Research graph
References from Deep learning for protein folding. Local targets link to admitted publications; unresolved targets remain external evidence.
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · 2024 · External reference
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
2022 · External reference
Unified rational protein engineering with sequence-based deep representation learning
10.1038/s41592-019-0598-1 · 2019 · External reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · 2021 · External reference
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
10.1093/bioinformatics/btad208 · 2023 · External reference
Deep learning methods for protein function prediction
10.1002/pmic.202300471 · 2025 · External reference
Structure prediction of alternative protein conformations
10.1038/s41467-024-51507-2 · 2024 · External reference
Structure prediction of protein-ligand complexes from sequence information with Umol
10.1038/s41467-024-48837-6 · 2024 · External reference
ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network
10.3390/molecules22101732 · 2017 · External reference
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding
10.1093/bioinformatics/btab198 · 2021 · External reference
Single-sequence protein structure prediction using a language model and deep learning
10.1038/s41587-022-01432-w · 2022 · External reference
Protein design with deep learning
10.3390/ijms222111741 · 2021 · External reference
ProtTrans: towards cracking the language of life’s code through self-supervised deep learning and high performance computing
2020 · External reference
Protein complex prediction with AlphaFold-multimer
2022 · External reference
Structure-based protein function prediction using graph convolutional networks
10.1038/s41467-021-23303-9 · 2021 · External reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · 2019 · External reference
Deep learning for protein structure prediction and design—progress and applications
10.1038/s44320-024-00016-x · 2024 · External reference
Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information
10.1093/bioinformatics/btad637 · 2023 · External reference
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
10.1093/bioinformatics/bty341 · 2018 · External reference
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
10.1093/bioinformatics/btu791 · 2015 · External reference
CopulaNet: learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
10.1038/s41467-021-22869-8 · 2021 · External reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · 2021 · External reference
A discussion of the solution for the best rotation to relate two sets of vectors
10.1107/s0567739478001680 · 1978 · External reference
DeepGOPlus: improved protein function prediction from sequence
10.1093/bioinformatics/btz595 · 2020 · External reference
Are there pathways for protein folding?
10.1051/jcp/1968650044 · 1968 · External reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · 2023 · External reference
lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
10.1093/bioinformatics/btt473 · 2013 · External reference
ColabFold: making protein folding accessible to all
10.1038/s41592-022-01488-1 · 2022 · External reference
TEMPROT: protein function annotation using transformers embeddings and homology search
10.1186/s12859-023-05375-0 · 2023 · External reference
High-accuracy protein structure prediction in CASP14
10.1002/prot.26171 · 2021 · External reference
Deep learning methods for protein structure prediction
10.1002/mef2.96 · 2024 · External reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · 2021 · External reference
State-of-the-art estimation of protein model accuracy using AlphaFold
10.1103/physrevlett.129.238101 · 2022 · External reference
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
2022 · External reference
SPOT-contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
10.1093/bioinformatics/btac053 · 2022 · External reference
Improved fragment sampling for ab initio protein structure prediction using deep neural networks
10.1038/s42256-019-0075-7 · 2019 · External reference
Identification of direct residue contacts in protein–protein interaction by message passing
10.1073/pnas.0805923106 · 2009 · External reference
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
10.1016/j.str.2022.05.001 · 2022 · External reference
High-resolution de novo structure prediction from primary sequence
2022 · External reference
Improved protein structure prediction using predicted interresidue orientations
10.1073/pnas.1914677117 · 2020 · External reference
Krzysztof fidelis, processing and analysis of CASP3 protein structure predictions
10.1002/(sici)1097-0134(1999)37:3+<22::aid-prot5>3.0.co;2-w · 1999 · External reference
Scoring function for automated assessment of protein structure template quality
10.1002/prot.20264 · 2004 · External reference
Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction
10.1371/journal.pcbi.1010793 · 2022 · External reference
Krzysztof fidelis, processing and analysis of CASP3 protein structure predictions
10.1002/(sici)1097-0134(1999)37:3+<22::aid-prot5>3.0.co;2-w · ExternalCitation · doi-reference
Deep learning methods for protein structure prediction
10.1002/mef2.96 · ExternalCitation · doi-reference
Deep learning methods for protein function prediction
10.1002/pmic.202300471 · ExternalCitation · doi-reference
Scoring function for automated assessment of protein structure template quality
10.1002/prot.20264 · ExternalCitation · doi-reference
High-accuracy protein structure prediction in CASP14
10.1002/prot.26171 · ExternalCitation · doi-reference
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
10.1016/j.str.2022.05.001 · ExternalCitation · doi-reference
CopulaNet: learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
10.1038/s41467-021-22869-8 · ExternalCitation · doi-reference
Structure-based protein function prediction using graph convolutional networks
10.1038/s41467-021-23303-9 · ExternalCitation · doi-reference
Structure prediction of protein-ligand complexes from sequence information with Umol
10.1038/s41467-024-48837-6 · ExternalCitation · doi-reference
Structure prediction of alternative protein conformations
10.1038/s41467-024-51507-2 · ExternalCitation · doi-reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · ExternalCitation · doi-reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · ExternalCitation · doi-reference
Single-sequence protein structure prediction using a language model and deep learning
10.1038/s41587-022-01432-w · ExternalCitation · doi-reference
Unified rational protein engineering with sequence-based deep representation learning
10.1038/s41592-019-0598-1 · ExternalCitation · doi-reference
ColabFold: making protein folding accessible to all
10.1038/s41592-022-01488-1 · ExternalCitation · doi-reference
Improved fragment sampling for ab initio protein structure prediction using deep neural networks
10.1038/s42256-019-0075-7 · ExternalCitation · doi-reference
Deep learning for protein structure prediction and design—progress and applications
10.1038/s44320-024-00016-x · ExternalCitation · doi-reference
Are there pathways for protein folding?
10.1051/jcp/1968650044 · ExternalCitation · doi-reference
Identification of direct residue contacts in protein–protein interaction by message passing
10.1073/pnas.0805923106 · ExternalCitation · doi-reference
Improved protein structure prediction using predicted interresidue orientations
10.1073/pnas.1914677117 · ExternalCitation · doi-reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · ExternalCitation · doi-reference
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding
10.1093/bioinformatics/btab198 · ExternalCitation · doi-reference
SPOT-contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
10.1093/bioinformatics/btac053 · ExternalCitation · doi-reference
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
10.1093/bioinformatics/btad208 · ExternalCitation · doi-reference
Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information
10.1093/bioinformatics/btad637 · ExternalCitation · doi-reference
lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
10.1093/bioinformatics/btt473 · ExternalCitation · doi-reference
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
10.1093/bioinformatics/btu791 · ExternalCitation · doi-reference
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
10.1093/bioinformatics/bty341 · ExternalCitation · doi-reference
DeepGOPlus: improved protein function prediction from sequence
10.1093/bioinformatics/btz595 · ExternalCitation · doi-reference
State-of-the-art estimation of protein model accuracy using AlphaFold
10.1103/physrevlett.129.238101 · ExternalCitation · doi-reference
A discussion of the solution for the best rotation to relate two sets of vectors
10.1107/s0567739478001680 · ExternalCitation · doi-reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · ExternalCitation · doi-reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · ExternalCitation · doi-reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · ExternalCitation · doi-reference
TEMPROT: protein function annotation using transformers embeddings and homology search
10.1186/s12859-023-05375-0 · ExternalCitation · doi-reference
Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction
10.1371/journal.pcbi.1010793 · ExternalCitation · doi-reference
Protein design with deep learning
10.3390/ijms222111741 · ExternalCitation · doi-reference
ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network
10.3390/molecules22101732 · ExternalCitation · doi-reference
