PreprintCited by 1,076Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index; 19 of the 29 sources its claims cite were checked for support.

Improved prediction of protein-protein interactions using AlphaFold2

Open access
Bryant, P., Pozzati, G., Elofsson, A.·2021·Nature Communications

Preprint — published as 10.1038/s41467-022-28865-w · Paper page

Abstract

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimized multiple sequence alignments, generate models with acceptable quality (DockQ≥0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR. The protocol can be found at: https://gitlab.com/ElofssonLab/FoldDock .

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

AlphaFold2, combined with optimized paired MSAs, can fold-and-dock many heterodimeric protein complexes and predict model quality from the resulting interfaces. The derived pDockQ score separates acceptable from incorrect models and interacting from non-interacting proteins with state-of-art accuracy.

Key findings

  • On the test set, AF2 + paired MSAs reached a success rate of 57.8% (856/1481), rising to 61.7% when five seeds were ranked by pDockQ; block diagonalization + paired MSAs with top-ranked models reached 62.7%. The best development-set result was 39.4% for AF2 + paired MSAs.
  • pDockQ, a sigmoidal fit to interface pLDDT × log(interface contacts), predicted DockQ with an average error of 0.11 and separated acceptable models (DockQ ≥ 0.23) from incorrect ones with AUC 0.95; 31% of models were acceptable at 1% error and 54% at 10% error.
  • For interacting vs non-interacting proteins (1481 positives, 5694 negatives), pDockQ gave AUC 0.87 and identified 51% of interacting pairs at 1% FPR, compared with 27%, 18%, and 13% for interface pLDDT, interface contacts, and interface residues, respectively.
  • Performance varied by group: bacteria 73.7%, archaea 84.5%, eukarya 61%, virus 60%; E. coli 76.4%, S. cerevisiae 66.2%, H. sapiens 58.1%; helix interfaces 62%, sheet 59%, loop 53%; largest interface tertile 74% vs smallest 47%. AF2 outperformed tested docking methods, while AF-multimer reached 72.2% but was trained on the same data as the test set.

Methods

  • AF2 was used to model heterodimeric complexes with a chain break, no templates, and relaxation off. The development set contained 216 Dockground complexes; the test set contained 1481 complexes; additional sets included six CASP14 heterodimers, eight novel PDB complexes, and 5694 non-interacting pairs (3989 E. coli + 1705 mammalian).
  • MSA strategies compared default AF2 MSAs, organism-paired MSAs, block diagonalization MSAs, and combinations; AF2 model_1 vs model_1_ptm and different recycle/ensemble configurations were tested, with five seeds for the best strategy.
  • Models were assessed with DockQ (acceptable ≥ 0.23), ROC/AUC, and pDockQ; comparisons included RoseTTAFold, GRAMM, MDockPP, TMdock, and AF-multimer.

Limitations

  • Stated: only heterodimeric states are considered; alternative oligomeric states and larger biological assemblies are outside scope. Stated: interaction/non-interaction separation needs extension to compare interactions within individual organisms. Stated: the development vs test success-rate discrepancy (39.4% vs 57.8%) suggests unknown selection bias, and the development set is much smaller (216 vs 1481).
  • Evident: AF-multimer comparison is difficult because it was trained using the same data as the test set. Evident: the initial non-interacting set was only E. coli while positives were mainly eukaryotic, so the authors added mammalian negatives to obtain a more realistic estimate.
  • Evident: several analyses excluded complexes due to memory limits, MSA errors, missing Cβs, or DSSP failures (e.g., final test set 1481; DSSP 1391; RoseTTAFold 1455; AF-multimer 1458), which may affect generalizability.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1101/2021.09.15.460468

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1 DockQ scores for the test set (n = 1481 for all but RF, n = 1455). Distribution of DockQ scores as boxplots for different modelling strategies on the test set. Boxes encompass data quartiles, horizontal lines mark the medians and upper and lower whiskers indicate respectively maximum and minimum values for each distribution. All AF2 models have been run with the same neural network configuration (m1-10-1). Outlier points are not displayed here. AF2, refers to running AF2 using the default AF2 MSAs, “Paired” refers to using MSAs paired using information about species and “Block” refers to using block diagonalization MSAs.
Fig. 1 DockQ scores for the test set (n = 1481 for all but RF, n = 1455). Distribution of DockQ scores as boxplots for different modelling strategies on the test set. Boxes encompass data quartiles, horizontal lines mark the medians and upper and lower whiskers indicate respectively maximum and minimum values for each distribution. All AF2 models have been run with the same neural network configuration (m1-10-1). Outlier points are not displayed here. AF2, refers to running AF2 using the default AF2 MSAs, “Paired” refers to using MSAs paired using information about species and “Block” refers to using block diagonalization MSAs.CC BY · PubMed Central
Fig. 2 Model quality metrics and multiple model ranking. a ROC curve as a function of different metrics for the test dataset (n = 1481, first run). Cβs within 8 Å from each other from different chains are used to define the interface. IF_plDDT is the average plDDT of interface residues, min plDDT per chain is the minimum average plDDT of both chains, average plDDT is the average of the entire complex and IF_contacts and IF_residues are the number of interface residues and contacts respectively. pDockQ is a sigmoidal fit to the combined metric IF_plDDT⋅log(IF_contacts) fitted to predict DockQ as the target score, see C. b Average interface plDDT vs the logarithm of the interface contacts coloured by DockQ score on the test set (n = 1481). Increasing both the number of interface contacts and average interface plDDT results in higher DockQ scores. c Using the combined metric IF_plDDT⋅log(IF_contacts), we fit a sigmoidal curve towards the DockQ scores on the test set (n = 1481), enabling predicting the DockQ score in a continuous manner (pDockQ). The average error overall is 0.14 DockQ score. d Impact of different initialisations on the modelling outcome in terms of DockQ score on the test dataset (n = 1481). The maximal and minimal scores are plotted against the top-ranked models using the pDockQ scores for the AF2 + paired MSAs, m1-10-1.
Fig. 2 Model quality metrics and multiple model ranking. a ROC curve as a function of different metrics for the test dataset (n = 1481, first run). Cβs within 8 Å from each other from different chains are used to define the interface. IF_plDDT is the average plDDT of interface residues, min plDDT per chain is the minimum average plDDT of both chains, average plDDT is the average of the entire complex and IF_contacts and IF_residues are the number of interface residues and contacts respectively. pDockQ is a sigmoidal fit to the combined metric IF_plDDT⋅log(IF_contacts) fitted to predict DockQ as the target score, see C. b Average interface plDDT vs the logarithm of the interface contacts coloured by DockQ score on the test set (n = 1481). Increasing both the number of interface contacts and average interface plDDT results in higher DockQ scores. c Using the combined metric IF_plDDT⋅log(IF_contacts), we fit a sigmoidal curve towards the DockQ scores on the test set (n = 1481), enabling predicting the DockQ score in a continuous manner (pDockQ). The average error overall is 0.14 DockQ score. d Impact of different initialisations on the modelling outcome in terms of DockQ score on the test dataset (n = 1481). The maximal and minimal scores are plotted against the top-ranked models using the pDockQ scores for the AF2 + paired MSAs, m1-10-1.CC BY · PubMed Central
Fig. 3 DockQ distributions for test dataset (n = 1481) tertiles. a Distribution of DockQ scores for three sets of interfaces with the majority of Helix, Sheet and Coil secondary structures. b Distribution of DockQ scores for tertiles derived from the distribution of contact counts in docking model interfaces. c Distribution of DockQ scores for tertiles derived from the distribution of Paired MSAs Neff scores. d Distribution of DockQ scores for the top three organisms H. sapiens, S. cerevisiae and E. coli.
Fig. 3 DockQ distributions for test dataset (n = 1481) tertiles. a Distribution of DockQ scores for three sets of interfaces with the majority of Helix, Sheet and Coil secondary structures. b Distribution of DockQ scores for tertiles derived from the distribution of contact counts in docking model interfaces. c Distribution of DockQ scores for tertiles derived from the distribution of Paired MSAs Neff scores. d Distribution of DockQ scores for the top three organisms H. sapiens, S. cerevisiae and E. coli.CC BY · PubMed Central
Fig. 4 Predicted and native structures from the set of novel proteins without templates. The native structures are represented as grey ribbons. a Docking of 7EIV chains A (blue) and C (green) (DockQ = 0.76). b Docking of 7MEZ chains A (blue) and B (green) (DockQ = 0.53). c Prediction of structure 7EL1 chains A (blue) and E (green) (DockQ = 0.01). The DNA going through chain A is coloured in orange. d Docking of 7LF7 chains A (blue) and M (magenta) (DockQ = 0.02) and chains B (green) and M (magenta) (DockQ = 0.02).
Fig. 4 Predicted and native structures from the set of novel proteins without templates. The native structures are represented as grey ribbons. a Docking of 7EIV chains A (blue) and C (green) (DockQ = 0.76). b Docking of 7MEZ chains A (blue) and B (green) (DockQ = 0.53). c Prediction of structure 7EL1 chains A (blue) and E (green) (DockQ = 0.01). The DNA going through chain A is coloured in orange. d Docking of 7LF7 chains A (blue) and M (magenta) (DockQ = 0.02) and chains B (green) and M (magenta) (DockQ = 0.02).CC BY · PubMed Central
Fig. 5 Discrimination of interacting (n = 1481) and non-interacting (n = 5694) proteins. a The ROC curve as a function of different metrics for discriminating between interacting and non-interacting proteins. IF_plDDT is the average plDDT in the interface, min plDDT per chain is the minimum average plDDT of both chains, average plDDT is the average of the entire complex and IF_contacts and IF_residues are the number of interface residues and contacts respectively. pDockQ is a sigmoidal fit to this with DockQ as the target score, as described above. b–d Distribution of the top discriminating features average interface plDDT (b), the number of interface contacts (c), and d the combination of these (IF_plDDT⋅log(IF_contacts)) and the pDockQ for interacting (non-grey) and non-interacting proteins (grey).
Fig. 5 Discrimination of interacting (n = 1481) and non-interacting (n = 5694) proteins. a The ROC curve as a function of different metrics for discriminating between interacting and non-interacting proteins. IF_plDDT is the average plDDT in the interface, min plDDT per chain is the minimum average plDDT of both chains, average plDDT is the average of the entire complex and IF_contacts and IF_residues are the number of interface residues and contacts respectively. pDockQ is a sigmoidal fit to this with DockQ as the target score, as described above. b–d Distribution of the top discriminating features average interface plDDT (b), the number of interface contacts (c), and d the combination of these (IF_plDDT⋅log(IF_contacts)) and the pDockQ for interacting (non-grey) and non-interacting proteins (grey).CC BY · PubMed Central
Fig. 6 Comparison of different MSAs. a Depiction of MSAs generated by AF2 and the paired version matched using organism information. Both AF and paired representations are sections containing 10% of the sequences aligned in the original MSA. Concatenated chains are separated by a vertical line (magenta). The visualisations were made using Jalview version 2.11.1.449. b Docking visualisations for PDB ID 5D1M with the model/native chains A in blue/grey and B in green/magenta using the three different MSAs in (a). The DockQ scores are 0.01, 0.02 and 0.90 for AF2, paired, and AF2 + paired MSAs, respectively.
Fig. 6 Comparison of different MSAs. a Depiction of MSAs generated by AF2 and the paired version matched using organism information. Both AF and paired representations are sections containing 10% of the sequences aligned in the original MSA. Concatenated chains are separated by a vertical line (magenta). The visualisations were made using Jalview version 2.11.1.449. b Docking visualisations for PDB ID 5D1M with the model/native chains A in blue/grey and B in green/magenta using the three different MSAs in (a). The DockQ scores are 0.01, 0.02 and 0.90 for AF2, paired, and AF2 + paired MSAs, respectively.CC BY · PubMed Central

Verification

Claims the paper makes

39 Own result · 21 Own method · 13 Own interpretation · 12 Couldn’t confirm · 4 Supported · 4 No full text of the sources · 4 No basis given · 1 Supported so far · 1 Partly supported · 1 Not supported · 1 Sources not found · accuracy not yet measured

Show all 39 claims

  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 36
  • The quote only partly states this claim. 1
62 of 62
  • The authors state this as an assumption.

    All residues in an interface contribute to the interaction energy.

    “Assuming that all residues in an interface contribute to the interaction energy could explain why larger interfaces are more likely to be correctly predicted.”
  • The paper relies on this without support.

    The 31% success rate at 1% FPR observed on the test set applies to the human reference interactome.

    “If 31% of these can be predicted at an error rate of 1%, this results in the structure of 19,842 human heterodimeric protein structures.”
  • The paper relies on this without support.

    The 3989 E. coli proteins used as negatives are truly non-interacting.

    “Using the best separator from the model ranking, the pDockQ, it is possible to distinguish the 3989 non-interacting proteins from Escherichia coli and the 1481 truly interacting proteins from the test set with an AUC of 0.87.”
  • The paper relies on this without support.

    The 1481 test set proteins used as positives are truly interacting.

    “Using the best separator from the model ranking, the pDockQ, it is possible to distinguish the 3989 non-interacting proteins from Escherichia coli and the 1481 truly interacting proteins from the test set with an AUC of 0.87.”
  • The paper relies on this without support.

    AF2 structure prediction is a non-deterministic process.

    “Since structure prediction with AF2 is a non-deterministic process, we generate five models initiated with different seeds.”
  • The paper relies on this without support.

    Ranking the five AF2 models for each complex by the number of residues in the interface gives the best result.

    “We rank the five models for each complex by the number of residues in the interface, giving the best result.”
6 of 6

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Citations

Cites

Show all 66 references

Research works printed but not linked: 1

  1. 1997, Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex, Proteins, Suppl 1, 226

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Metadata

Authors
Bryant, P., Pozzati, G., & Elofsson, A.
Affiliations
Stockholm University (SE)
Corresponding author
  • Bryant, P.
  • Elofsson, A.
Venue
Nature Communications
Publisher
openRxiv
Published
2021-09-15
Volume
13
Issue
1
Pages
1265-1265
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4220824576
Cited by
1,022
References
66
Citations by year
2026: 170 · 2025: 269 · 2024: 292 · 2023: 203 · 2022: 83 · 2021: 5
FWCI
62.72
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Research Square (submittedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Swedish e-Science Research Centre
  • Knut och Alice Wallenbergs Stiftelse (COVID 19)
  • Vetenskapsrådet (2016-06301, Berzelius-2021-29)
  • Stockholms Universitet
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
protein-protein interaction, protein complex structure prediction, AlphaFold2, multiple sequence alignment, protein interface prediction
MeSH terms
Protein Conformation, Proteins, Computational Biology

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