Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with nearexperimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution. T-tests and normal probability plots showed that these computational methods predict atomic position standard errors 3.5-6 times bigger than experimental errors. Synopsis: Positional standard errors in AI-based protein 3D models are 3.5-6 times larger than in atomic resolution crystal structures.
Accuracy of AlphaFold models: Comparison with short N O contacts in atomic resolution protein crystal structures
Carugo, Oliviero
2024-01-01
Abstract
Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with nearexperimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution. T-tests and normal probability plots showed that these computational methods predict atomic position standard errors 3.5-6 times bigger than experimental errors. Synopsis: Positional standard errors in AI-based protein 3D models are 3.5-6 times larger than in atomic resolution crystal structures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
