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17875509f15067b736fe9c933970ace3e1ab9af2d867a26b2963ac05a9ae0a40  nucacires_a2026m7v54nw1pw327.pdf

Title:           SPSignal: a web tool for structure-assisted prediction of nuclear localization and nuclear export signals in proteins
Subject:         DOI: 10.1093/nar/gkag421 Nucleic Acids Research, 54, Web Server issue, 11-5-2026. Abstract: Nuclear localization signals (NLSs) and nuclear export signals (NESs) mediate nucleocytoplasmic transport of proteins through the nuclear pore complex and are essential determinants of protein function. However, their short and degenerate sequence patterns frequently lead to high false-positive rates in sequence-based prediction methods, as similar motifs occur widely in proteins without mediating nuclear transport. Here, we present SPSignal, a webserver for improved identification of NLS and NES motifs by integrating sequence-based predictions with structural features. SPSignal combines curated datasets of experimentally validated signals with analyses of solvent accessibility, intrinsic disorder, and structural context derived from experimental or predicted protein structures. Using these features, interpretable machine-learning models based on the RuleFit algorithm prioritize candidate motifs that are structurally exposed and therefore more likely to be functional. The web server integrates sequence predictors with structure-informed analyses in a unified workflow that accepts protein sequences or structures as input and provides interactive visualization of predicted signals within their three-dimensional context. SPSignal assigns confidence scores to candidate motifs and allows users to explore their spatial distribution along protein sequences and structures. Application to proteins with validated localization signals shows that SPSignal improves prediction accuracy by reducing false positives without compromising sensitivity. SPSignal is available at https://sps.cragenomica.es.
Keywords:        
Author:          Engler Camila, Abriata Luciano A., Bologna Nicolas G.
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    Subject: DOI: 10.1093/nar/gkag421 Nucleic Acids Research, 54, Web Server issue, 11-5-2026. Abstract: Nuclear localization signals (NLSs) and nuclear export signals (NESs) mediate nucleocytoplasmic transport of proteins through the nuclear pore complex and are essential determinants of protein function. However, their short and degenerate sequence patterns frequently lead to high false-positive rates in sequence-based prediction methods, as similar motifs occur widely in proteins without mediating nuclear transport. Here, we present SPSignal, a webserver for improved identification of NLS and NES motifs by integrating sequence-based predictions with structural features. SPSignal combines curated datasets of experimentally validated signals with analyses of solvent accessibility, intrinsic disorder, and structural context derived from experimental or predicted protein structures. Using these features, interpretable machine-learning models based on the RuleFit algorithm prioritize candidate motifs that are structurally exposed and therefore more likely to be functional. The web server integrates sequence predictors with structure-informed analyses in a unified workflow that accepts protein sequences or structures as input and provides interactive visualization of predicted signals within their three-dimensional context. SPSignal assigns confidence scores to candidate motifs and allows users to explore their spatial distribution along protein sequences and structures. Application to proteins with validated localization signals shows that SPSignal improves prediction accuracy by reducing false positives without compromising sensitivity. SPSignal is available at https://sps.cragenomica.es.
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               <rdf:li xml:lang="x-default">DOI: 10.1093/nar/gkag421 Nucleic Acids Research, 54, Web Server issue, 11-5-2026. Abstract: Nuclear localization signals (NLSs) and nuclear export signals (NESs) mediate nucleocytoplasmic transport of proteins through the nuclear pore complex and are essential determinants of protein function. However, their short and degenerate sequence patterns frequently lead to high false-positive rates in sequence-based prediction methods, as similar motifs occur widely in proteins without mediating nuclear transport. Here, we present SPSignal, a webserver for improved identification of NLS and NES motifs by integrating sequence-based predictions with structural features. SPSignal combines curated datasets of experimentally validated signals with analyses of solvent accessibility, intrinsic disorder, and structural context derived from experimental or predicted protein structures. Using these features, interpretable machine-learning models based on the RuleFit algorithm prioritize candidate motifs that are structurally exposed and therefore more likely to be functional. The web server integrates sequence predictors with structure-informed analyses in a unified workflow that accepts protein sequences or structures as input and provides interactive visualization of predicted signals within their three-dimensional context. SPSignal assigns confidence scores to candidate motifs and allows users to explore their spatial distribution along protein sequences and structures. Application to proteins with validated localization signals shows that SPSignal improves prediction accuracy by reducing false positives without compromising sensitivity. SPSignal is available at https://sps.cragenomica.es.</rdf:li>
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         <dc:contributor>
            <rdf:Seq>
               <rdf:li>Engler Camila</rdf:li>
               <rdf:li>Abriata Luciano A.</rdf:li>
               <rdf:li>Bologna Nicolas G.</rdf:li>
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               <rdf:li>Oxford University Press</rdf:li>
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               <rdf:li>Engler Camila, Abriata Luciano A., Bologna Nicolas G.</rdf:li>
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