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PredictProtein - Predicting Protein Structure and Function for 29 Years.

Michael Bernhofer1,2, Christian Dallago1,2, Tim Karl1

  • 1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr 3, 85748 Garching/Munich, Germany.

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PredictProtein is a long-standing online tool for protein sequence analysis, offering diverse structure and function predictions. Recent upgrades enhance usability and incorporate deep learning for improved protein binding and functional predictions.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • PredictProtein has been a leading online resource for protein sequence analysis since 1992.
  • It pioneered the integration of evolutionary information and machine learning for protein predictions.
  • The platform is hosted at the Luxembourg Centre for Systems Biomedicine (LCSB).

Purpose of the Study:

  • To provide a comprehensive online resource for protein sequence analysis.
  • To offer predictions of protein structure (1D and 2D) and function.
  • To continuously improve prediction accuracy and user experience through infrastructure and method updates.

Main Methods:

  • Utilizes evolutionary information and machine learning for predictions.
  • Incorporates MMseqs2 for accelerated sequence searching, reducing runtime five-fold.
  • Integrates deep learning embeddings for Gene Ontology (GO) and secondary structure predictions.

Main Results:

  • Outputs include multiple sequence alignments, secondary structure, solvent accessibility, transmembrane segments, disordered regions, flexibility, and disulfide bridges.
  • Functional predictions encompass effects of mutations, GO terms, subcellular localization, and binding capabilities (protein, RNA, DNA).
  • Recent updates include deep learning-based predictions and a DNA/RNA/protein binding prediction method.

Conclusions:

  • PredictProtein.org offers reliable predictions for computational and experimental biologists.
  • The platform has enhanced usability and throughput with infrastructure upgrades.
  • All scripts and methods are available for free offline, high-throughput execution.