A structural biology community assessment of AlphaFold 2 applications
Akdel, M.; Pires, D. E.; Porta-Pardo, E.; Janes, J.; Zalevsky, A. O.; Meszaros, B.; Bryant, P.; Good, L. L.; Laskowski, R. A.; Pozzati, G.; Shenoy, A.; Zhu, W.; Kundrotas, P.; Ruiz-Serra, V.; Rodrigues, C. H.; Dunham, A. S.; Burke, D.; Borkakoti, N.; Velankar, S.; Frost, A.; Lindorff-Larsen, K.; Valencia, A.; Ovchinnikov, S.; Durairaj, J.; Ascher, D. B.; Thornton, J. M.; Davey, N. E.; Stein, A.; Elofsson, A.; Croll, T. I.; Beltrao, P.
Show abstract
Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Protein language model embeddings for fast, accurate, alignment-free protein structure prediction 97%
- Assessing PDB Macromolecular Crystal Structure Confidence at the Individual Amino Acid Residue Level 96%
- A two-site flexible clamp mechanism for RET-GDNF-GFRα1 assembly reveals both conformational adaptation and strict geometric spacing 95%
Similar papers in this journal
- Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics 96%
- SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures 95%
- Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.