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Status: Unchecked Keyword: protein mutants Clear all
4 claims from 2 papers
Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics
Unified rational protein engineering with sequence-based deep representation learning
Alley, Khimulya, Biswas, AlQuraishi and Church · Nature Methods · 2019
The authors trained deep learning on unlabelled amino-acid sequences to make a protein representation, UniRep, and report it predicts stability and function competitively and improves efficiency in a protein engineering task.
Unchecked3 claimsShow 3 claims
- UncheckedSimple models built on UniRep, a learned summary of protein sequences, are said to work across many tasks and on unseen regions of sequence space.“We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space.”
- UncheckedA model trained on unlabelled protein sequences predicts protein stability and mutant function about as well as leading existing methods.“Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods.”
- UncheckedThe paper states that UniRep, a learned summary of protein sequences, cut the effort needed in one protein engineering task by about a hundredfold.“UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task.”
Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics
Probing Functional Allosteric States and Conformational Ensembles of the Allosteric Protein Kinase States and Mutants: Atomistic Modeling and Comparative Analysis of AlphaFold2, OmegaFold, and AlphaFlow Approaches and Adaptations
Raisinghani, Alshahrani, Gupta et al. · The Journal of Physical Chemistry B · 2024
Unchecked1 claim
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