Assessing the utility of CASP14 models for molecular replacement

Abstract

The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real-world application. In CASP7, the metric for molecular replacement assessment involved full likelihood-based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood-based rigid-body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined log-likelihood-gain (LLG) score. This enabled multi-copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative-expected-LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X-ray, NMR or cryo-EM.We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing.

The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report

“…They suggest that the overall quality of these models will help in solving the phase problem in many cases. This is in line with the outstanding results obtained by AlphaFold during the CASP14 session (Milla ´n et al, 2021;Pearce & Zhang, 2021;Pereira et al, 2021). We managed to solve the structure of a small protein and to obtain 2F o À F c electron-density maps of excellent quality in a straightforward way, whereas we had previously struggled with solving the structure for almost two years following the acquisition of good-quality diffraction data.…”

“…Several examples indicate that AlphaFold models exhibit r.m.s.d. values of lower than 1.5 A ˚with the final structures (Dowah et al, 2021;Gao et al, 2021;Kuttiyatveetil et al, 2021;Milla ´n et al, 2021;Yin et al, 2021;Yu et al, 2021;Fowler & Williamson, 2022;Paul et al, 2022), even for proteins without related structural templates deposited in the PDB. However, the impact of these high-quality models in structural biology goes far beyond the simple case of molecular replacement.…”

“…However, it is now obvious that beyond the sequence identity, the structural similarity between the search model and the crystal structure is most important (McCoy et al, 2022). Thanks to their high accuracy and also to the significantly improved estimation of the error in the coordinates, both AlphaFold and RoseTTAFold 3D structure models have already influenced the process of 3D protein structure determination by the molecular-replacement technique (Flower & Hurley, 2021;Kryshtafovych, Schwede et al, 2021;Milla ´n et al, 2021;Moi et al, 2021).…”

The AlphaFold Database of Protein Structures: A Biologist’s Guide

“…Moreover, the improved accuracy of AlphaFold models compared to template-based ones will be important in several applications, including structure-based drug discovery, 7 variant prediction and to assist experimental structure determination (e.g. molecular 8 ) 9 , 10 , 11 , 12 (and extensively discussed in the JMB AlphaFold Special Issue, Volume 433, Issue 20, 1st October 2021). However, in cases where the predicted model of the holo form with its cognate ligand is important, a less accurate model which inherits the ligand coordinates from the template may provide more biological insights compared to a more accurate AlphaFold model of the apo form.…”

Using machine learning to study protein–protein interactions: From the uromodulin polymer to egg zona pellucida filaments

“…The recent developments that culminated with the release of open-source code for both AlphaFold2 (Jumper et al, 2021) and RoseTTAFold (Baek et al, 2021) brought protein structure prediction to a level where it can both rival and facilitate experimental structure determination. This is highlighted by a growing number of reports that models produced by both systems can be successfully used to phase by molecular replacement native X-ray diffraction data for the corresponding proteins (Baek et al, 2021;Flower & Hurley, 2021;Millán et al, 2021;Pereira et al, 2021), as well as to fit maps obtained by cryo-EM (Baek et al, 2021;Gupta et al, 2021). The availability of a database of high-quality structure predictions for the proteomes of several major experimental systems, including human (Tunyasuvunakool et al, 2021), is bound to significantly expand these and other applications in the near future.…”