Theoretical models: Difference between revisions

Overall, AlphaFold2 continued to "convincingly outperform all other methods" when various methods were compared using "fully automated mode with default parameter settings, without any manual interventions"<ref name="bhattacharya">PMID: 37523536</ref>. AlphaFold2 predictions had a mean [[Calculating GDT TS|GDT-TS]] score of 73. ESMFold, which is not based upon multiple sequence alignments, attained second best for backbone positioning (mean GDT-TS 61.6), outperforming RoseTTAFold (which is MSA based) for >80% of cases<ref name="bhattacharya" />. Individual domains were reliably predicted in the 19 multidomain targets, but predictions of domain orientations was less successful<ref name="bhattacharya" />. As an example, AlphaFold 2 achieved the best prediction for one large multi-domain target T1154, but the GDT-TS was only 24<ref name="bhattacharya" /> There is considerable room for improvement in prediction of side-chain positioning: while AlphaFold2 was most successful, its mean GDC-SC score fell short of 50<ref name="bhattacharya" />. Targets in CASP 15 (2022) included several new categories: 12 with RNA<ref name="rna">PMID: 37162955</ref><ref name="rna2">PMID: 37466021</ref>, some ligand protein complexes, and 41 quaternary assembly protein complexes<ref name="casp15new">PMID: 37306011</ref>. "... for the vast majority of proteins and protein complexes, AlphaFold can produce a model close to experimental quality."<ref name="elofsson">PMID: 37060758</ref>. The success rate for overall fold and interface prediction in complexes was 90%, vs. 31% in CASP 14<ref name="assemblies">PMID: 37503072</ref>. This was "largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines"<ref name="assemblies" />.