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

Abstract

BackgroundCrm1-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.ResultsWe 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.ConclusionApplying the NoLogo model highlights the observation that NESs are more diverse than previously appreciated. Our work questions the practice of assigning each NES to one of several predefined NES classes. Finally, our analysis suggests a novel and testable biophysical perspective on interaction between Crm1 receptor and Crm1-dependent NESs.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2076-7) contains supplementary material, which is available to authorized users.

Comprehensive analysis of nuclear export of herpes simplex virus type 1 tegument proteins and their Epstein‐Barr virus orthologs

“…Hence, experimental identification and validation is mandatory. However, since mutational analysis of HSV1 NESs predicted by the NES pattern and by NetNES confirmed their functionality in all cases, the major strength of a range of developed NES prediction algorithms may currently lie in the detection of the relevant export signals in proteins known to be exported.…”

“…Initially, NES prediction was limited to two bioinformatic tools, the NES pattern and the NetNES (http://www.cbs.dtu.dk/services/NetNES ). More recently, other bioinformatic tools were developed including the LocNES algorithm (http://prodata.swmed.edu/LocNES/LocNES.php ). Comprehensive analysis of all HSV1 tegument proteins using the LocNES revealed that, with few exceptions (pUL41, pUS2, pUS11), most HSV1 tegument proteins were predicted to contain a leucine‐rich NES with a score >0.200 (Table ; Figure A).…”

“…[16][17][18] While NLSs with basic stretches can be identified with high confidence based on consensus sequences, prediction of a functional NES using bioinformatic tools is difficult and often results in false positives and false negatives. [19][20][21][22][23] Therefore, the export activity of a protein needs to be determined experimentally, in vivo and in the context of the folded protein, and predicted NESs require experimental confirmation.…”

“…Consensus sequences have been deduced for basic mono‐ and bipartite NLSs recognized by the importin‐α/β heterodimer, for signals recognized by four other import factors of the importin β family and for leucine‐rich NESs . While NLSs with basic stretches can be identified with high confidence based on consensus sequences, prediction of a functional NES using bioinformatic tools is difficult and often results in false positives and false negatives . Therefore, the export activity of a protein needs to be determined experimentally, in vivo and in the context of the folded protein, and predicted NESs require experimental confirmation.…”

Identification of peptide domains involved in the subcellular localization of the feline coronavirus 3b protein

“…Like nuclear import, shuttling proteins from the nucleus back to the cytoplasm is established by a nuclear export signal (NES) that is recognized by exportins such as CRM1. A wellcharacterized NES is the export motifϕχ (2)(3) ϕχ (2)(3) ϕχϕ (where ϕ represents one of the hydrophobic residues L, V, I, F or M and χ is any residue) [92,93], but this consensus pattern has been progressively expanded and computational methods have been addressed to further model the sequence diversity of NESs [93][94][95][96][97][98][99][100].…”