Prediction of protease substrates using sequence and structure features

Barkan, David T.; Hostetter, Daniel R.; Mahrus, Sami; Pieper, Ursula; Wells, James A.; Craik, Charles S.; Šali, Andrej

doi:10.1093/bioinformatics/btq267

Cited by 63 publications

(72 citation statements)

References 50 publications

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“…Our data support the view that both sequence and structure determine where a protein is cut. We find caspase-2 and caspase-6 cut their substrates on average 1.2-1.3 times per protein, similar to the average number of cuts per substrate protein seen previously for caspase-3, caspase-7, and caspase-8 (10) and observed globally in apoptosis (18, aspartic acids are cleavage sites, clearly showing that extended sequence is critical (8,31). Moreover, using structural bioinformatics for sites of known or modeled proteins, we find that caspase-2 and caspase-6 have similar secondary structural preferences for cutting loops > helices > sheets (SI Appendix, Fig.…”

Section: Discussionsupporting

confidence: 87%

“…Similarly, we obtained an average SVM score of 0.26 for caspase-6. These data further validate the methodology used by Barkan et al to predict caspase cleavage sites, exploiting both the primary sequence and structural features of the substrates (31). It is possible these predictive models could be improved for the specific caspases if they used training sets based on the catalytic efficiencies for individual substrates for each of the caspases generated here, instead of a more generic motif derived from apoptosis data that likely emphasizes capase-3, capase-7, and capase-8 cleavage sites.…”

Section: Discussionsupporting

confidence: 77%

“…The use of structural information (secondary structure, solvent accessibility, degrees of disordered regions), in combination with the primary sequence of validated apoptotic substrates, has been previously used to predict caspase cleavage sites (31). In these studies, all possible caspase cleavage sites in the human proteome were ranked on the basis of their support vector machine (SVM) score, representing the likelihood of a given site to be a "real" caspase cleavage site.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles

Julien

Zhuang

Wiita

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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105

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Proteases constitute the largest enzyme family, yet their biological roles are obscured by our rudimentary understanding of their cellular substrates. There are 12 human caspases that play crucial roles in inflammation and cell differentiation and drive the terminal stages of cell death. Recent N-terminomics technologies have begun to enumerate the diverse substrates individual caspases can cleave in complex cell lysates. It is clear that many caspases have shared substrates; however, few data exist about the catalytic efficiencies (k cat /K M ) of these substrates, which is critical to understanding their true substrate preferences. In this study, we use quantitative MS to determine the catalytic efficiencies for hundreds of natural protease substrates in cellular lysate for two understudied members: caspase-2 and caspase-6. Most substrates are new, and the cleavage rates vary up to 500-fold. We compare the cleavage rates for common substrates with those found for caspase-3, caspase-7, and caspase-8, involved in apoptosis. There is little correlation in catalytic efficiencies among the five caspases, suggesting each has a unique set of preferred substrates, and thus more specialized roles than previously understood. We synthesized peptide substrates on the basis of protein cleavage sites and found similar catalytic efficiencies between the protein and peptide substrates. These data suggest the rates of proteolysis are dominated more by local primary sequence, and less by the tertiary protein fold. Our studies highlight that global quantitative rate analysis for posttranslational modification enzymes in complex milieus for native substrates is critical to better define their functions and relative sequence of events.N-terminomics | caspase | proteolysis | proteomics | apoptosis C aspases are cysteine-class proteases that typically cleave after aspartic acid and play critical roles in cellular remodeling during development, cell differentiation, inflammation, and cell death (reviewed in refs. 1-3). There are 12 caspases in humans alone, which have been classically grouped on the basis of sequence homology, domain architecture, and cell biology as inflammatory (caspase-1, caspase-4, caspase-5, and caspase-11), apoptotic initiators (caspase-2, caspase-8, caspase-9, and caspase-10), or executioners (caspase-3, caspase-6, and caspase-7) (4, 5). Identifying the natural protein substrates for caspases can provide important insights into their cellular roles. A series of large-scale proteomic studies from several laboratories has collectively identified nearly 2,000 native proteins cleaved after an aspartic acid during apoptosis of human cells (recently reviewed in refs. 6-8). By adding individual caspases to extracts, it has been possible to begin to identify caspasespecific substrates, ranging from just a few, in the case of caspase-4, caspase-5, and caspase-9 (9), to several hundred for caspase-1, caspase-3, caspase-7, and caspase-8 (10, 11). Some of these substrates overlap, but others are unique to specific caspase...

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Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles

Julien

Zhuang

Wiita

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

105

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“…The many findings coming out of high throughputbased approaches have been organized on the protease platform "Merops", a database providing detailed and carefully validated data on proteases, their substrates, and their inhibitors (Rawlings et al 2014). Bioinformatics platforms enable one to predict the substrate cleavage specificity of a given protease; some of these programs may also give hints on proteases that potentially cleave a known substrate at a specific cleavage site (Barkan et al 2010;Biniossek et al 2011;Lee and Bogyo 2013;Lohmuller et al 2003;Ng et al 2009;Schilling and Overall 2008;Song et al 2012).…”

Section: The Protease Family Businessmentioning

confidence: 99%

Proteolysis mediated by cysteine cathepsins and legumain—recent advances and cell biological challenges

et al. 2014

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Proteases play essential roles in protein degradation, protein processing, and extracellular matrix remodeling in all cell types and tissues. They are also involved in protein turnover for maintenance of homeostasis and protein activation or inactivation for cell signaling. Proteases range in function and specificity, with some performing distinct substrate cleavages, while others accomplish proteolysis of a wide range of substrates. As such, different cell types use specialized molecular mechanisms to regulate the localization of proteases and their function within the compartments to which they are destined. Here, we focus on the cysteine family of cathepsin proteases and legumain, which act predominately within the endo-lysosomal pathway. In particular, recent knowledge on cysteine cathepsins and their primary regulator legumain is scrutinized in terms of their trafficking to endo-lysosomal compartments and other less recognized cellular locations. We further explore the mechanisms that regulate these processes and point to pathological cases which arise from detours taken by these proteases. Moreover, the emerging biological roles of specific forms and variants of cysteine cathepsins and legumain are discussed. These may be decisive, pathogenic, or even deadly when localizing to unusual cellular compartments in their enzymatically active form, because they may exert unexpected effects by alternative substrate cleavage. Hence, we propose future perspectives for addressing the actions of cysteine cathepsins and legumain as well as their specific forms and variants. The increasing knowledge in non-canonical aspects of cysteine cathepsin- and legumain-mediated proteolysis may prove valuable for developing new strategies to utilize these versatile proteases in therapeutic approaches.

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“…Over the past decades, an increasing number of diverse feature encoding methods or descriptors extracted from protein and peptide sequence information have been proposed for improving various predictions. Applications include predicting protein structural and function classes (Chou and Fasman, 1978), protein-protein interactions, protein-ligand interactions (Cao et al, 2015;Shen et al, 2007), subcellular locations , enzyme substrates (Barkan et al, 2010;Rottig et al, 2010;Song et al, 2010), among others.…”

Section: Introductionmentioning

confidence: 99%

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

et al. 2018

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Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation:

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Prediction of protease substrates using sequence and structure features

Abstract: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands.

Cited by 63 publications

References 50 publications

Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles

Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles

Proteolysis mediated by cysteine cathepsins and legumain—recent advances and cell biological challenges

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

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