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High-performance transformation of protein structure representation from internal to Cartesian coordinates.

Mahsa BayatiMiriam LeeserJaydeep P Bardhan
Published in: Journal of computational chemistry (2020)
We present a highly parallel algorithm to convert internal coordinates of a polymeric molecule into Cartesian coordinates. Traditionally, converting the structures of polymers (e.g., proteins) from internal to Cartesian coordinates has been performed serially, due to an inherent linear dependency along the polymer chain. We show this dependency can be removed using a tree-based concatenation of coordinate transforms between segments, and then parallelized efficiently on graphics processing units (GPUs). The conversion algorithm is applicable to protein engineering and fitting protein structures to experimental data, and we observe an order of magnitude speedup using parallel processing on a GPU compared to serial execution on a CPU.
Keyphrases
  • machine learning
  • protein protein
  • amino acid
  • deep learning
  • binding protein
  • high resolution
  • drug delivery
  • electronic health record
  • small molecule
  • artificial intelligence