What is AlphaFold?

AlphaFold is a groundbreaking deep learning algorithm developed by DeepMind for predicting protein structures with high accuracy. It has demonstrated remarkable performance in the Critical Assessment of protein Structure Prediction (CASP) competition, significantly outperforming other methods, and has been hailed as a major breakthrough in computational biology. Accurate protein structure prediction can have wide-ranging implications in drug discovery, disease understanding, and bioengineering.

How does AlphaFold work?

AlphaFold combines several techniques to predict protein structures, including:

  1. Multiple sequence alignment: AlphaFold uses a deep learning model called the Alphafold neural network to process multiple sequence alignments of homologous proteins, capturing evolutionary information that can inform the prediction.

  2. Distance and torsion angle predictions: The Alphafold neural network predicts pairwise distances between amino acids and torsion angles for the protein backbone.

  3. Optimization and structure assembly: AlphaFold uses gradient descent optimization to refine the protein structure, minimizing an energy function based on the predicted distances and angles.

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