ArticleJournal of molecular modeling2017
NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins containing classical nuclear localization signals.
Article in Journal of molecular modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Identification of candidate nucleomodulins in ESKAPE bacteria -Frontiers in cellular and infection microbiology · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of a nuclear localization signal (NLS) in proteins can be inferred by the presence of a stretch of basic amino acids (KRKK). These NLSs are termed classical NLS (cNLS). However, only a fraction of proteins containing the cNLS pattern are transported into the nucleus by binding to importin α. Hence, there must exist, additional structural determinants that guide the appropriate interaction between putative NLSs containing cargo and importin α. Using 52 protein structures containing cNLS obtained from RCSB PDB, we assembled a training set and a validation set such that both sets were comprised of a combination of proteins with proven nuclear localization and ones that were non-nuclear. We modeled the interface between cargoes containing cNLS and importin α. We conducted rigid body docking and produced induced-fit modes by allowing both side chain and the backbone to be flexible. The output of these studies and additional determinants such as energy of interaction, atomic contacts, hydrophilic interaction, cationic interaction, and penetration of the cargo protein were used to derive a 26 parameter quantitative structure activity relationship based regression equation. This was further optimized by a step-wise backward elimination approach to derive a 15 parameter score. This NLScore was not only able to correctly classify confirmed nuclear and non-nuclear localized proteins but it was able to perform better than currently implemented algorithms like NucPred, Euk-mPLoc 2.0, cNls Mapper, and NLStradamus. Leave-one-out cross validation (LOOCV) showed that NLScore correctly predicted 78.6% and 81.6% of non-nuclear and nuclear proteins respectively. Graphical abstract NLScore: a novel quantitative algorithm based on 3 dimensional structural determinants to predict the probability of nuclear localization in proteins.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.