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DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.

Umberto Lupo1,2, Damiano Sgarbossa1,2, Martina Milighetti3,4

  • 1Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.

Bioinformatics (Oxford, England)
|December 13, 2024
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Summary
This summary is machine-generated.

We developed DiffPaSS, a fast, flexible, and hyperparameter-free computational method for identifying interacting protein sequences. This tool improves upon existing algorithms for sequence pairing and aids in predicting protein complex structures.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Identifying interacting protein sequences is crucial for understanding biological pathways and functions.
  • Interacting proteins exhibit evolutionary similarities and co-occurrence patterns in amino acid usage.
  • Existing methods often rely on approximate optimization for sequence pairing, limiting flexibility and speed.

Purpose of the Study:

  • To introduce DiffPaSS (Differentiable Pairing using Soft Scores), a novel differentiable framework for optimizing interacting biological sequence pairs.
  • To provide a flexible, fast, and hyperparameter-free solution for sequence pairing across various scoring systems.
  • To demonstrate the utility of DiffPaSS in predicting protein complex structures and analyzing non-aligned sequences.

Main Methods:

  • Developed DiffPaSS, a differentiable optimization framework implemented in PyTorch.
  • Applied DiffPaSS to a prokaryotic dataset using mutual information and neighbor graph alignment scores.
  • Evaluated DiffPaSS performance against existing algorithms for sequence pairing.

Main Results:

  • DiffPaSS significantly outperforms existing algorithms in optimizing sequence similarity and coevolution scores.
  • The paired alignments generated by DiffPaSS are effective for predicting protein complex structures.
  • DiffPaSS successfully handles non-aligned sequences, as shown with T-cell receptor data.

Conclusions:

  • DiffPaSS offers a superior, efficient, and versatile approach for identifying interacting protein sequences.
  • The framework's ability to handle diverse scores and non-aligned sequences expands its applicability in bioinformatics.
  • DiffPaSS provides a valuable tool for advancing research in protein interactions and structural biology.