Protease target prediction via matrix factorization

Marini, Simone; Vitali, Francesca; Rampazzi, Sara; Demartini, Andrea; Akutsu, Tatsuya

doi:10.1093/bioinformatics/bty746

Cited by 9 publications

(7 citation statements)

References 42 publications

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“…Both ensemble tree classifiers outperformed logistic regression and SVC for all caspases by comparing all three metrics. All evaluated metrics (accuracy, AUC PRC, AUC ROC, MCC, sensitivity and specificity) for all investigated classifiers comparing predictive performance of these models for all selected proteases using window P4-P4' are shown in S8 Table. In addition, predictive performance of RFC and GBC models was equal 21 for caspases-2 and 6, meanwhile RFC demonstrated higher accuracy for caspase-7 and GBC overperformed RFC model for caspase-1 that also can be related with the size of the training dataset. Lastly, we evaluated predictive performance of LR, SVC, GBC and RFC models on external dataset to understand if trained models were able to predict known cleavage sites with the top-ranking positions for the protease of interest.…”

Section: Resultsmentioning

confidence: 93%

“…Proteolytic enzymes play critical role in many processes including cell proliferation, immune response, cell death and others [20]. Protease specificity was studied in different studies [21,36]. Protease cleavage of peptides is directed by short amino acid motifs, from two to eight amino acids around the scissile bond (site of cleavage, SoC) [17].…”

Section: Introductionmentioning

confidence: 99%

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Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery: Peptide cleavage sites prediction workflow

Radchenko

Fontaine

Morettoni

et al. 2018

Preprint

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Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

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Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery: Peptide cleavage sites prediction workflow

Radchenko

Fontaine

Morettoni

et al. 2018

Preprint

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“…Even if the source is diligently measured on the cell-type level, more studied cell types might weight more than less studied ones in the gene network definition. Finally, even the knowledge of single gene and protein interactions is unbalanced toward “superstar” genes or proteins, which are more studied than others Lam et al (2016); Marini et al (2018). Users should select the gene label annotation source carefully and according to the problem they want to analyze.…”

Section: Resultsmentioning

confidence: 99%

MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data

Vella

Coronnello

et al. 2019

Front. Genet.

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The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at .

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“…Accordingly, we explore a space transformation—with concomitant dimension reduction—of the k -mer spectrum that identifies a set of (orthogonal) multiple features, i.e., metafeatures, each as an independent combination of the original k -mers contributing to a cumulative portion of the data variance. To do so, we apply a matrix factorization approach, which has been previously shown apt to tackle complex feature extraction problems, e.g., oncology and proteomics [ 36 , 37 ]. The method is based on non-negative matrix tri-factorization [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

et al. 2022

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Background Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. Results We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2–0.9). On semi-synthetic metagenomic data—external test—on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. Conclusions AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

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Protease target prediction via matrix factorization

Abstract: Supplementary data are available at bioinformatics online.

Cited by 9 publications

References 42 publications

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery: Peptide cleavage sites prediction workflow

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery: Peptide cleavage sites prediction workflow

MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

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