RESEARCH ARTICLES

Cite as: M. Baek et al., Science
10.1126/science.abj8754 (2021).

Accurate prediction of protein structures and interactions
using a three-track neural network
Minkyung Baek1,2, Frank DiMaio1,2, Ivan Anishchenko1,2, Justas Dauparas1,2, Sergey Ovchinnikov3,4,
Gyu Rie Lee1,2, Jue Wang1,2, Qian Cong5,6, Lisa N. Kinch7, R. Dustin Schaeffer6, Claudia Millán8,
Hahnbeom Park1,2, Carson Adams1,2, Caleb R. Glassman9,10, Andy DeGiovanni12, Jose H. Pereira12,
Andria V. Rodrigues12, Alberdina A. van Dijk13, Ana C. Ebrecht13, Diederik J. Opperman14, Theo Sagmeister15,
Christoph Buhlheller15,16, Tea Pavkov-Keller15,17, Manoj K. Rathinaswamy18, Udit Dalwadi19, Calvin K. Yip19,
John E. Burke18, K. Christopher Garcia9,10,11,20, Nick V. Grishin6,21,7, Paul D. Adams12,22, Randy J. Read8,
David Baker1,2,23*
1Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. 2Institute for Protein Design, University of Washington, Seattle, WA 98195, USA. 3Faculty of

DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction
assessment conference. We explored network architectures incorporating related ideas and obtained thebest performance with a three-track network in which information at the 1D sequence level, the 2D distance
map level, and the 3D coordinate level is successively transformed and integrated. The three-track network
produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the
rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides
insights into the functions of proteins of currently unknown structure. The network also enables rapid
generation of accurate protein-protein complex models from sequence information alone, short circuiting
traditional approaches which require modeling of individual subunits followed by docking. We make themethod available to the scientific community to speed biological research.
The prediction of protein structure from amino acid sequence
information alone has been a longstanding challenge. The biannual Critical Assessment of Structure (CASP) meetings
have demonstrated that deep learning methods such as AlphaFold (1, 2) and trRosetta (3), that extract informationfrom the large database of known protein structures in the
PDB, outperform more traditional approaches that explicitly
model the folding process. The outstanding performance of
DeepMind’s AlphaFold2 in the recent CASP14 meeting(https://predictioncenter.org/casp14/zscores_final.cgi) left
the scientific community eager to learn details beyond theoverall framework presented and raised the question ofwhether such accuracy could be achieved outside of a worldleading deep learning company. As described at the CASP14
conference, the AlphaFold2 methodological advancesFirst release: 15 July 2021

included 1) starting from multiple sequence alignments(MSAs) rather than from more processed features such as inverse covariance matrices derived from MSAs, 2) replacementof 2D convolution with an attention mechanism that better
represents interactions between residues distant along thesequence, 3) use of a two-track network architecture in which
information at the 1D sequence level and the 2D distance map
level is iteratively transformed and passed back and forth, 4)
use of an SE(3)-equivariant Transformer network to directly
refine atomic coordinates (rather than 2D distance maps asin previous approaches) generated from the two-track network, and 5) end-to-end learning in which all network parameters are optimized by backpropagation from the final
generated 3D coordinates through all network layers back to
the input sequence.

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Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138, USA. 4John Harvard Distinguished Science Fellowship Program, Harvard University,
Cambridge, MA 02138, USA. 5Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
6Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA. 7Howard Hughes Medical Institute, University of Texas Southwestern Medical
Center, Dallas, TX, USA. 8Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. 9Program in Immunology,
Stanford University School of Medicine, Stanford, CA 94305, USA. 10Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA
94305, USA. 11Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA. 12Molecular Biophysics & Integrated Bioimaging Division,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA. 13Department of Biochemistry, Focus Area Human Metabolomics, North-West University, 2531 Potchefstroom,
South Africa. 14Department of Biotechnology, University of the Free State, 205 Nelson Mandela Drive, Bloemfontein 9300, South Africa. 15Institute of Molecular Biosciences,
University of Graz, Humboldtstrasse 50, 8010 Graz, Austria. 16Medical University of Graz, Graz, Austria. 17BioTechMed-Graz, Graz, Austria. 18Department of Biochemistry
and Microbiology, University of Victoria, Victoria, BC, Canada. 19Life Sciences Institute, Department of Biochemistry and Molecular Biology, The University of British
Columbia, Vancouver, BC, Canada. 20Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 21Department of Biochemistry,
University of Texas Southwestern Medical Center, Dallas, TX, USA. 22Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA. 23Howard
Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
*Corresponding author. Email: dabaker@uw.edu

First release: 15 July 2021

structure modeling step.
The three-track models with attention operating at the 1D,
2D, and 3D levels and information flowing between the threelevels were the best models we tested (Fig. 1B), clearly outperforming the top 2 server groups (Zhang-server and
BAKER-ROSETTASERVER), BAKER human group (ranked
second among all groups), and our two-track attention models on CASP14 targets. As in the case of AlphaFold2, the correlation between multiple sequence alignment depth andmodel accuracy is lower for RoseTTAFold than for trRosetta
and other methods tested at CASP14 (fig. S2). The performance of the three-track model on the CASP14 targets was
still not as good as AlphaFold2 (Fig. 1B). This could reflect
hardware limitations that limited the size of the models we
could explore, alternative architectures or loss formulations,or more intensive use of the network for inference. DeepMind
reported using several GPUs for days to make individual predictions, whereas our predictions are made in a single passthrough the network in the same manner that would be used
for a server; following sequence and template search(~1.5 hours), the end-to-end version of RoseTTAFold requires~10 min on an RTX2080 GPU to generate backbone coordinates for proteins with less than 400 residues, and the pyRosetta version requires 5 min for network calculations on asingle RTX2080 GPU and an hour for all-atom structure generation with 15 CPU cores. Incomplete optimization due tocomputer memory limitations and neglect of side chain information likely explain the poorer performance of the endto-end version compared to the pyRosetta version (Fig. 1B;
the latter incorporates side chain information at the all-atom
relaxation stage); since SE(3)-equivariant layers are used in
the main body of the three-track model, the added gain fromthe final SE(3) layer is likely less than in the AlphaFold2 case.
We expect the end-to-end approach to ultimately be at leastas accurate once the computer hardware limitations are overcome, and side chains are incorporated.
The improved performance of the three-track models over
the two-track model with identical training sets, similar attention-based architectures for the 1D and 2D tracks, andsimilar operations in inference (prediction) mode suggeststhat simultaneously reasoning at the multiple sequencealignment, distance map, and three-dimensional coordinate
representations can more effectively extract sequence-structure relationships than reasoning over only MSA and distance map information. The relatively low compute costmakes it straightforward to incorporate the methods in a
public server and predict structures for large sets of proteins,for example, all human GPCRs, as described below.
Blind structure prediction tests are needed to assess any
new protein structure prediction method, but CASP is held
only once every two years. Fortunately, the Continuous Automated Model Evaluation (CAMEO) experiment (7) tests

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Network architecture development
Intrigued by the DeepMind results, and with the goal of increasing protein structure prediction accuracy for structural
biology research and advancing protein design (4), we explored network architectures incorporating different combinations of these five properties. In the absence of a publishedmethod, we experimented with a wide variety of approaches
for passing information between different parts of the networks, as summarized in the methods and table S1. We succeeded in producing a “two-track” network with information
flowing in parallel along a 1D sequence alignment track and a
2D distance matrix track with considerably better performance
than trRosetta (BAKER-ROSETTASERVER and BAKER in Fig.
1B), the next best method after AlphaFold2 in CASP14(https://predictioncenter.org/casp14/zscores_final.cgi).
We reasoned that better performance could be achievedby extending to a third track operating in 3D coordinate
space to provide a tighter connection between sequence, residue-residue distances and orientations, and atomic coordinates. We constructed architectures with the two levels of the
two-track model augmented with a third parallel structure
track operating on 3D backbone coordinates as depicted inFig. 1A (see methods and fig. S1 for details). In this architecture, information flows back and forth between the 1D aminoacid sequence information, the 2D distance map, and the 3D
coordinates, allowing the network to collectively reason
about relationships within and between sequences, distances,and coordinates. In contrast, reasoning about 3D atomic coordinates in the two-track AlphaFold2 architecture happens
after processing of the 1D and 2D information is complete(although end-to-end training does link parameters to someextent). Because of computer hardware memory limitations,
we could not train models on large proteins directly as thethree-track models have many millions of parameters; instead, we presented to the network many discontinuous cropsof the input sequence consisting of two discontinuous sequence segments spanning a total of 260 residues. To generate final models, we combined and averaged the 1D featuresand 2D distance and orientation predictions produced foreach of the crops and then used two approaches to generate
final 3D structures. In the first, the predicted residue-residue
distance and orientation distributions are fed into pyRosetta(5) to generate all-atom models. In the second, the averaged
1D and 2D features are fed into a final SE(3)-equivariant layer(6), and following end-to-end training from amino acid sequence to 3D coordinates, backbone coordinates are generated directly by the network (see methods). We refer to these
networks, which also generate per residue accuracy predictions, as RoseTTAFold. The first has the advantage of requiring lower memory (for proteins over 400 residues, 8GB rather
than 24GB) GPUs at inference time and producing full side
chain models but requires CPU time for the pyRosettaavailable methods prompted us to test whether it could help
solve previously unsolved challenging MR problems and improve the solution of borderline cases. Four recent crystallographic datasets (summarized, including resolution limits, intable S2), which had eluded solution by MR using models
available in the PDB, were reanalyzed using RoseTTAFoldmodels: glycine N-acyltransferase (GLYAT) from Bos taurus(fig. S5A), a bacterial oxidoreductase (fig. S5B), a bacterial
surface layer protein (SLP) (Fig. 2A) and the secreted protein
Lrbp from the fungus Phanerochaete chrysosporium (Fig. 2B
and fig. S5C). In all four cases, the predicted models had sufficient structural similarity to the true structures that led tosuccessful MR solutions [see methods for details; the per-residue error estimates by DeepAccNet (12) allowed the moreaccurate parts to be weighted more heavily]. The increased
prediction accuracy was critical for success in all cases, asmodels made with trRosetta did not yield MR solutions.
To determine why the RoseTTAFold models were successful, where PDB structures had previously failed, we compared
the models to the crystal structures we obtained. The images
in Fig. 2A and fig. S5 show that in each case, the closest homolog of the known structure was a much poorer model thanthe RoseTTAFold model; in the case of SLP, only a distantmodel covering part of the N-terminal domain (38% of thesequence) was available in the PDB, while no homologs of the
C-terminal domain of SLP or any portion of Lrbp could be
detected using HHsearch (13).
Building atomic models of protein assemblies from cryoEM maps can be challenging in the absence of homologs withknown structures. We used RoseTTAFold to predict the p101
Gᵦᵧ binding domain (GBD) structure in a heterodimeric PI3Kᵧ
complex. The top HHsearch hit has a statistically insignificant E-value of 40 and only covers 14 residues out of 167 residues. The predicted structure could readily fit into theelectron density map despite the low local resolution [Fig. 2C,
top; trRosetta failed to predict the correct fold with the same
MSA input (fig. S6)]. The Cɑ-RMSD between the predicted
and the final refined structure is 3.0 Å over the beta-sheets(Fig. 2C, bottom).

Enabling experimental protein structure determination
With the recent considerable progress in protein structure
prediction, a key question is what accurate protein structure
models can be used for. We investigated the utility of the RoseTTAFold to facilitate experimental structure determinationby X-ray crystallography and cryo-electron microscopy and tobuild models providing biological insights for key proteins ofcurrently unknown structures.
Solution of X-ray structures by molecular replacement(MR) often requires quite accurate models. The much higher
accuracy of the RoseTTAFold method than currently

Providing insights into biological function
Experimental structure determination can provide considerable insight into biological function and mechanism. We investigated whether structures generated by RoseTTAFoldcould similarly provide new insights into function. We focused on two sets of proteins: first, G protein-coupled receptors of currently unknown structure, and second, a set ofhuman proteins implicated in disease. Benchmark tests on
GPCR sequences with determined structures showed that
RoseTTAFold models for both active and inactive states canbe quite accurate even in the absence of close homologs withknown structures [and better than those in current GPCRFirst release: 15 July 2021

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structure prediction servers blindly on protein structures as
they are submitted to the PDB. RoseTTAFold has been evaluated since May 15th, 2021 on CAMEO; over the 69 medium
and hard targets released during this time (May 15th, 2021 ~June 19th, 2021), it outperformed all other servers evaluatedin the experiment including Robetta (3), IntFold6-TS (8),BestSingleTemplate (9), and SWISS-MODEL (10) (Fig. 1C).
We experimented with approaches for further improving
accuracy by more intensive use of the network during sampling. Since the network can take as input templates ofknown structures, we experimented with a further couplingof 3D structural information and 1D sequence information by
iteratively feeding the predicted structures back into the network as templates and random subsampling from the multiple sequence alignments to sample a broader range ofmodels. These approaches generated ensembles containing
higher accuracy models, but the accuracy predictor was notable to consistently identify models better than those generated by the rapid single pass method (fig. S3). Nevertheless,we suspect that these approaches can improve model performance and are carrying out further investigations along theselines.
In developing RoseTTAFold, we found that combining
predictions from multiple discontinuous crops generated
more accurate structures than predicting the entire structure
at once (fig. S4A). We hypothesized that this arises from selecting the most relevant sequences for each region from thevery large number of aligned sequences often available(fig. S4B). To enable the network to focus on the most relevant sequence information for each region while keeping access to the full multiple sequence alignment in a morememory efficient way, we experimented with the Perceiverarchitecture (11), updating smaller seed MSAs (up to 100 sequences) with extra sequences (thousands of sequences)through cross-attention (fig. S4C). Current RoseTTAFold onlyuses the top 1000 sequences due to memory limitations; withthis addition, all available sequence information can be used
(often over 10,000 sequences). Initial results are promising(fig. S4D), but more training will be required for rigorouscomparison.

First release: 15 July 2021

orthologs line one side of the barrel and likely interact withthe metalloprotease (Fig. 3D).
Transmembrane spanning Ceramide synthase (CERS1) isa key enzyme in sphingolipid metabolism which uses acylCoA to generate ceramides with various acyl chain lengths
that regulate differentiation, proliferation, and apoptosis(27). Structure information is not available for any of the CerS
enzymes or their homologs, and the number and orientationof transmembrane helices (TMH) are not known (28). The
RoseTTAFold CERS1 model for residues 98 to 304 (Pfam TLC
domain) (29) includes six TMH that traverse the membrane
in an up and down arrangement (Fig. 3E). A central crevice
extends into the membrane and is lined with residues required for activity (His182 and Asp213) (30) or conserved(W298), as well as a pathogenic mutation (H183Q) found in
progressive myoclonus epilepsy and dementia that decreases
ceramide levels (31). This active site composition (His182,
Asp 213, and potentially a neighboring Ser212) suggests testable reaction mechanisms for the enzyme (Fig. 3F).
Direct generation of protein-protein complex models
The final layer of the end-to-end version of our three-track
network generates 3D structure models by combining features from discontinuous crops of the protein sequence (twosegments of the protein with a chain break between them).
We reasoned that because the network can seamlessly handle
chain breaks, it might be able to predict the structure of protein-protein complexes directly from sequence information.
Rather than providing the network the sequence of a single
protein, with or without possible template structures, two or
more sequences (and possible templates for these) can be input, with the output the backbone coordinates of two or more
protein chains. Thus, the network enables the direct buildingof structure models for protein-protein complexes from sequence information, short circuiting the standard procedureof building models for individual subunits and then carryingout rigid-body docking. In addition to the great reduction incompute time required (complex models are generated fromsequence information in ~30 min on a 24G TITAN RTX GPU),
this approach implements “flexible backbone” docking almost by construction as the structures of the chains are predicted in the context of each other. We tested the end-to-end
three-track network on paired sequence alignments for complexes of known structures (32) (see methods and table S4 fordetails) containing two (Fig. 4A) or three (Fig. 4B) chains, andin many cases, the resulting models were very close to theactual structures [TM-score (33) > 0.8]. Information on residue-residue co-evolution between the paired sequences likely
contributes to the accuracy of the rigid body placement as
more accurate complex structures were generated when moresequences were available (fig. S10). The network was trainedon monomeric proteins, not complexes, so there may be somewww.sciencemag.org(Page numbers not final at time of first release)4

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model databases (14, 15); fig. S7] and that the DeepAccNet
model quality predictor (12) provides a good measure of actual model accuracy (fig. S7D). We provide RoseTTAFoldmodels and accompanying accuracy predictions for closed
and open states of all human GPCRs of currently unknown
structure.
Protein structures can provide insight into how mutations
in key proteins lead to human disease. We identified humanproteins without close homologs of known structure that contain multiple disease-causing mutations or have been thesubject of intensive experimental investigation (see methods). We used RoseTTAFold to generate models for 693 domains from such proteins. Over one-third of these models
have a predicted lDDT > 0.8, which corresponded to an average Cɑ-RMSD -RMSD of 2.6 Å on CASP14 targets (fig. S8).
Here, we focus on three examples that illustrate the different
ways in which structure models can provide insight into thefunction or mechanisms of diseases.
Deficiencies in TANGO2 (transport and Golgi organization protein 2) lead to metabolic disorders, and the protein
plays an unknown role in Golgi membrane redistribution
into the ER (16, 17). The RoseTTAFold model of TANGO2
adopts an N-terminal nucleophile aminohydrolase (Ntn) fold(Fig. 3A) with well-aligned active site residues that are conserved in TANGO2 orthologs (Fig. 3B). Ntn superfamily members with structures similar to the RoseTTAFold model
suggest that TANGO2 functions as an enzyme that might hydrolyze a carbon-nitrogen bond in a membrane component(18). Based on the model, known mutations that cause disease(magenta spheres in Fig. 3A) could act by hindering catalysis(R26K, R32Q, and L50P, near active site) or produce stericclashes (G154R) (19) in the hydrophobic core. By comparison,a homology model based on very distant (<15% sequence
identity) homologs had multiple alignment shifts that misplace key conserved residues (fig. S9 and table S3)
The ADAM (A Disintegrin And Metalloprotease) and
ADAMTS families of metalloproteases are encoded by over 40
human genes, mediate cell-cell and cell-matrix interactions(20, 21) and are involved in a range of human diseases, including cancer metastasis, inflammatory disorders, neurological diseases and asthma (21, 22). The ADAMs containprodomain and metalloprotease domains; the fold of themetalloprotease is known (23, 24), but not that of the prodomain, which has no homologs of known structure. The
RoseTTAFold predicted structure of the ADAM33 prodomain
has a lipocalin-like beta-barrel fold (Fig. 3C) belonging to an
extended superfamily that includes metalloproteaseinhibitors (MPIs) (25). There is a cysteine in an extension following the predicted prodomain barrel; taken together, thesedata are consistent with experimental data suggesting that
the ADAM prodomain inhibits metalloprotease activity usinga cysteine switch (26). Conserved residues within ADAM33

Conclusions
RoseTTAFold enables solutions of challenging X-ray crystallography and cryo-EM modeling problems, provides insight
into protein function in the absence of experimentally determined structures, and rapidly generates accurate models ofprotein-protein complexes. Further training on protein-protein complex datasets will likely further improve the modeling of the structures of multiprotein assemblies. The
approach can be readily coupled with existing small moleculeand protein binder design methodology to improve computational discovery of new protein and small molecule ligands
for targets of interest. The simultaneous processing of sequence, distance, and coordinate information by the threetrack architecture opens the door to new approaches incorporating constraints and experimental information at all
three levels for problems ranging from cryo-EM structure determination to protein design.
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training set bias in the monomer structures, but there is none
for the complexes.
To illustrate the application of RoseTTAFold to complexesof unknown structure with more than three chains, we used
it to generate models of the complete four-chain human
IL-12R/IL-12 complex (Fig. 4C and fig. S11). A previously published cryo-EM map of the IL-12 receptor complex indicateda similar topology to that of the IL-23 receptor; however, theresolution was not sufficient to observe the detailed interaction between IL-12Rβ2 and IL-12p35 (34). Such an understanding is important for dissecting the specific actions of
IL-12 and IL-23 and generating inhibitors that block IL-12
without impacting IL-23 signaling. The RoseTTAFold model
fits the experimental cryo-EM density well and identified ashared interaction between Y189 in IL-12p35 and G115 in
IL-12Rβ2 analogous to the packing between W156 in IL-23p19
with G116 in IL-23R. In addition, the model suggests a role
for the IL-12Rβ2 N-terminal peptide (residue 24-31) in IL-12
binding not observed in the IL-12 cryo-electron microscopy(IL-12Rβ2 D26 may interact with nearby K190 and K194 in
IL-12p35), which may provide an avenue to target the interaction between IL-12 and IL-12Rβ2 specifically.

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ACKNOWLEDGMENTS
We thank Eric Horvitz, Naozumi Hiranuma, David Juergens, Sanaa Mansoor, andDoug Tischer for helpful discussions, David E. Kim for web-server construction,and Luki Goldschmidt for computing resource management. TPK thanks Bernd
Nidetzky and Mareike Monschein from Graz University of Technology forproviding protein samples for crystallization. DJO gratefully acknowledges
assistance with data collection from scientists of Diamond Light Source
beamline I04 under proposal mx20303. TS, CB, and TPK acknowledge the ESRF
(ID30-3, Grenoble, France) and DESY (P11, PETRAIII, Hamburg, Germany) for
provision of synchrotron-radiation facilities and support during data collection.
Funding: This work was supported by Microsoft (MB, DB, and generous gifts of
Azure compute time and expertise), Open Philanthropy (DB, GRL), Eric andWendy Schmidt by recommendation of the Schmidt Futures program (FD, HP),
The Washington Research Foundation (MB, GRL, JW), National Science
Foundation Cyberinfrastructure for Biological Research, Award # DBI 1937533(IA), Wellcome Trust, grant number 209407/Z/17/Z (RJR), National Institute of
Health, grant numbers P01GM063210 (PDA, RJR), DP5OD026389 (SO), RO1AI51321 (KCG) and GM127390 (NVG), Mathers Foundation (KCG), Canadian
Institute of Health Research (CIHR) Project Grant, grant numbers 168998 (JEB)and 168907 (CKY), the Welch Foundation I-1505 (NVG), Global Challenges
Research Fund (GCRF) through Science & Technology Facilities Council (STFC),
grant number ST/R002754/1: Synchrotron Techniques for African Research andTechnology (START) (DJO, AAvD, ACE), Austrian Science Fund (FWF) projects
P29432 and DOC50 (doc.fund Molecular Metabolism) (TS, CB, TP). Author
contributions: MB, FD, and DB designed the research; MB, FD, IA, JD, SO, JW
developed deep learning network; GRL and HP analyzed GPCR modeling results;
QC, LNK, RDS, NVG analyzed modeling results for proteins related to the humandiseases; CRG KCG analyzed modeling results for the IL-12R/IL-12 complex;
PDA, RJR, CA, FD, CM worked on structure determination; AAvD, ACE, DJO, TS,
CB, TPK, MKR, UD, CKY, JEB, AD, JHP, AVR provided experimental data; MB, FD,
GRL, QC, LNK, HP, CRG, PDA, RJR, DB wrote the manuscript; all authors
discussed the results and commented on the manuscript. Competing interests:
Authors declare that they have no competing interests. Data and materials
availability: The GPCR models of unknown structures have been deposited tohttp://files.ipd.uw.edu/pub/RoseTTAFold/all_human_GPCR_unknown_models.
tar.gz andhttp://files.ipd.uw.edu/pub/RoseTTAFold/GPCR_benchmark_one_state_unkno
wn_models.tar.gz. The model structures for structurally uncharacterized humanproteins have been deposited tohttp://files.ipd.uw.edu/pub/RoseTTAFold/human_prot.tar.gz. Coordinates forthe full PI3K complex structure determined by cryo-EM are available at theProtein Data Bank (PDB) with accession code PDB: 7MEZ. Model structures used
for Molecular Replacement are available at
http://files.ipd.uw.edu/pub/RoseTTAFold/MR_models.tar.gz. The refinedstructures for GLYAT, oxidoreductase, SLP, and Lrbp proteins will be depositedin the PDB when final processing is completed. The method is available as aserver at https://robetta.bakerlab.org (RoseTTAFold option), and the sourcecode and model parameters are available athttps://github.com/RosettaCommons/RoseTTAFold or Zenodo (36).

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SUPPLEMENTARY MATERIALS
science.sciencemag.org/cgi/content/full/science.abj8754/DC1
Materials and Methods
Figs. S1 to S17
Tables S1 to S4
References (37–82)
MDAR Reproducibility Checklist
7 June 2021; accepted 7 July 2021
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Fig. 1. Network architecture and performance. (A) RoseTTAFold architecture with 1D, 2D, and 3D
attention tracks. Multiple connections between tracks allow the network to simultaneously learn
relationships within and between sequences, distances, and coordinates (see methods and fig. S1
for details). (B) Average TM-score of prediction methods on the CASP14 targets. Zhang-server and
BAKER-ROSETTASERVER were the top 2 server groups while AlphaFold2 and BAKER were the top
2 human groups in CASP14; BAKER-ROSETTASERVER and BAKER predictions were based on
trRosetta. Predictions with the two-track model and RoseTTAFold (both end-to-end and pyRosettaversion) were completely automated. (C) Blind benchmark results on CAMEO medium and hardtargets; model accuracies are TM-score values from the CAMEO website (https://cameo3d.org/).

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Fig. 2. Enabling experimental structure determination with RoseTTAFold. (A and B) Successful
molecular replacement with RoseTTAFold models. (A) SLP. (top) C-terminal domain: comparison of
final refined structure (gray) to RoseTTAFold model (blue); there are no homologs with known
structure. (bottom) N-terminal domain: refined structure is in gray, and RoseTTAFold model iscolored by the estimated RMS error (ranging from blue for 0.67 Å to red for 2 Å or greater). 95 Cɑ
atoms of the RoseTTAFold model can be superimposed within 3 Å of Cɑ atoms in the final structure,yielding a Cɑ-RMSD of 0.98 Å. In contrast, only 54 Cɑ atoms of the closest template (4l3a, brown) canbe superimposed (with a Cɑ-RMSD of 1.69 Å). (B) Refined structure of Lrbp (gray) with the closest
RoseTTAFold model (blue) superimposed; residues having estimated RMS error greater than 1.3 Å
are omitted (full model is in fig. S5C). (C) Cryo-EM structure determination of p101 Gᵦᵧ binding
domain (GBD) in a heterodimeric PI3Kᵧ complex using RoseTTAFold. (top) RoseTTAFold models
colored in a rainbow from the N terminus (blue) to the C terminus (red) have a consistent all-beta
topology with a clear correspondence to the density map. (bottom) Comparison of the final refinedstructure to the RoseTTAFold model colored by predicted RMS error ranging from blue for 1.5 Å orless to red 3 Å or greater. The actual Cɑ-RMSD between the predicted structure and final refinedstructure is 3.0 Å over the beta-sheets. Figure prepared with ChimeraX (35).

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Fig. 3. RoseTTAFold models provide insights into function. (A) TANGO2 model, colored in a
rainbow from the N terminus (blue) to the C terminus (red), adopts an Ntn hydrolase fold. Pathogenic
mutation sites are in magenta spheres. (B) Predicted TANGO2 active site colored by ortholog
conservation in rainbow scale from variable (blue) to conserved (red) with conserved residues instick and labeled. Pathogenic mutations (spheres with wild-type side chains in the sticks) are labeledin magenta; select neighboring residues are depicted in the sticks. (C) ADAM33 prodomain adopts a
lipocalin-like barrel shown in a rainbow from N terminus (blue) to C terminus (red). (D) ADAM33
model surface rendering colored by ortholog conservation from blue (variable) to red (conserved),
highlighting a conserved surface patch. (E) CERS1 transmembrane structure prediction is coloredfrom N terminus (blue) to C terminus (red), with a pathogenic mutation in TMH2 near a central cavity
in magenta. (F) Zoom of CERS1 active site with residues colored by ortholog conservation fromvariable (blue) to conserved (red). Residues that contribute to catalysis (H182 and D213) or areconserved (W298 and D213) line the cavity. The conserved pathogenic mutation is adjacent to theactive site.

First release: 15 July 2021

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Fig. 4. Complex structure prediction using RoseTTAFold. (A and B) Prediction of structures ofE.coli protein complexes from sequence information. Experimentally determined structures are on
the left, RoseTTAFold models, on the right; the TM-scores below indicate the extent of structuralsimilarity. (A) Two chain complexes. The first subunit is colored in gray, and the second subunit iscolored in a rainbow from blue (N-terminal) to red (C-terminal). (B) Three chain complexes. Subunits
are colored in gray, cyan, and magenta. (C) IL-12R/IL-12 complex structure generated by
RoseTTAFold fits the previously published cryo-EM density (EMD-21645).

Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa
N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H.
Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller,
Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin,
Paul D. Adams, Randy J. Read and David Baker

published online July 15, 2021

http://science.sciencemag.org/content/early/2021/07/14/science.abj8754

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