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SWAMPNN: End-to-end protein structures alignment
Jeanne Trinquier · Samantha Petti · Shihao Feng · Johannes Soeding · Martin Steinegger · Sergey Ovchinnikov

Sat Dec 03 08:30 AM -- 08:45 AM (PST) @
in Machine Learning in Structural Biology Workshop »

With the recent breakthrough of highly accurate structure prediction methods, there has been a rapid growth of available protein structures. Efficient methods are needed to infer structural similarity within these datasets. We present an end-to-end alignment method, called SWAMPNN, that takes as input the 3D coordinates of a protein pair and outputs a structural alignment. We show that the model is able to recapitulate TM-align alignments while running faster and is more accurate than Foldseek on the alignment task while being comparable for classification.

Author Information

Jeanne Trinquier (LCQB)
Samantha Petti (Harvard University)
Shihao Feng (Shanghai Jiao Tong University)
Johannes Soeding (Max Planck Institute for Multidisciplinary Sciences)
Martin Steinegger (Seoul National University)
Sergey Ovchinnikov (Harvard University)

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