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Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.

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Protein Science : a Publication of the Protein Society
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Deep learning models like AlphaFold can accurately predict many protein complex structures from sequence, outperforming traditional docking methods. However, challenges remain for modeling antibody-antigen and T cell receptor-antigen interactions.

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Area of Science:

  • Computational biology
  • Structural biology
  • Deep learning in bioinformatics

Background:

  • Experimental determination of protein-protein interactions provides mechanistic insights but is limited by scale.
  • Accurate computational methods are needed to model the vast number of protein interactions.
  • Deep learning approaches offer potential for predicting protein complex structures from sequence.

Purpose of the Study:

  • To evaluate the accuracy of AlphaFold in predicting protein complex structures.
  • To compare AlphaFold's performance against traditional protein-protein docking methods.
  • To identify factors influencing AlphaFold's success and limitations, particularly for immune recognition complexes.

Main Methods:

  • Benchmarking AlphaFold (multiple implementations and parameters) on 152 heterodimeric protein complexes.
  • Testing AlphaFold-Multimer on antibody-antigen and T cell receptor-antigen complexes.
  • Analyzing sequence and structural features associated with prediction accuracy.
  • Investigating the impact of multiple sequence alignment input on model performance.

Main Results:

  • AlphaFold generated near-native models for 43% of heterodimeric complexes, significantly outperforming unbound docking (9% success).
  • AlphaFold and AlphaFold-Multimer showed limited success in modeling antibody-antigen (11% success) and T cell receptor-antigen complexes.
  • Specific sequence and structural features were linked to prediction failures.
  • Multiple sequence alignment input affected prediction accuracy.

Conclusions:

  • End-to-end deep learning, exemplified by AlphaFold, can accurately model many transient protein complexes.
  • Current AlphaFold versions struggle with adaptive immune recognition complexes (antibody-antigen, TCR-antigen).
  • Further development is needed to enhance the reliability of deep learning models for all protein-protein interactions.