials offer modest predictive performance for antibody affinity changes.
  • Further development of energy functions is necessary for accurate affinity prediction in antibody design.
  • The AB-Bind database serves as a valuable resource for benchmarking computational antibody modeling methods.