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

• URL: http://arxiv.org/abs/2310.03899v1
• Date: Thu, 5 Oct 2023 21:10:22 GMT
• Title: CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention
• Authors: Chen Dun, Qiutai Pan, Shikai Jin, Ria Stevens, Mitchell D. Miller, George N. Phillips, Jr., Anastasios Kyrillidis
• Abstract summary: A protein's three-dimensional structure often poses nontrivial computation costs. We propose the first transformer-based model that directly utilizes protein crystallography and partial structure information. We demonstrate our method, dubbed textttCrysFormer, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.
• Score: 7.716601082662128
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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