PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment

Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue

Research output: Contribution to conferencePaperpeer-review


Protein sequence alignment is a fundamental problem in computational structure biology and popular for protein 3D structural prediction and protein homology detection. Most of the developed programs for detecting protein sequence alignments are based upon the likelihood information of amino acids and are sensitive to alignment noises. We present a robust method PALM for modeling pairwise protein structure alignments, using the area distance to reduce the biological measurement noise. PALM generatively learn the alignment of two protein sequences with probabilistic area distance objective, which can denoise the measurement errors and offsets from different biologists. During learning, we show that the optimization is computationally efficient by estimating the gradients via dynamically sampling alignments. Empirically, we show that PALM can generate sequence alignments with higher precision and recall, as well as smaller area distance than the competing methods especially for long protein sequences and remote homologies. This study implies for learning over large-scale protein sequence alignment problems, one could potentially give PALM a try.

Original languageEnglish (US)
Number of pages10
StatePublished - 2021
Event37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 - Virtual, Online
Duration: Jul 27 2021Jul 30 2021


Conference37th Conference on Uncertainty in Artificial Intelligence, UAI 2021
CityVirtual, Online

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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