CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention

Understand

Determining the structure of a protein has been a decades-long open question.

• A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized.
• Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvements for this problem, by learning from a large dataset of sequence information and corresponding protein structures.
• Yet, such methods only focus on sequence information; other available prior knowledge, such as protein crystallography and partial structure of amino acids, could be potentially utilized.