actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions
Hebrew University of Jerusalem · Massachusetts Institute of Technology
Abstract
SUMMARY: One of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions. The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interactions, in particular motif-mediated interactions, often also contain regions that remain flexible upon binding. These noninteracting flanking regions are assigned low confidence values and will affect ipTM, as it considers all interchain residue-residue pairs, and two models of the same motif-domain interaction, but differing in the length of their…
Citation impact
- FWCI
- 25.88
- Percentile
- 100%
- References
- 16
Authors
3Topics & keywords
- USable
- Computer science
- Confidence interval
- Data mining
- Metric (unit)
- Protein–protein interaction
- Measure (data warehouse)
- Mathematics