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Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.

Negin Manshour1, Jarett Zida Ren1,2, Farzaneh Esmaili1

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Genomics, Proteomics & Bioinformatics
|March 26, 2026
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Summary
This summary is machine-generated.

This study compares AlphaFold3, AlphaFold-Multimer, and ColabFold for protein-peptide complex structure prediction. AlphaFold-Multimer and ColabFold show versatility, while AlphaFold3 offers high-quality structures but moderate accuracy.

Keywords:
AlphaFoldColabFoldProtein binding scoring functionProtein structure prediction assessmentProtein–peptide complex structure

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Drug Discovery

Background:

  • Accurate three-dimensional structures of protein-peptide complexes are vital for understanding biological mechanisms and developing peptide therapeutics.
  • Protein-peptide docking computational methods are essential for predicting these complex structures.

Purpose of the Study:

  • To evaluate the performance of AlphaFold-Multimer, ColabFold, and AlphaFold3 in predicting protein-peptide complex structures.
  • To compare template-based (TB) and template-free (TF) prediction methods across these tools.
  • To assess the effectiveness of various scoring functions in ranking predicted complex structures.

Main Methods:

  • Comparative analysis of AlphaFold-Multimer, ColabFold, and AlphaFold3 using both TB and TF approaches.
  • Evaluation of prediction accuracy for top-ranked models and the overall prediction pool.
  • Assessment of scoring functions, including the integrated AlphaFold scorer, FoldX-Stability, and HADDOCK-mdscore.

Main Results:

  • AlphaFold-Multimer demonstrates strong performance in TB predictions and moderate capability in TF scenarios.
  • ColabFold shows adaptability in both TB and TF prediction settings.
  • AlphaFold3 produces high-quality structures but with lower medium accuracy compared to AlphaFold-Multimer's large model pool.
  • The integrated AlphaFold scoring function performed best, with FoldX-Stability and HADDOCK-mdscore offering complementary ranking insights.

Conclusions:

  • The study highlights the strengths and weaknesses of AlphaFold-Multimer, ColabFold, and AlphaFold3 for protein-peptide structure prediction.
  • Combining multiple scoring functions or using consensus approaches may enhance the accuracy of AlphaFold-based predictions.