How AlphaFold2 shaped the structural coverage of the human transmembrane proteome
Márton A Jambrich1, Gabor E Tusnady2,3, Laszlo Dobson1,4,5
1Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest, 1117, Hungary.
Scientific Reports
|November 21, 2023
Summary
AlphaFold2 significantly enhances transmembrane protein structure prediction and analysis. The TmAlphaFold database offers quality assessments for membrane-embedded protein structures, aiding in identifying areas needing experimental validation.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- Transmembrane proteins are crucial but experimentally challenging to study, limiting structural biology.
- Existing structure prediction algorithms struggle with transmembrane proteins due to limited training data.
Purpose of the Study:
- To assess AlphaFold2's impact on transmembrane protein structural coverage.
- To evaluate AlphaFold2's utility in identifying homologous proteins within diverse families.
- To combine structural prediction quality with homology searches to guide experimental efforts.
Main Methods:
- Utilizing the TmAlphaFold database for membrane-embedded AlphaFold2 predictions.
- Implementing geometrical evaluation for quality assessment of predicted structures.
- Analyzing structural coverage improvements and homology search capabilities of AlphaFold2.
Main Results:
- AlphaFold2 has substantially increased the structural coverage of membrane proteins compared to the era of experimental structures alone.
- The TmAlphaFold database provides valuable quality assessments for predicted transmembrane protein structures.
- AlphaFold2 facilitates the search for distant homologs in diverse protein families.
Conclusions:
- AlphaFold2, integrated with the TmAlphaFold database, greatly advances transmembrane protein structural biology.
- Quality assessment and homology search can pinpoint protein families requiring further experimental structure determination.
- This approach optimizes the use of computational predictions to guide future research directions.
