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    Deep neural networks for protein structure prediction - overview of derivative work

    Structural Biology
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      admin last edited by admin

      Papers

      [1] Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling
      [2] Current protein structure predictors do not produce meaningful folding pathways
      [3] Harnessing protein folding neural networks for peptide-protein docking
      [4] Improved prediction of protein-protein interactions using AlphaFold2
      [5] AlphaFold2: A role for disordered protein prediction?
      [6] AlphaFold2 transmembrane protein structure prediction shines
      [7] Can AlphaFold2 predict protein-peptide complex structures accurately?
      [8] Improved prediction of protein-protein interactions using AlphaFold2
      [9] Possible Implications of AlphaFold2 for Crystallographic Phasing by Molecular Replacement
      [10] Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro
      [11] Identification of Iron-Sulfur (Fe-S) and Zn-binding Sites Within Proteomes Predicted by DeepMind’s AlphaFold2 Program Dramatically Expands the Metalloproteome

      Glossary

      Intrinsically Disordered Proteins (IDPs) are a large class of proteins without a rigid structure which accomplish their function despite (or thanks to) their dynamic behavior. They can become rigid in complexes with other molecules.

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