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Unified rational protein engineering with sequence-based deep representation learning
TLDR
We apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. Expand
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ProteinNet: a standardized data set for machine learning of protein structure
TLDR
We created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships in programmatically accessible file formats tailored for machine learning frameworks. Expand
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End-to-end differentiable learning of protein structure
Predicting protein structure from sequence is a central challenge of biochemistry. Co‐evolution methods show promise, but an explicit sequence‐to‐structure map remains elusive. Advances in deepExpand
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Unified rational protein engineering with sequence-only deep representation learning
TLDR
We apply deep learning to unlabelled amino acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily, and biophysically grounded. Expand
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AlphaFold at CASP13
TLDR
We contextualize the significance of DeepMind's entry within the broader history of CASP, relate AlphaFold's methodological advances to prior work, and speculate on the future of this important problem. Expand
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Direct inference of protein–DNA interactions using compressed sensing methods
Compressed sensing has revolutionized signal acquisition, by enabling complex signals to be measured with remarkable fidelity using a small number of so-called incoherent sensors. We show thatExpand
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A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks
Functional interpretation of genomic variation is critical to understanding human disease, but it remains difficult to predict the effects of specific mutations on protein interaction networks andExpand
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End-to-End Differentiable Learning of Protein Structure.
Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deepExpand
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Pyruvate kinase M2: A simple molecule with complex functions.
Pyruvate kinase M2 is a critical enzyme that regulates cell metabolism and growth under different physiological conditions. In its metabolic role, pyruvate kinase M2 catalyzes the last glycolyticExpand
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Three enhancements to the inference of statistical protein‐DNA potentials
The energetics of protein‐DNA interactions are often modeled using so‐called statistical potentials, that is, energy models derived from the atomic structures of protein‐DNA complexes. ManyExpand
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