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A structural biology community assessment of AlphaFold2 applications

A structural biology community assessment of AlphaFold2 applications

Added structural coverage by AlphaFold2 predictions of model proteomes

The AF2 database has released predictions of the canonical protein isoforms for 21 model species, covering nearly every residue in 365,198 proteins. This represents around twice the number of experimental structures and six times the number of unique proteins in the Protein Data Bank (PDB). It is important to assess the extent to which AF2 predictions extend the structural coverage beyond previous proteome-wide structural predictions. We compared the structures of 11 model species that were included in both the SMR and AF2 databases and that had an average additional coverage of 44% of residues by AF2 (Fig. 1a, residues). However, not all of AF2’s residue predictions have high confidence. For residues that are not present in the SMR, we observed that an average of 49.4% are predicted with confidence by AF2 (predicted local distance difference test score (pLDDT) > 70) (Fig. 1a, AF residue confidence). With a more stringent cut-off (pLDDT > 90), AF2 predicts, on average, 25% of residues with very high confidence. In summary, an average of around 25% of the residues of the proteomes of the 11 model species are covered by AF2 with novel (not present in SRM) and confident (pLDDT > 70) predictions.

Fig. 1: Additional coverage provided by AF2-predicted models.
figure 1

a, Added structural coverage (per-protein, left; per-residue, middle) and per-residue confidence of regions not covered by SMR (right) for 11 species included in both the AF2 and SMR databases. b, Fraction of confident (pLDDT > 70) residues per human AF2 model, binned by r.m.s.d. from the corresponding trRosetta-derived domain-level Pfam model; 3,035 AF2 predicted structures of protein regions matching one of 1,464 different Pfam domain families were compared with the corresponding trRosetta model. c, Median fragment length and median pLDDT score of human AF2-only regions. The highlighted area identifies high-confidence regions with domain-like length. The bottom, middle line and top of the box correspond to the 25th, 50th and 75th percentiles, respectively. d, Comparison of AF2 SASA (SASA20, 20-residue smoothing) and pLDDT (pLDDT20, 20-residue smoothing) against a disorder prediction method (IUpred2).

We then compared AF2 predictions with those derived for Pfam protein domains15 using trRosetta16. As there is only one trRosetta representative structure per domain family, we selected one species—human—and compared 3,035 AF2 models of 1,464 different Pfam domain families with the representative trRosetta model. These two approaches generally agree, with around 50% of AF2 domain structures having a root-mean-square deviation (r.m.s.d.) < 2 Å from the generic trRosetta model (Supplementary Fig. 1a). We observed a correlation between the estimated accuracy of the AF2 model (pLDDT) and the r.m.s.d. from the trRosetta model (Fig. 1b and Supplementary Fig. 1b,c). For AF2 models with an r.m.s.d. below 2 Å from the trRosetta model have, more than 90% of their residues, on average, have a pLDDT above 70 (Fig. 1b). We also examined the variability of domain structure for 273 domain families with 3 or more instances in the human proteome (Supplementary Fig. 2), and observed that 70% of domain instances are within one s.d. of the mean r.m.s.d. for their domain family. Together, these results indicate that, for at least 50% of human Pfam domains, the trRosetta Pfam model was already likely to be accurate.

We assessed the confidence and length of AF2 contiguous regions that are not covered in SMR to identify regions that may correspond to novel structures of folded domains, rather than short termini or interdomain linkers. The distribution of median confidence scores of a fragment versus fragment length shows an enrichment for high-confidence predictions with a length of 100–500 residues (Fig. 1c and Supplementary Fig. 3), consistent with the size of a typical protein domain21. This relation can be observed for all species, except Staphylococcus aureus (Supplementary Fig. 3). We identified, across the 11 species, 18,429 contiguous regions that are ‘domain like’ (with a length of 100–500 residues) with confident predictions (pLDDT > 70) that have no model in SMR. The human regions are provided in Supplementary Table 1.

Around half the residues in AF2 predictions of the 11 model species are of low confidence, many of which may correspond to regions without a well-defined structure in isolation. It has been shown that regions with low pLDDT are often intrinsically disordered proteins or regions (IDPs/IDRs)13. We benchmarked AF2-derived metrics against IUPred2 (ref. 22), a commonly used disorder predictor (Fig. 1c), using regions annotated for order/disorder (Supplementary Table 2). In addition to using pLDDT, we tested the relative solvent accessible surface area (SASA) of each residue and smoothed versions of these metrics (Fig. 1d and Supplementary Fig. 4). pLDDT and window averages of pLDDT or SASA outperformed IUPred2, indicating that AF2’s low-confidence predictions are enriched for IDRs. To facilitate the study of human IDRs, we provide these predictions for human proteins in Supplementary Dataset 1 and in ProViz23: http://slim.icr.ac.uk/projects/alphafold?page=alphafold_proviz_homepage.

Characterization of structural elements in AlphaFold2’s predicted models across 21 proteomes

The AF2 database is likely to contain structural elements that may not have been extensively seen in experimental structures. Owing to the presence of low-confidence regions in the AF2 proteins, we first split each prediction into smaller high-confidence units (see Methods). We then performed a global comparison of structural elements between the 365,198 proteins in the AF2 database and 104,323 proteins from the CASP12 dataset in the PDB. We applied the Geometricus algorithm24 to obtain a description of protein structures as a collection of discrete and comparable shape-mers, analogous to k-mers in protein sequences. We then obtained a matrix of such shape-mer counts for all proteins, which we clustered using non-negative matrix factorization (NMF) (see Methods). The clustering identified 250 groups of proteins, dubbed ‘topics’ (Supplementary Dataset 2), with characteristic combinations of shape-mers. These characteristic shape-mers could include small structural elements, such as repeats, the specific arrangements of ion-binding sites or larger structural elements that could define specific folds. For visualization, we performed a t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction in which proteins composed of similar shape-mers are expected to group together (Fig. 2). In line with this, the shape-mer representation of AF2 proteins can predict the corresponding PDB protein entries with high accuracy (area under the receiver operating characteristic curve of 0.95 using the cosine similarity of the shape-mer vector). Additionally, the 20 most common superfamilies, predicted from sequence, tend to be placed together.

Fig. 2: The space of characteristic structural elements in AF2 structural models for 21 species.
figure 2

Visualization of t-SNE dimensionality reduction analysis, in which structures with similar structural elements are placed closer together and the 20 most common superfamilies are colored. The axes corresponding to the t-SNE dimension 1 and t-SNE dimension 2 were omitted. Six shape-mer groups (that is, topics) discussed in the text, consisting of mainly AF2 proteins as opposed to PDB proteins, are labeled A–F, and a representative structure is depicted for each. Residues in the representative structures are colored according to their contribution to the topic under consideration—red residues have the highest contribution, and blue residues are specific to the example and not to the topic.

Out of 250 total groups, we selected 5 examples that were almost exclusively (>90%) composed of structures derived from AF2, as well as 1 example with >80% AF2 structures with a particularly interesting novel predicted structural element. We illustrated these with a representative structure in Figure 2. Examples include 4,192 proteins annotated as G-protein-coupled olfactory or odorant receptors (Pfam PF13853), 97% of which are mammalian (Fig. 2a, Topic 88, and Supplementary Fig. 5a); a group of primarily (94%) plant proteins, annotated as PCMP-H and PCMP-E subfamilies of the pentatricopeptide repeat (PPR) superfamily (Fig. 2b, Topic 60, and Supplementary Fig. 5b); a group of heterogeneous structures that were mostly (>75%) annotated as ATP or ion binding (Fig. 2c, Topic 150, and Supplementary Fig. 5c); groups of proteins with leucine-rich repeats (Fig. 2d, Topic 16, and Supplementary Fig. 5d); some proteins with uncommon, regular patterns (Fig. 2e, Topic 188, and Supplementary Fig. 5e); and long α-helical constructs (Fig. 2f, Topic Helix, Supplementary Fig. 5f). For the PCMP-H and PCMP-E subfamilies (Fig. 2b), there are no known experimental structures mapped. AF2 predictions could help elucidate the structural peculiarities of these subfamilies, including the mechanism of RNA recognition and binding for PCMP-H and PCMP-E proteins.

Studying examples from Mycobacterium tuberculosis in Topic 188 led us to identify an interesting structure for a tandem repeat. Tandem repeat proteins with repetitive units of 6–10 residues predominantly have beta-solenoid structures25. Analyzing the AF2 results, we found a novel beta-solenoid structure predicted for a large family of pentapeptide repeats26, found in the mycobacterial PPE proteins (Pfam: PF01469) (Fig. 2e and Supplementary Fig. 6). This structure represents a beta-solenoid, with the shortest possible coil of ten residues (two pentapeptide repeats) (Supplementary Fig. 6b). Although such a beta-solenoid has not yet been resolved, our evaluation of the quality of the atomic structure (stereochemistry and contacts) suggests that the AF2 model is highly probable. Thus, AF2 may have allowed us to answer the question of what is the shortest length of repeat that forms a beta-solenoid.

Finally, we also considered protein groups consisting primarily of PDB proteins to study why AF2 proteins are absent from them. In some cases, this seemed to be due to the limited number of species and proteins covered by the current AF2 database. Topics 209 and 113 consist of immune response proteins, such as immunoglobulins and T-cell receptors, mainly from the PDB. As many of these antibodies are under intense study, there are many more PDB structures (based on multiple individuals and antibody-drug research) than the actual number of such proteins in the respective UniProt proteomes. Topic 38 consists of short fragments of PDB structures, with an average length of 63 residues—there are no AF2 proteins, because AlphaFold models the entire structure instead of returning fragments.

Application of AlphaFold2 models for structure-based variant effect prediction

A protein structure facilitates the generation of hypotheses regarding the impact of missense mutations. Conversely, an agreement between the expected and observed impacts of mutations provides confidence in the accuracy of a structural model. We obtained two independent compilations of experimentally measured impacts of protein mutations on protein function: (1) a compilation of measured changes in stability upon mutations27,28; and (2) a compilation of deep mutational scanning (DMS) experiments29,30 measuring the outcome of any possible single point mutation on most protein positions.

The DMS data were available for 33 proteins with 117,135 mutations; we obtained experimentally derived models for 31 of the proteins and AF2 models for all 33. We then used three structure-based variant effect predictors (FoldX31, Rosetta32 and DynaMut2 (ref. 33)) to compare the DMS measurements with predicted impacts. Although the correlation estimates between the experimental and predicted impacts of mutations varied across the proteins, those derived from the AF2 models consistently matched or were better than those derived from experimental models (Fig. 3a,b and Supplementary Fig. 7). Regions with confidence scores lower than 50 result in lower concordance (Fig. 3a), but restriction to protein regions without an experimental model can still lead to correlations that are comparable to those observed in experimental structures (Fig. 3b). Because low AF2 confidence scores are enriched for intrinsically disordered protein regions, it is possible that the poor correlation in low-confidence regions is in part owing to higher tolerance to protein mutations. In line with this, we observed an average higher tolerance to mutations in low-confidence regions (Fig. 3c).

Fig. 3: Comparing structure-based prediction of impact of protein missense mutations using experimental and AF2-derived models.
figure 3

a, Relationship between the predicted ΔΔG for mutations with measured experimental impact of the mutation from deep mutational scanning data (−1 × Pearson correlation). The predicted change in stability was determined using one of three structure-based methods, using structures from AF2 or available experimental models. The bottom, middle line and top of the box correspond to the 25th, 50th and 75th percentiles, respectively. The lines extend to 1.5 × IQR (interquartile range). A total of 117,135 mutations were used in the analysis. b, Correlations based on the FoldX predictions as in a, but subsetting the positions in AF2 models according to confidence and whether the position is present in an experimental structure. Data are presented as mean values ± the confidence intervals calculated via fisher’s Z transform (R’s cor.test function). c, The mean impact of a mutation, calculated as the enrichment ratio (ER) score, from DMS data for positions in AF2 models with different degrees of confidence. A total of 117,135 mutations were used in the analysis. d, Comparative performance of methods for predicting stability changes upon mutation using AF2 and experimental and homology models based on protein structure templates of different identity cut-offs. Experimental measurements of stability are for 2,648 single-point missense mutations over 121 proteins. The bottom, middle line and top of the box correspond to the 25th, 50th and 75th percentiles, respectively. The lines extend to 1.5 × IQR. e, Example application for structure-based prediction of stability impact of known disease mutations for a human protein with little structural coverage prior to AF2. ΔΔG stability changes were predicted using Rosetta, and a substantial impact was considered for ΔΔG > 1.5 kcal/mol.

The compilation of measured impacts of mutations on protein stability contains information for 2,648 single-point missense mutations over 121 distinct proteins. We compared the accuracy of structure-based prediction of stability changes using AF2 structures, experimental structures and homology models using different sequence identify cut-offs (Fig. 3d and Supplementary Fig. 8; see Methods). Across 11 well-established methods (Fig. 3d and Supplementary Fig. 8), the predictions of stability changes based on AF2 models were comparable to those of experimental structures. Homology-model-based predictions tended to show substantial decreases in performance for templates below 40% sequence identity.

We investigated, as an example, the human Sphingolipid delta(4)-desaturase (DEGS1), a 323-residue protein associated with leukodystrophy, for which no structure or model was available. All but the terminal residues are predicted by AF2 with high confidence. The presumed catalytic core is discussed further below. Here we focus on disease-associated missense variants. p.A280V has been shown to lead to loss of protein stability34 and has a predicted Gibbs free energy change (ΔΔG) of 3.7 kcal/mol. Two additional pathogenic variants have ΔΔG values of >1.5 kcal/mol, pointing towards loss of stability being the mechanism of pathogenicity; the benign variants do not substantially affect protein stability, as expected (Fig. 3e). The likely pathogenic variant p.R133W is not predicted to affect stability, and hence likely has a different mechanism underlying disease. This is in line with previous findings that core variant changes in particular lead to loss of stability, whereas surface variants are more likely to act through other mechanisms30.

Functional characterization of AF2 models by pocket and structural motif prediction

High-confidence proteome-wide structural predictions open the door for a large expansion of predicted protein pockets35,36. However, the full protein models produced by AF2 have to be considered carefully given their potential errors, such as the likely incorrect placement of protein segments of low confidence or the low confidence in interdomain orientations. To investigate whether these issues may result in the formation of spurious pockets, we predicted pockets on a set of 225 proteins with known binding sites defined using bound (holo) structures for which the corresponding unbound (apo) structures are available37.

Pockets identified from structures have a wider size range than do ground-truth binding sites (Fig. 4a). This is also true for pockets predicted from AF2 structures, including a small number of particularly large pockets (Fig. 4a). We divided AF2 pocket predictions into high-quality (mean pLDDT > 90) and low-quality (mean pLDDT ≤ 90) subsets (Fig. 4b,c) on the basis of the mean pLDDT of pocket-associated residues. Low-quality pockets are larger on average, and include particularly large pockets (Fig. 4a, bottom). We then asked whether mean pLDDT could be useful as a general metric of prediction confidence by quantifying the overlap between known and predicted pockets (Fig. 4b and Supplementary Fig. 9). We did not observe a difference between the performance of high-quality AF2 pockets and pockets identified from experimental structures. In contrast, low-confidence pockets generally did not overlap with known sites. Although there may be bias because high-confidence AF2 regions are more likely to have relevant deposited templates, we suggest that the mean pLDDT of predicted pockets can be used as an additional criterion for pocket selection in AF2 structures.

Fig. 4: Pocket detection and function prediction.
figure 4

a, Size of known binding sites (or unified binding sites) compared with the size of top AutoSite pockets in experimental holo (bound), experimental apo (unbound) and AF2 structures. AF2 structures are split into high-confidence (mean pLDDT > 90) and low-confidence (mean pLDDT ≤ 90) subsets. The bottom, middle line and top of the box correspond to the 25th, 50th and 75th percentiles, respectively. The lines extend to 1.5 × IQR. b, Distribution of overlap between known binding sites and top predicted pockets for holo, apo and AF2 structures. The bottom, middle line and top of the box correspond to the 25th, 50th and 75th percentiles, respectively. The lines extend to 1.5 × IQR (interquartile range). c, Enzymatic activity prediction using pocket-derived, template-derived and combined metrics. AUC, area under the curve. TPR, true positive rate; FPR, false positive rate. d, Superposition of the AF2 model of DEGS1 (O15121) with PDB entry 4ZYO. Orange: ribbon representation of AF2 predicted structure for DEGS1. Cyan: ribbon representation of 4ZYO. Zinc atoms (light blue spheres) and bound substrate (dark blue ball and stick) as observed in the structure of 4ZYO are also shown. e, Close up of the metal-binding center of 4ZYO. Ribbon representation of the protein and metal chelators for DEGS1 and 4ZYO are shown in orange and cyan, respectively. The zinc atoms observed in 4ZYO are shown as light blue spheres. Metal-chelating residues for DEGS1 are clearly identifiable.

Conserved local conformations of specific residues can be used to identify important functions, such as enzyme activity, ion or ligand binding beyond global sequence and fold similarities38. To showcase the potential of this application for AF2 models in the future, we focused on 912 human proteins with no experimental or homology models available. We found that the prediction score of the highest ranked pocket enriched the set for proteins with previous annotations for enzymatic activity (Fig. 4c and Supplementary Table 3). Discarding pockets with a low mean pLDDT led to slightly improved enrichment. As a specific example, we focused on the human sphingolipid delta(4)-desaturase (EC 1.14.19.17, DEGS1, UniProt Accession O15121, pocket score rank 57 of 912), which has a high confidence level (average pLDDT = 96.31) and for which there are no previous structural data. A sequence search of the 323-residue protein against all existing entries in the PDB shows that the best sequence match is 23.5%, with PDB entry 1VHB (Bacterial dimeric hemoglobin, 9115439), indicating the lack of any structural models from homology. A scan of 400 auto-generated 3-residue templates from the AF2-predicted structure against representative structures in the PDB (reverse template comparison38) yielded a possible 3-residue template match: PDB entry 4ZYO (EC 1.14.19.1, human stearoyl-CoA desaturase39, Fig. 4d). A close up of the metal-binding center (Fig. 4e) of DEGS1 and 4YZO (overall sequence homology, 12.1%) superimposed via the 3-residue templates (Fig. 4d) clearly indicates the potential dimetal catalytic center for DEGS1. The histidine-coordinating metal center of DEGS1, together with data on the bound substrate of 4ZYO, provides a foundation for modeling studies that could impact the pharmacology of DEGS1 by exploring the details of its catalytic mechanism.

AlphaFold2-based prediction of protein complex structures

Since the first development of direct coupling analysis algorithms, co-evolutionary-information-based methods have been used to predict protein-protein interactions40. It has been recently reported that several deep-learning-based methods, such as trRosetta16 and Raptor-X41, can predict the structure of protein complexes. To examine the capacity of AF2 to predict protein complex structures, we tested the ability of AF2 to fold and ‘dock’ two benchmark sets—a set of proteins known to form oligomers42 and the Dockground 4.3 heterodimeric benchmark43.

For oligomerization, we obtained sets of proteins known either not to oligomerize or to form oligomers, including dimers, trimers or tetramers. We then made AF2 predictions for each protein, attempting to predict either a monomer or an oligomeric form (see Methods). Across the set of predictions, higher scores were given to models corresponding to the correct oligomerization state, and 71 out of 87 (82%) predicted top-scoring models corresponded to the correct state (Fig. 5a and Supplementary Table 4). Generally, the multimeric state scores are well separated from the monomeric state scores (Fig. 5b). In 28/30 examples, AF2 was able to correctly predict monomeric proteins as monomers, 29/35 dimers as dimers, 7/9 trimers as trimers and 7/13 tetramers as tetramers. Notably, although the failure rate is high for tetramer state predictions, the predicted structure for the corresponding state was actually correct for 5/6 failures. Examples of failure modes for dimers and a tetramer are shown in Figure 5c,d. We noted that, for some cases of failed tetramer predictions, we could obtain higher confidence of the tetramer predictions by increasing the number of recycles.

Fig. 5: Using AF2 to predict homo-oligomeric assemblies and their oligomeric state.
figure 5

a, AF2 prediction for each oligomeric state (1–4 for monomers and dimers, and 1–5 for trimers and tetramers). Only proteins for which the monomer had pLDDT > 90 are shown. For visualization, the predicted successes (top) and failures (bottom) were separated into two plots. Success is defined when the peak of the homo-oligomeric state scan matches the annotation, or the pTMscore of the next oligomer state is substantially lower (−0.1). b, For each of the annotated assemblies, the pTMscore of monomeric prediction is compared with the max pTMscore of non-monomeric prediction. c, Monomer prediction failure. Two monomers were predicted to be homo-dimers. For the first case (PDB: 1BKZ), the prediction matched the asymmetric unit (shown as blue/green and prediction in white). For the second case (PDB: 1BWZ), the prediction matched one of the crystallographic interfaces. d, 3TDT trimer was predicted to be a tetramer. Although the interface is technically correct, for this c-symmetric protein, the pTMscore was not able to discriminate between 3 and 4 copies. e, Comparison of docking quality between AF2 (x axis) and a standard docking tool GRAMM (y axis). Comparisons were made using the DockQ score. Models with a DockQ score that was higher than 0.23 are assumed to be acceptable according to the Critical Assessment for Predicted Interactions (CAPRI) criteria (marked outside the shaded area). Black circles indicate the complex was well modeled by both methods. The average DockQ score and the number of acceptable or better models are shown in the axis labels. It should be noted here that AF2 both folds and docks the proteins, whereas GRAMM only docks them. f, Examples of AF2-predicted interactions mediated by regions of intrinsic disorder.

We next examined the Dockground 4.3 heterodimeric benchmark set43. We predicted complex structures using the DeepMind default dataset and the small Big Fantastic Database (BFD) database. This method does not include any ‘pairing’ of interacting chains, as was used in earlier fold-and-dock approaches. The docking quality was evaluated using DockQ44,45. Only one model for each target was made, and a maximum of three recycles were allowed. In Figure 5e, it can be seen that the performance is far superior to traditional docking methods, with 31% of correctly predicted protein complex models, compared with 7% using GRAMM, a standard shape-complementarity docking method44.

Finally, we studied examples of complexes containing IDPs/IDRs that adopt a stable structure upon binding. IDRs often bind through short linear motifs (SLiMs), recognizing folded domains driven by a few residues. The longer IDRs can contain arrays of SLiMs and can also form stable structures upon binding to other IDRs without a structured template. We selected 14 cases of complexes involving IDRs with known structures and analyzed their distinguishing features compared with the experimental complex (Fig. 5f contains selected examples and Supplementary Figs. 10 and 11 show all examples). In general, AF2 performs well at predicting SLiMs that fit into a well-defined binding pocket driven by hydrophobic interactions, such as the SUMO interacting motif of RanBP2. Longer IDRs, which frequently contain tandem motifs, are often challenging, especially if they have a symmetric structure. For the RelA–CBP interaction, AF2 correctly finds the binding groove, but fits the IDR in a reverse orientation. AF2 also performs well on complexes in which IDRs are part of a multi-IDR single folding unit, such as the E2F1–DP1–Rb trimer; however, building complexes for proteins with highly unusual residue compositions, such as collagen triple helices, often fail. We provide a detailed description of the 14 examples in Supplementary Figures 10 and 11 and Supplementary Table 5 and detail the factors that enable or hinder successful predictions.

Evaluation of AlphaFold2 models for use in experimental model building

The accuracy of AF2 predictions provides opportunities for their use in experimental model building: (1) AF2 models could be used for molecular replacement or docking into cryo-EM density, experimental phasing and/or ab initio model building; and (2) they could be used as reference points to improve existing low-resolution structures. These use cases will typically involve the use of conformational restraints, for example to maintain the local geometry of domains while flexibly fitting a large multi-domain model, or to restrain the local geometry of an existing model of an AF2-derived reference to highlight and correct likely sites of error. It is critical to use restraint schemes designed to avoid forcing the model into conformations that clearly disagree with the data. Typically, this is achieved through some form of top-out restraint, for which the applied bias drops off at large deviations from the target. Here, we take advantage of the fact that AF2 models typically include very strong predictions of their own local uncertainty to adjust per-restraint weighting of the adaptive restraints recently implemented in ISOLDE46 (see Methods). For the two case studies discussed below, a comparison of validation statistics for the original and revised models is provided in Supplementary Table 6.

As an example of the improvement of existing structures, we used the eukaryotic translation initiation factor (eIF) 2B bound to substrate eIF2 (6O85)47,48. The eIF2B complex is a decamer comprising two copies each of five unique chains. It displays allosteric communication between physically distant substrate-, ligand- and inhibitor-binding sites. eIF2 is a heterotrimer of three unique chains. We analyzed a 0.4-MDa co-complex enzyme-active state captured by cryo-EM at an overall resolution of 3 Å (ref. 49). Rigid-body alignment of AF2 models to their corresponding experimental chains (Fig. 6a) showed overall excellent agreement, with the largest deviations corresponding to correctly folded domains with flexible connections to their neighbors. Other mismatched smaller regions corresponded to either register errors in the original model or flexible loops and tails. Each chain was restrained to its corresponding AF2 model using ISOLDE’s reference-model distance and torsion restraints, with each distance restraint adjusted according to pLDDT. Future work will explore the use of the predicted aligned error (PAE) matrix for this purpose, and weighing of torsion restraints according to pLDDT. Simple energy minimization and equilibration of the restrained model at 20 K corrected the majority of local geometry issues (for example, Fig. 6b,c); a high-confidence prediction for the C-terminal domain of chains I and J allowed us to add this into previously untraceable low-resolution density (Fig. 6d, left of the dashed line). We emphasize that detailed manual inspection remains necessary to find and correct larger errors in the experimental model, sites of disagreement arising from conformational variability and sites where high-confidence predictions are in fact incorrect. An example of the latter is the side chain of Trp A111, which, despite its high confidence (pLDDT = 86.1), was modeled incorrectly by AF2 (Fig. 6f).

Fig. 6: Application of AF2 predictions to modeling into cryo-EM or crystallographic data.
figure 6

a, AF2 predictions for individual chains in 6O85, aligned to the original model and colored by Cα–Cα distance, with the map (EMD-0651) contoured at 6.5 σ. Red domains at the bottom were correctly folded but misplaced owing to flexibility; smaller regions of red correspond either to flexible tails or register errors in the original model. b,c, Use of adaptive distance and torsion restraints to correct problematic geometry in the original model. The models before (b) and after (c) refitting are shown; satisfied distance restraints are hidden for clarity. d, Owing to very poor local resolution and lack of homologs, the carboxy-terminal domain in chain J (left of the dashed line) was previously left unmodeled. This domain was predicted with high confidence by AF2 (mean pLDDT = 83.0), and fit readily into the available density. e, High-confidence regions may still contain subtle errors that are difficult or impossible to detect in the absence of experimental data. The side chain of Trp A111 (pLDDT = 86.1) was modeled backwards (blue), forming an H-bond with Asp A77; the final model fitted to the map (gray) instead forms an H-bond with Glu A81. f, Rebuilding the recent 3.3-Å crystal structure 7OGG, starting from molecular replacement with AF2 models, dramatically improved model completeness. Blue, residues identified in original model; yellow sticks, residues modeled as unknown in the original model; red, residues identified in rebuilt model. g, Helix modeled as unknown (residues 558–573 of chain R, red), surrounded by unmodeled density (3 σ mFo-DFc, green(+), red(–); +2 σ sharpened 2mFo-DFc, cyan surface; +1.5 σ unsharpened 2mFo-DFc difference map (Fo and Fc are the experimentally measured and model-based amplitudes, D is the Sigma-A weighting factor and m is the figure of merit), cyan wireframe; +5 σ anomalous difference map, purple surface and arrows). h, Final model, with anomalous difference blobs corresponding to selenomethionine residues 213 and 217 of chain Q and with the previously unmodeled density filled; this region was predicted with an average pLDDT of 88, and required only minor side chain corrections to fit the density.

To explore the use of AF2 structures for solving and refining new structures, and to map out suitable workflows, we attempted to recapitulate the recent 3.3-Å crystal structure of the Saccharomyces cerevisiae Nse5/6 complex (7OGG)50. This was not included in the AF2 training set, and no existing structures have ≥30% identity to either chain. Originally solved using selenomethionine experimental phasing, the combination of low-resolution and anisotropy (ΔB = 80 Å2) meant that, although the core of the complex was confidently and correctly modeled, only 583 out of 850 total residues were definitively modeled by the authors, with a further 65 residues traced as unknown sequence and one peripheral 27-residue helix modeled out of register. For testing purposes, we discarded this model and used the AF2 predictions for molecular replacement (MR). MR requires very close correspondence between atom positions in the search model and in the crystal; separation into individual rigid domains and trimming of flexible loops is a necessity. We used the PAE matrix to extract a single rigid core from each chain (see Methods) and performed MR in Phaser51, leading to a clear solution with translation function Z-score (TFZ) = 28.2 and log-likelihood gain (LLG) = 884 (see Methods).

Currently, a refined MR solution is typically used as the starting point for some combination of automatic and manual building of missing portions into the density. In many cases, however, it appears that AF2 predictions will support a more ‘top-down’ approach, in which all residues predicted with at least moderate confidence are present in the initial model. To explore this, we trimmed the predicted chains to exclude residues with pLDDT ≤ 50 and aligned the result to the MR solution, setting the occupancies of all atoms not used for MR to zero. This was used as the starting point for rebuilding in ISOLDE; here, zero-occupancy atoms do not contribute to structure factor calculations or bulk solvent masking, but still take part in molecular interactions and are attracted into the map. The model was subjected to three rounds of end-to-end inspection and rebuilding interspersed with refinement with phenix.refine52. In the initial round, zero-occupancy residues fitting the map were reinstated to full occupancy, and residues that seemed to be truly unresolved were deleted; a small number of these were re-introduced in subsequent rounds. The total time spent was approximately one working day; the final model (Fig. 6f–h) increased the number of modeled, identified residues from 600 to 818, slightly improved overall geometry and reduced the Rfree from 0.317 to 0.295. With few exceptions (primarily at heterodimer and symmetry interfaces), rebuilding was limited to minor side chain adjustments.



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