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

Keywords: AI for science, protein crystallography, Transformer model

TL;DR: We propose the first transformer-based model that directly utilizes protein crystallography and partial structure information to directly predict electron density map of proteins, one step beyond current approaches.

Abstract: 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 AlphaFold-- 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 CrysFormer, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 8479