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De novo protein design by deep network hallucination
Open accessPreprint — published as 10.1038/s41586-021-04184-w · Paper page
Abstract
There has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution 1–3 . We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures. We obtained synthetic genes encoding 129 of these network hallucinated sequences, expressed and purified the proteins in E coli, and found that 27 folded to monomeric stable structures with circular dichroism spectra consistent with the hallucinated structures. Thus deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute, alongside traditional physically based models, to the de novo design of proteins with new functions.
Overview
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Written from the paper’s full text.
TL;DR
The authors invert the trRosetta structure-prediction network by Monte Carlo sampling in amino acid sequence space to “hallucinate” new 100-residue proteins with sequences unrelated to native proteins. Of 129 selected designs expressed in E. coli, 27 folded to monodisperse species with CD spectra matching the hallucinated structures, and three experimentally solved structures closely matched the models.
Key findings
- Hallucinated sequences had very low similarity to native proteins, with best BLAST E-values to UniProt of ~0.1, while their predicted structures had TM-align scores of 0.6–0.9 to native PDB structures.
- Of 129 hallucinated proteins expressed and purified from E. coli, 27 yielded SEC peaks corresponding to monomeric or small oligomeric species and CD spectra consistent with the target structures; 21 were highly thermostable with apparent melting temperatures above 70 °C, and alpha-beta designs showed no unfolding transitions up to 95 °C.
- In orthogonal Rosetta folding simulations, 82 of 129 designs had lowest-energy structures close to the hallucinated structures with Cα-RMSDs <3.0 Å, and for all 129 the design model was lower in energy than any structure obtained from an extended chain.
- Experimental structures matched hallucinated models: NMR structure of design 0515 had ~1.84 Å Cα RMSD to the model; X-ray structure of 0217 at 2.9 Å resolution had 2.53 Å backbone RMSD over all 100 residues; X-ray structure of 0738_mod at 2.4 Å resolution had 3.68 Å Cα RMSD over 96 residues, with N- and C-terminal halves aligning at 1.32 Å over 57 residues and 2.17 Å over 43 residues.
Methods
- The trRosetta network predicts inter-residue distance and orientation distributions from sequence; a background network with similar architecture but no amino acid sequence identity information provided a reference distribution. The objective combined KL-divergence of trRosetta predictions from the background with negative KL-divergence of amino acid composition from PDB frequencies, optimized by simulated annealing in sequence space with 40,000 attempted moves, starting temperature 0.1 halved every 5,000 steps, cysteines excluded, and length L = 100.
- Two thousand designs were generated, clustered by TM-score into 95 clusters at a 0.75 threshold; 129 sequences from 27 structural clusters were selected for experimental testing, expressed in E. coli BL21(DE3), purified by IMAC and size-exclusion chromatography, and characterized by circular dichroism, NMR, and X-ray crystallography.
- Structural comparisons used TM-align, Theseus superposition, and Rosetta ab initio folding simulations; crystallographic data were processed with HKL2000/Scalepack and structures solved by molecular replacement.
Limitations
- The paper states that many hallucinated proteins form oligomers in solution, likely because the network incorporated sequence features from native protein-protein interfaces; surface hydrophobic substitutions were needed to produce monomeric species in some cases.
- The paper states that design 0738_mod showed multiple monomeric conformations in slow conformational exchange, and suggests that incorporating an explicit sidechain representation in the hallucination method could reduce such structural heterogeneity.
- Evident from the text: only 27 of 129 expressed designs folded to monodisperse species, only three structures were determined experimentally, one of those required surface modification, and the study did not test designed functions.
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In the paper
“The overall distributions of hallucinated sequences and structures are very different from those of naturally occurring proteins of the same (100 residue) length which were used during trRosetta training (Extended Data Fig. 1a–e).”
§ De novo protein design by deep network hallucination > Introduction
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In the paper
“for all 129 the lowest energy structure sampled starting from the design model was lower in energy than any other structure obtained starting from an extended chain.”
§ De novo protein design by deep network hallucination > Introduction
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“The hallucinated proteins are more similar to each other (blue lines) than they are to natural proteins (grey lines).”
§ De novo protein design by deep network hallucination > Extended Data
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“Hallucinations are more ideal than natural proteins in having less loops but longer secondary structure elements.”
§ De novo protein design by deep network hallucination > Extended Data
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“we found that the trRosetta deep neural network trained using multiple sequence information could consistently predict structure quite accurately for de novo designed proteins from just a single sequence -- i.e., in the complete absence of co-evolution information3.”
§ De novo protein design by deep network hallucination > Introduction
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- Not checked yetcites [3] source
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“Structural superposition of the 0738 model and 0738_mod crystal structure revealed a 3.68 Å Cɑ RMSD over 96 residues”
§ De novo protein design by deep network hallucination > Introduction
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“we found that substitution of surface hydrophobic residues with polar residues in a subset of the hallucinations that formed oligomers resulted in monomeric species by SEC”
§ De novo protein design by deep network hallucination > Introduction
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“Of these, 27 yielded size exclusion chromatography (SEC) peaks corresponding to monomeric or small oligomeric species”
§ De novo protein design by deep network hallucination > Introduction
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“The experimentally validated proteins span a wide range of topologies”
§ De novo protein design by deep network hallucination > Introduction
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“trajectories started from different random sequences resulted in very different sequences and structures (Fig 1e).”
§ De novo protein design by deep network hallucination > Introduction
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In the paper
“In all cases, the CD spectra were consistent with the target structures (Figs. 3e, 4e and Extended Data Fig. 3e,k), with the characteristic profiles of all alpha helical proteins for the all alpha helical designs (Fig. 3e and Extended Data Fig. 3e), and of alpha-beta proteins for the alpha-beta designs”
§ De novo protein design by deep network hallucination > Introduction
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“all of the sequences are predicted by Rosetta large scale energy calculations to have funneled landscapes leading into the target structure”
§ De novo protein design by deep network hallucination > Introduction
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“One of these surface-modified hallucinations, 0738_mod, yielded crystals and the 2.4 Å resolution structure was determined.”
§ De novo protein design by deep network hallucination > Introduction
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“Both de novo designed proteins and the hallucinated proteins generated here have regular alpha helices and beta sheets, and lack the long loops and other idiosyncrasies of native protein structures”
§ De novo protein design by deep network hallucination > Introduction
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“We also succeeded in determining a 2.9 Å resolution crystal structure of design 0217, which revealed a 3-helix bundle with an overall fold similar to the hallucinated model; the backbone RMSD between model and crystal structure is 2.53 Å over all 100 residues”
§ De novo protein design by deep network hallucination > Introduction
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“The average hetNOE for the homodimer 0217 is lower than for 0515 and 0738_mod, and it has fewer peaks than expected due to exchange broadening.”
§ De novo protein design by deep network hallucination > Extended Data
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“the N- and C-terminal halves of the crystal structure align remarkably well to the corresponding regions in the hallucinated model with backbone RMSDs of 1.32 Å over 57 residues, and 2.17 Å over 43 residues for the N-terminal and C-terminal half, respectively”
§ De novo protein design by deep network hallucination > Introduction
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“NMR data on design 0417 are consistent with the alpha/beta fold of the hallucinated model, and both SEC and NMR relaxation measurements (15N T1 ~730 ms, 15N T2 ~77 ms, and τc ~10.4 ns at 25 °C, Extended Data Fig. 6) indicate that it is primarily monomeric but with some transient self-association in solution.”
§ De novo protein design by deep network hallucination > Introduction
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“For example, 0217 forms a dimer in the crystal structure (Extended Data Fig. 5), consistent with SEC-MALS analysis and NMR rotational correlation time measurements”
§ De novo protein design by deep network hallucination > Introduction
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“the predicted structures resemble but are not identical to native structures in the PDB, with TM-align scores of 0.6–0.9 (Fig. 1g).”
§ De novo protein design by deep network hallucination > Introduction
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“Optimization from different random starting points results in novel proteins spanning a very wide range of sequences and predicted structures.”
§ De novo protein design by deep network hallucination > Abstract
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“the 0217 model displays clear amphipathic sequence patterning across the 3-helix topology, with numerous solvent-exposed hydrophobic residues (Suppl. Fig. 2) that mediate the dimer contacts in the crystal structure.”
§ De novo protein design by deep network hallucination > Introduction
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“The solution data for designs 0515 and 0217 (dimer) suggest well-ordered structures in solution, with internal dynamics typical of small natural proteins.”
§ De novo protein design by deep network hallucination > Introduction
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“A single solution was found in I 2 3 from one of the ab initio models with two copies in the asymmetric unit and a TFZ score of 13.3 (no other model yielded a TFZ score >8).”
§ De novo protein design by deep network hallucination > Methods > Crystallography sample preparation, data collection, and analysis.
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“SEC data demonstrating monodispersity of these proteins in solution, with predominantly monomer for 0515 and 0738_mod and predominantly dimer for 0217.”
§ De novo protein design by deep network hallucination > Extended Data
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In the paper
“The accuracy does not reflect PDB memorization; the closest BLAST hits in the PDB for the N and C terminal halves have E-values of 0.29 and 0.63, respectively.”
§ De novo protein design by deep network hallucination > Introduction
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In the paper
“Two thousand proteins were generated using the hallucination procedure described above, and structurally compared to each other using the template modeling score (TM-score)29. Average-linkage hierarchical clustering yielded 95 clusters with an average inter-cluster similarity of TM-score = 0.75.”
§ De novo protein design by deep network hallucination > Methods > Design selection.
What the sources say
- Not checked yetcites [29] source
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In the paper
“One-hundred twenty-nine hallucinated sequences from the remaining 27 structural clusters (no more than 10 designs per cluster) were selected for experimental testing.”
§ De novo protein design by deep network hallucination > Methods > Design selection.
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In the paper
“The structures span all alpha, all beta and mixed alpha-beta fold classes, with 95 different sub-folds at a TM-score clustering threshold of 0.75.”
§ De novo protein design by deep network hallucination > Introduction
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“We determined the solution NMR structure for design 0515 to be a monomeric antiparallel four-helix bundle (1D estimated 15N T1 ~ 780 ms, 15N T2 ~ 77 ms, and τc ~ 9.6 ns at 25 °C); the ensemble of 20 structures had a Cɑ root mean square deviation (RMSD) to the hallucinated model of ~1.84 Å”
§ De novo protein design by deep network hallucination > Introduction
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“While both sets of predictions yielded converged ensembles on a single topology, the classical ab initio models had significant diversity within that ensemble.”
§ De novo protein design by deep network hallucination > Methods > Crystallography sample preparation, data collection, and analysis.
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In the paper
“Although 0738_mod has a similar hetNOE distribution as monomeric 0515, it has more than double the expected number of peaks, indicating at least two folded conformations (for all or parts of the protein) in solution that are in slow conformational exchange on the NMR time-scale.”
§ De novo protein design by deep network hallucination > Extended Data
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In the paper
“SDS-PAGE data (not shown) show that each is >95% homogeneous, which together with MALDI-TOF mass spectrometry indicate that the spectral heterogeneity observed is not due to chemical heterogeneity.”
§ De novo protein design by deep network hallucination > Extended Data
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In the paper
“Random sequences give diffuse predictions, while existing de novo designs produce peaked distributions with low variance3.”
§ De novo protein design by deep network hallucination > Methods > Networks and objective function.
What the sources say
- Not checked yetcites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“This is a completely orthogonal test as the network was trained exclusively on native protein structures, and has no access to the Rosetta energy function.”
§ De novo protein design by deep network hallucination > Introduction
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In the paper
“For 0738_mod refinement, final Ramachandran favored and outliers were 96% and 0%, respectively.”
§ De novo protein design by deep network hallucination > Methods > Crystallography sample preparation, data collection, and analysis.
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In the paper
“For 0217 final Ramachandran favored and outliers were 99% and 0%, respectively.”
§ De novo protein design by deep network hallucination > Methods > Crystallography sample preparation, data collection, and analysis.
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In the paper
“Many of the hallucinated proteins are monomeric, stable, have the expected secondary structure, and are strongly predicted to fold to the target structure by Rosetta in completely orthogonal calculations”
§ De novo protein design by deep network hallucination > Conclusion
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In the paper
“The network appears to incorporate sequence features associated with the protein-protein interfaces of the native oligomeric proteins included in the PDB training set, likely explaining why many of the network hallucinated proteins self associate.”
§ De novo protein design by deep network hallucination > Introduction
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In the paper
“Even under optimized conditions, there is still evidence of exchange broadening (e.g. Trp side chain NεHs are weak), resonances that appear only at high temperature and high glycerol concentrations, and some resonances that are doubled; all indications of transient self-association.”
§ De novo protein design by deep network hallucination > Extended Data
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In the paper
“This sample was >98% homogeneous by SDS-PAGE analysis, and >95% isotope enriched based on MALDI-TOF mass spectrometry.”
§ De novo protein design by deep network hallucination > Methods > NMR sample preparation.
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In the paper
“in benchmark tests this network outperformed other methods3.”
§ De novo protein design by deep network hallucination > Introduction
What the sources say
- Not checked yetcites [3] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“The trRosetta protein structure prediction network, described in detail elsewhere3, is a 2D residual-convolutional neural network which takes 1- and 2-site features derived from a multiple sequence alignment or a single sequence as an input and produces a 2D output describing distances and orientations for all residue pairs in a protein in a probabilistic manner”
§ De novo protein design by deep network hallucination > Methods > Networks and objective function.
What the sources say
- Not checked yetcites [3] source
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In the paper
“This was further validated by the appearance of new peaks in spectra at lower temperature (288K), and different peaks at higher temperatures (308 and 318K), and confirmed by detection of 15N ZZ-exchange cross peaks at 318K with 600 and 750 ms mixing times (Bruker pulse sequence hsqcetexf3gp, data not shown)58.”
§ De novo protein design by deep network hallucination > Extended Data
What the sources say
- Not checked yetcites [58]
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In the paper
“These samples were each >~98% homogeneous, based on SDS-PAGE.”
§ De novo protein design by deep network hallucination > Methods > NMR sample preparation.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Random amino acid sequences have a vanishingly small probability of folding to a defined structure.
“As expected for random sequences, which have a vanishingly small probability of folding to a defined structure”
The authors state this as an assumption.
The background network's predictions can be viewed as representing a generic molten globule state.
“this can loosely be viewed as representing a generic “molten globule” state”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The bi-annual Critical Assessment of Structure (CASP) meetings have demonstrated that deep learning methods such as AlphaFold (1, 2) and trRosetta (3), that extract information from the large database of known protein structures in the PDB, outperform more traditional approaches that explicitly model the folding process.”
From Accurate prediction of protein structures and interactions using a 3-track network (2021) · cites this paper as [2]
Experimental — accuracy not yet measuredIn the citing paper
“crystal and NMR structures confirm that the hallucinated sequences fold to the hallucinated structures (12).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [12]
In the citing paper
“We previously demonstrated that the trRosetta structure-prediction neural network (11) can be used to generate new proteins by maximizing the trRosetta output probability that a sequence folds to some (unspecified) three dimensional structure during Monte Carlo sampling in sequence space (12).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [12]
In the citing paper
“There has also been recent success in applying the hallucination technique to general protein design [72].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [72]
In the citing paper
“The competing RoseTTAFold approach has allowed for de novo design through network hallucination by training a similar model specially for inpainting portions of proteins given context [72,83].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
The reported resolution for 0217 differs from its table entry
MinorThe text reports a 2.9 Å crystal structure for 0217, but the crystallographic table gives its data-collection resolution as 3.0 Å.
“We also succeeded in determining a 2.9 Å resolution crystal structure of design 0217”
“| Resolution (Å) | 47.8–3.0 (3.1–3.0)b | 50.0–2.4 (2.49–2.40) |”
Citations
Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Highly accurate protein structure prediction with AlphaFold
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Protein Oligomer Engineering: A New Frontier for Studying Protein Structure, Function, and Toxicity
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Module-Based Polyketide Synthase Engineering for de Novo Polyketide Biosynthesis
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Rationally seeded computational protein design of ɑ-helical barrels
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Self-citationBeyond Biosecurity by Taxonomic Lists: Lessons, Challenges, and Opportunities
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From peptides to proteins: coiled-coil tetramers to single-chain 4-helix bundles
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Determinants for an Efficient Enzymatic Catalysis in Poly(Ethylene Terephthalate) Degradation
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Machine Learning for Protein Engineering
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Enzymatic synthesis of chiral alcohols using ketoreductases
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Leveraging large language models for peptide antibiotic design
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The evolution of language models: From N-Grams to LLMs, and beyond
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Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing
Shi, Z., Liu, P., Liao, X., Mao, Z., Zhang, J., Wang, Q., Sun, J., Ma, H., & Ma, Y. (2022) · BioDesign Research · doi:10.34133/2022/9898461 · Cited by 22
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Wang, X., Xu, K., Tan, Y., Liu, S., & Zhou, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043827 · Cited by 22
Small molecule allosteric modulation of the adenosine A1 receptor
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Accelerating the design of pili-enabled living materials using an integrative technological workflow
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Extracellular vesicles for developing targeted hearing loss therapy
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An integrative approach to protein sequence design through multiobjective optimization
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Leveraging ancestral sequence reconstruction for protein representation learning
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Sampling of structure and sequence space of small protein folds
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Self-citationProtein engineering of amine transaminases
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When synthetic biology meets medicine
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Parametrically guided design of beta barrels and transmembrane nanopores using deep learning
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Self-citationRecent advances in generative biology for biotherapeutic discovery
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Accelerating protein design by scaling experimental characterization
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Self-citationThe Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe
Zhang, G., Liu, C., Lu, J., Zhang, S., & Zhu, L. (2025) · Biology · doi:10.3390/biology14091268 · Cited by 22
Quantifying uncertainty in protein representations across models and tasks
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Hallucinating structure-conditioned antibody libraries for target-specific binders
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Unlocking the Gates: Therapeutic Agents for Noninvasive Drug Delivery Across the Blood-Brain Barrier
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Principles and Methods in Computational Membrane Protein Design
Vorobieva, A. A. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167154 · Cited by 20
What Have We Learned from Design of Function in Large Proteins?
Khersonsky, O., & Fleishman, S. J. (2022) · BioDesign Research · doi:10.34133/2022/9787581 · Cited by 20
Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis
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De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution
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Designing Multivalent and Multispecific Biologics
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AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design
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A Deep SE(3)-Equivariant Model for Learning Inverse Protein FoldingPreprint
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Design of Peptide-Based Protein Degraders via Contrastive Deep LearningPreprint
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Training bias and sequence alignments shape protein–peptide docking by AlphaFold and related methods
Guan, L., & Keating, A. E. (2025) · Protein Science · doi:10.1002/pro.70331 · Cited by 18
Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics
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Glycoside hydrolases in the biodegradation of lignocellulosic biomass
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AI in cellular engineering and reprogramming
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Advancing sustainable biotechnology through protein engineering
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Cutting-edge computational approaches in enzyme design and activity enhancement
Sun, R., Wu, D., Chen, P., & Zheng, P. (2024) · Biochemical Engineering Journal · doi:10.1016/j.bej.2024.109510 · Cited by 20
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Physics-informed multi-fidelity learning-driven imaging method for electrical capacitance tomography
Lei, J., Liu, Q., & Wang, X. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105467 · Cited by 16
Rational design of small‐molecule responsive protein switches
Shui, S., Buckley, S., Scheller, L., & Correia, B. E. (2023) · Protein Science · doi:10.1002/pro.4774 · Cited by 17
De novo protein fold design through sequence-independent fragment assembly simulations
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Proteus: Exploring Protein Structure Generation for Enhanced Designability and EfficiencyPreprint
Wang, C., Qu, Y., Peng, Z., Wang, Y., Zhu, H., Chen, D., & Cao, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.10.579791 · Cited by 17
Protein Hunter: exploiting structure hallucination within diffusion for protein designPreprint
Cho, Y., Rangel, G., Bhardwaj, G., & Ovchinnikov, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.681530 · Cited by 16
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Proteoformics: Current status and future perspectives
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Hallucination of closed repeat proteins containing central pockets
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Machine learning for predicting protein properties: A comprehensive review
Wang, Y., Zhang, Y., Zhan, X., He, Y., Yang, Y., Cheng, L., & Alghazzawi, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128103 · Cited by 15
Design of light- and chemically responsive protein assemblies through host-guest interactions
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Toward deep learning sequence–structure co-generation for protein design
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Accurate Protein-Ligand Complex Structure Prediction using Geometric Deep LearningPreprint
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Applying artificial intelligence to accelerate and de-risk antibody discovery
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NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores
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Recent advances in the improvement of cyanobacterial enzymes for bioalkane production
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Illuminating enzyme design using deep learning
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PXDesign: Fast, Modular, and Accurate De Novo Design of Protein BindersPreprint
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Self-citationEnzyme structure correlates with variant effect predictability
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Artificial intelligence in de novo protein design
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Tertiary motifs as building blocks for the design of protein‐binding peptides
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How Deep Learning Tools Can Help Protein Engineers Find Good Sequences
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P( all-atom ) Is Unlocking New Path For Protein DesignPreprint
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Exploring “dark-matter” protein folds using deep learning
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De novo designed ice-binding proteins from twist-constrained helices
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Transfer learning for cross-context prediction of protein expression from 5’UTR sequence
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Machine learning in biological physics: From biomolecular prediction to design
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Metadata
- Authors
- Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2020-07-23
- Volume
- 600
- Issue
- 7889
- Pages
- 547-552
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC9293396
- OpenAlex
- W3216341763
- Cited by
- 629
- References
- 61
- Citations by year
- 2026: 102 · 2025: 120 · 2024: 139 · 2023: 122 · 2022: 110 · 2021: 29 · 2020: 6
- FWCI
- 31.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Novo Nordisk Fonden (NNF18OC0030446)
- National Institutes of Health (1s10od021527-01, DP5OD026389)
- National Institute of General Medical Sciences (R01GM120574, R35GM141818, P30GM124165)
- 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 › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- de novo protein design, deep neural network, hallucinations, protein structure prediction, Kullback-Leibler divergence, synthetic proteins, protein folding, circular dichroism, X-ray crystallography, nuclear magnetic resonance
- MeSH terms
- Amino Acid Sequence, Hallucinations, Humans, Protein Conformation, Proteins, Neural Networks, Computer, Crystallography, X-Ray
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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