Class I: Protein Sequence design ("Fixed-backbone")

November 5, 2022 · View on GitHub

💡 Notes

  • This is a list accompanying our preprint: https://www.biorxiv.org/content/10.1101/2022.08.31.505981v1 . We focus on deep learning methods for protein design released after 2018 (and mostly 2019). This table complements Table 1 in our manuscript.

  • We curated this list manually and as such it might be incomplete. Please drop us an email or open an issue if you find we didn't describe your method correctly or it's missing.

  • We order the methods by release date (preprint when available) and categorize them in four classes (for more details on these categories see our preprint, Figure 1 and text):

    • 1️⃣: 'Fixed-backbone' protein design; p(sequence|structure)
    • 2️⃣: Structure generation; p(structure)
    • 3️⃣: Sequence generation; p(sequence) or p(sequence|sequence*)
    • 4️⃣: Concomitant protein and sequence design. p(sequence and structure) (which can be constrained).
  • Others before us have also done a fantastic work assembling deep learning methods for other protein-related problems, sometimes overlapping with this list. We link these lists here:

  • 💥 This work was recently highlighted in Nature

Contributors

Class I: Protein Sequence design ("Fixed-backbone")

Methods in this class attempt to solve the classical protein design problem: Find an optimal sequence that adopts a pre-determined 3D structure.

NameArchitectureNumber of ParametersUser InputOutputTraining DatasetPaperCodeRelease Month/Year
SPIN2FNN~105k3D structuresequence1,532 X-ray structuresPaperCode used to be here - no longer available2018/02
SPROFCNN-LSTM-3D structuresequence1,532 X-ray structuresPaperCode Web Server2019/08
Ingraham et al.modified Transformer>3k sequence CATH 4.2 40% sequences/structures PaperCode2019/12
ProDCoNNCNN>28k3D structure sequence Two datasets: ID90TR: 17,044; ID30TR: 9,135 sequences/PDB pairs PaperReimplementation2019/12
Anand et al.CNN-3D structure Amino acid and side chain conformation53,414 CATH domain structures  PaperCode2020/01
DenseCPDCNN3M3D structure sequence11,227 X-ray structures   PaperWeb server Reimplementation2020/01
ProteinSolverGNN-3D structure sequence 72,464,122 sequences/adjacency matrices pairs PaperCode2020/03
Norn et al.CNNN/Adistances, angles, and dihedrals for every pair of residues (trRosetta) sequence N/A PaperCode2020/07
GVP-GNNGVP-3D structuresequence CATH 4.2 40% sequences/structures  PaperCode2020/09
Fold2Seqmodified Transformer-3D structure  sequence45,995 3D structures from CATH 4.2 filtered @ 100% PaperCode2021/06
CNN_protein_landscapeCNN>10M3D structure  sequence16,569 PDB chains PaperCode2021/08
Orellana et al.GCN-3D structures sequence CATH 4.2 40% sequences/structures Paper-2021/11
ABACUS-RTransformer152M3D structures sequence CATH 4.2 PaperCode2022/02
ESM-IF1GVP-Transformer142M3D structure sequence16k X-ray structures + 1.2M AF2 predictions PaperCode2022/04
TERMinatorGNN-3D structures sequencesCATH 4.2 40% sequences/structures  Paper-2022/04
McPartlon et al.modified Transformer-3D structures sequences37k X-ray structures from BC40  Paper-2022/04
MIFStructured GNN6.8M3D structure sequence  PaperCode2022/05
ProteinMPNNMPNN1.8M3D structure sequence CATH 4.2 40% sequences/structures PaperCode Web Interface2022/06
ProDESIGN-LETransformer + FNN-3D structure sequence 5,867,488 residues from PDB40 Paper-2022/07
TIMEDCNN3M3D structure sequence 32k structures from the PISCES server PaperCode2022/08
PiFoldGNN-3D structure sequence - Paper2022/09

Class II: Structure generation

Methods in this class generate structures unconditionally or from a set of secondary structural conditions.

NameArchitectureNumber of ParametersUser InputOutputTraining DatasetPaperCodeRelease Month/Year
64GANGAN--contact map (3D structure via ADMM)427,659 contact mapsPaper-2018/12
Anand et al.GAN--distance map (3D structure via CNN)800,000 distance mapsPaper2019/03
RamaNetLSTM>2k-A sequence of φ and ψ angles607 helical structuresPaperCode19/06
DECO-VAEVAE-Structures represented as graphscontact graph (translatable to contact map)>650,000 contact graphsPaperUpon request2020/04
SCUBANC-NN~20ksecondary structure motifbackbone12,465 structuresPaperCode2022/02
Ig-VAEVAE--protein backbone coordinates10,768 individual immunoglobulin domainsPaperCode2022/02
GENESISVAE-secondary structure motifcontact map40,726 backbones with remodeled loopsPaper-2022/03
ProtDiff & SMCDiffEGNN-Optional: structural motifcoordinates4,269 PDB structuresPaper-2022/06
Lai et al.VAE-topologyprotein backbone coordinatesCATH 4.2 40% sequences/structuresPaper-2022/07
ProteinSGMSDE + RefineNet-optional: masked matricesmatrices describing distance and torsional angles10,361 CATH 4.3 95% structuresPaper-2022/07
FoldingDiffTransformer--internal anglesCATH 4.2 40% structuresPaperCode2022/09

Class III: Sequence generation

Methods in this class generate sequences usually from autoregressive language models, and can sometimes be conditioned.

NameArchitectureNumber of ParametersUser InputOutputTraining DatasetPaperCodeRelease Month/Year
ProteinGANGAN60Msequence16,706 MDH sequencesPaperCode2019/10
ProGenTransformer1.2BOptional: sequence or functionsequence280M sequencesPaper2020/03
ProtXLnetTransformer409MOptional: sequencesequenceUniRef100PaperCode2020/07
ProtXLTransformer562MOptional: sequencesequenceBFD100Paper2020/07
ProtElectra-GeneratorTransformer420MOptional: sequencesequenceUniref100PaperCode2020/07
ProtT5Transformer11BOptional: sequencesequenceBFD100PaperCode2020/07
EVEVAEMSASequence3,219 MSAs extracted from UniRef100PaperCode2020/12
DARK3Transformer110MOptional: sequencesequence615,000 synthetic sequencesPaper-2022/01
ReLSOModified transformer110Msequencesequence and predicted value for labeldirected evolution datasetsPaperCode2022/02
ProtGPT2Transformer739MOptional: sequencesequenceUniRef50PaperCode2022/03
RITATransformer1.2BOptional: sequencesequenceUniRef100PaperCode2022/05
TranceptionTransformer700MOptional: sequencesequenceUniRef100PaperCode2022/05
ProGEN2Transformer6.4BOptional: sequencesequenceUniref90+BF30PaperCode2022/06

Class IV: Sequence and structure design

Methods in this class generate sequences and structures concomitantly, and include hallucination methods and constrained generation (inpainting)

NameArchitectureNumber of ParametersUser InputOutputTraining DatasetPaperCodeRelease Month/Year
HallucinationCNN (trRosetta)N/Arandom sequencesequence/structureN/APaperCode2020/07
Constrained hallucinationCNN (trRosetta)N/Asequence/structuresequence/structureN/APaperCode2020/11
Constrained hallucination2CNN (RoseTTAFold)N/Asequence/structuresequence/structureN/APaperCode2021/11
RFjointCNN (RoseTTAFold, finetuned)N/Asequence/structuresequence/structureFinetuned with 25% PDB version 02/2020 + 75 % AF2 structuresPaperCode2021/11
Protein DiffusionDiffussion model-Secondary structure motif sketchessequence/structure53,414 3D structures (95% CATH 4.2 S95)PaperCode2022/05
RoneyAlphaFold2N/Arandom sequencesequence/structureN/APaperCode2022/06