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Challenges in bridging the gap between protein structure prediction and functional interpretation.

Mihaly Varadi¹, Maxim Tsenkov¹, Sameer Velankar¹

¹Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.

|October 18, 2023

Summary

Protein structure prediction tools offer unprecedented access to structural data, accelerating research. However, challenges in dynamics, multi-chain prediction, and quality assessment require further interdisciplinary collaboration and open data sharing for deeper biological insights.

Keywords:

AI‐based structure prediction AlphaFold PDB functional interpretation protein structures structural bioinformatics

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Area of Science:

Structural Biology
Computational Biology
Bioinformatics

Background:

Advancements in protein structure prediction tools have democratized access to vast amounts of protein structural data.
Predicted protein models hold significant potential to accelerate fundamental and translational research across various biological disciplines.

Purpose of the Study:

To highlight the transformative impact of protein structure prediction on scientific research.
To identify persistent challenges and propose strategies for future advancements in the field.
To emphasize the importance of interdisciplinary collaboration and FAIR data access.

Main Methods:

Review of current protein structure prediction tools and their capabilities.
Analysis of existing challenges in accurately predicting protein dynamics, multi-chain complexes, and function.
Examination of data accessibility through major databases and networks (e.g., AlphaFold Protein Structure Database, ESM Metagenomic Atlas, 3D-Beacons Network).

Main Results:

Significant progress has been made, broadening access to protein structural information.
Key challenges remain, including capturing protein dynamics, predicting multi-chain structures, interpreting function, and assessing model accuracy.
Databases and networks are crucial for FAIR data access and broader scientific application.

Conclusions:

Interdisciplinary collaboration is essential to overcome current limitations in protein structure prediction.
Continued development requires robust training materials, fostered collaborations, and open data sharing practices.
Further advancements will deepen the understanding of protein function, accelerating discoveries in disease pathogenesis and drug development.