CrysFormer: Protein Structure Prediction via 3d Patterson Maps and
  Partial Structure Attention
Chen Dun Qiutai Pan Shikai Jin Ria Stevens Mitchell D. Miller George N. Phillips, Jr. Anastasios Kyrillidis
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. To the best of our knowledge, we propose the first
transformer-based model that directly utilizes protein crystallography and
partial structure information to predict the electron density maps of proteins.
Via two new datasets of peptide fragments (2-residue and 15-residue) , we
demonstrate our method, dubbed \texttt{CrysFormer}, can achieve accurate
predictions, based on a much smaller dataset size and with reduced computation
costs.