State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold

The problem of predicting a protein's 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models' accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a…

• NS
  National Science Foundation

  Award: MCB2032259
• SF
  Simons Foundation

  Award: 735929LPI
• NI
  National Institutes of Health

  Award: DP5OD026389