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Tapobrata Lahiri, Rajkrishna Mondal
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Deep learning methods for protein function prediction
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Structure prediction of alternative protein conformations
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Deep learning methods for protein structure prediction
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10.1038/s42256-019-0075-7 · 2019
Identification of direct residue contacts in protein–protein interaction by message passing
10.1073/pnas.0805923106 · 2009
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
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High-resolution de novo structure prediction from primary sequence
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Scoring function for automated assessment of protein structure template quality
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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 · doi-reference
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
10.1016/j.str.2022.05.001 · doi-reference
Identification of direct residue contacts in protein–protein interaction by message passing
10.1073/pnas.0805923106 · doi-reference
Improved fragment sampling for ab initio protein structure prediction using deep neural networks
10.1038/s42256-019-0075-7 · doi-reference
SPOT-contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
10.1093/bioinformatics/btac053 · doi-reference
State-of-the-art estimation of protein model accuracy using AlphaFold
10.1103/physrevlett.129.238101 · doi-reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
10.1073/pnas.2016239118 · doi-reference
Deep learning methods for protein structure prediction
10.1002/mef2.96 · doi-reference
High-accuracy protein structure prediction in CASP14
10.1002/prot.26171 · doi-reference
TEMPROT: protein function annotation using transformers embeddings and homology search
10.1186/s12859-023-05375-0 · doi-reference
ColabFold: making protein folding accessible to all
10.1038/s41592-022-01488-1 · doi-reference
lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
10.1093/bioinformatics/btt473 · doi-reference
Improved protein structure prediction using predicted interresidue orientations
10.1073/pnas.1914677117 · doi-reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · doi-reference
Are there pathways for protein folding?
10.1051/jcp/1968650044 · doi-reference
DeepGOPlus: improved protein function prediction from sequence
10.1093/bioinformatics/btz595 · doi-reference
A discussion of the solution for the best rotation to relate two sets of vectors
10.1107/s0567739478001680 · doi-reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · doi-reference
CopulaNet: learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
10.1038/s41467-021-22869-8 · doi-reference
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
10.1093/bioinformatics/btu791 · doi-reference
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
10.1093/bioinformatics/bty341 · doi-reference
Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information
10.1093/bioinformatics/btad637 · doi-reference
Deep learning for protein structure prediction and design—progress and applications
10.1038/s44320-024-00016-x · doi-reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · doi-reference
Structure-based protein function prediction using graph convolutional networks
10.1038/s41467-021-23303-9 · doi-reference
Protein design with deep learning
10.3390/ijms222111741 · doi-reference
Single-sequence protein structure prediction using a language model and deep learning
10.1038/s41587-022-01432-w · doi-reference
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding
10.1093/bioinformatics/btab198 · doi-reference
ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network
10.3390/molecules22101732 · doi-reference
Structure prediction of protein-ligand complexes from sequence information with Umol
10.1038/s41467-024-48837-6 · doi-reference
Structure prediction of alternative protein conformations
10.1038/s41467-024-51507-2 · doi-reference
Deep learning methods for protein function prediction
10.1002/pmic.202300471 · doi-reference
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
10.1093/bioinformatics/btad208 · doi-reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · doi-reference
Unified rational protein engineering with sequence-based deep representation learning
10.1038/s41592-019-0598-1 · doi-reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · doi-reference