Evaluating AlphaFold Protein Structure Predictions

AlphaFold initial release and v2.0.0 were downloaded from github and installed as described (https://github.com/deepmind/alphafold) under Linux (Debian 10, 96 GB RAM, NVidia Quadro P6000 GPU with 24 GB RAM or NVidia RTX A6000 GPU with 48GB RAM GPU). We introduced minor modifications into the code to overcome memory usage problems in case of large multiple sequence alignment files and to be able to run multimer predictions with the initial release (http://alphafold.hegelab.org). Our runs used all genetic databases (--db_preset=full_dbs). Generated structures were evaluated based on PAE, pLDDT, and ipTM+pTM scores [21 (link),36 (link)]. In addition, all top-scored structures were inspected visually.

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Tordai H., Suhajda E., Sillitoe I., Nair S., Varadi M, & Hegedus T. (2022). Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology. International Journal of Molecular Sciences, 23(16), 8877.

Publication 2022

Quadro P6000 GPU

Memory problems Sequence alignment

Corresponding Organization : Semmelweis University

Other organizations : Budapest University of Technology and Economics, Wigner Research Centre for Physics, Institute of Structural and Molecular Biology, University College London, European Bioinformatics Institute

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Variable analysis

independent variables

AlphaFold initial release and v2.0.0 versions
Minor modifications introduced into the code

dependent variables

PAE (Predicted Aligned Error) scores
PLDDT (per-residue local-distance-difference test) scores
IpTM+pTM (integrated predicted TM-score plus predicted TM-score) scores

control variables

Operating system (Linux Debian 10)
Hardware (96 GB RAM, NVidia Quadro P6000 GPU with 24 GB RAM or NVidia RTX A6000 GPU with 48GB RAM GPU)
Genetic databases used (--db_preset=full_dbs)

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