NoLogo: a new statistical model highlights the diversity and suggests new classes of Crm1-dependent nuclear export signals

BMC Bioinformatics
Muluye E LikuAlan M Moses

Abstract

Crm1-dependent Nuclear Export Signals (NESs) are clusters of alternating hydrophobic and non-hydrophobic amino acid residues between 10 to 15 amino acids in length. NESs were largely thought to follow simple consensus patterns, based on which they were categorized into 6-10 classes. However, newly discovered NESs often deviate from the established consensus patterns. Thus, identifying NESs within protein sequences remains a bioinformatics challenge. We describe a probabilistic representation of NESs using a new generative model we call NoLogo that can account for a large diversity of NESs. Using this model to predict NESs, we demonstrate improved performance over PSSM and GLAM2 models, but do not achieve the performance of the state-of-the-art NES predictor LocNES. Our findings illustrate that over 30% of NESs are best described by novel NES classes rather than the 6-10 classes proposed by current/existing models. Finally, many NESs have additional hydrophobic residues either upstream or downstream of the canonical four residues, suggesting possible functionality. Applying the NoLogo model highlights the observation that NESs are more diverse than previously appreciated. Our work questions the practice of assigning each NES to ...Continue Reading

References

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Software Mentioned

RStudio
Wregex
DISOPRED3
GLAM2
PSSM
NESmapper
LocNES
NoLogo
R
ComplexHeatmap

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