Accurate prediction of protein structures and interactions using a 3-track neural network
Explore this paper's citation graph
Summary
A 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.
- Type
- article
- Published
- 2021-07-15
- Cited by
- 3,464
- References
- 90
- Access
- Open access
- OpenAlex
- https://openalex.org/W3186179742
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:236141270
Keywords
CASP, Protein structure prediction, Artificial neural network, Computer science, Folding (DSP implementation)
References
- Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement
- A Disintegrin and Metalloproteinase (ADAM) and ADAM with Thrombospondin Motifs (ADAMTS) Family in Vascular Biology and Disease
- Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard
- Relaxation of backbone bond geometry improves protein energy landscape modeling
- ECOD: An Evolutionary Classification of Protein Domains
- ADAM, a novel family of membrane proteins containing A Disintegrin And Metalloprotease domain: multipotential functions in cell-cell and cell- matrix interactions
- Mammalian Ceramide Synthases
- Crystal structures of VAP1 reveal ADAMs' MDC domain architecture and its unique C‐shaped scaffold
- TRAM, LAG1 and CLN8: members of a novel family of lipid-sensing domains?
- The lipocalin protein family: structural and sequence overview.
- AL2CO: calculation of positional conservation in a protein sequence alignment
- Necessary Role for the Lag1p Motif in (Dihydro)ceramide Synthase Activity*
- Hidden Markov model speed heuristic and iterative HMM search procedure
- I-TASSER server: new development for protein structure and function predictions
- Improvement of molecular-replacement models with Sculptor
- Crystal structure of the catalytic domain of human ADAM33.
- TANGOing along the protein secretion pathway
- Clustal W and Clustal X version 2.0
- lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
- PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta
Cited by
- Orphan gene in Littorina: an unexpected role of symbionts in the host evolution.
- The Potential of Purinergic Signaling to Thwart Viruses Including SARS-CoV-2
- SARS-CoV-2 3CLpro mutations selected in a VSV-based system confer resistance to nirmatrelvir, ensitrelvir, and GC376
- De novo protein design by deep network hallucination
- Investigating the determinants of performance in machine learning for protein fitness prediction
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins
- Mimetic Neural Networks: A Unified Framework for Protein Design and Folding
- A memetic algorithm enables efficient local and global all-atom protein-protein docking with backbone and sidechain flexibility
- Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
- Limits and potential of combined folding and docking
- Assessing the utility of CASP14 models for molecular replacement
- Interface Refinement of Low-to-Medium Resolution Cryo-EM Complexes using HADDOCK2.4
- DeepMind's AI for protein structure is coming to the masses.
- Evolution of a chordate-specific mechanism for myoblast fusion
- ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation
- Geometric deep learning on molecular representations
- Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments
- AlphaFold2 and the future of structural biology
- NanoNet: Rapid end-to-end nanobody modeling by deep learning at sub angstrom resolution
- Protein pKa Prediction with Machine Learning
Related papers
- A Review on Protein Structure Prediction
- Protein structure prediction
- Protein structure prediction
- General overview on structure prediction of twilight-zone proteins
- Progress in protein structure prediction
- The prospects and opportunities of protein structure prediction with AI
- Deep Learning-Based Advances in Protein Structure Prediction
- Toward the solution of the protein structure prediction problem
- Consensus Prediction of Protein Secondary Structures