Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis
Abstract
:1. Introduction
2. Short Linear Motifs and Their Emerging Role in Cell Biology and Cancer
3. In Silico Methods to Study PPI Networks
4. In Silico Approaches and Tools to Characterize SLiMs in PPI Networks
5. SLiMs in CRC Molecular Networks
5.1. SLiMs in CRC Signaling Pathways and Tumorigenesis
5.2. SLiMs in CRC Hallmarks: A Case Study
5.3. SLiMs in CRC-Related Microbiome
6. Potential Small-Molecule Anticancer Drugs Based on SLiMs and Short Peptides in CRC: Where Do We Stand?
6.1. Pharmacological Suitability of SLiMs and Short Peptides as Anticancer Drugs
6.2. Short Peptides as Potential Anti-CRC Drugs
6.3. Short Peptides in Clinical Studies
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name of In Silico Resource | Website | Description | Technical Advantages | Refs |
---|---|---|---|---|
PINA (Protein Interaction Network Analysis) | https://omics.bjcancer.org/pina/ (accessed on 3 September 2022) | An integrated server for PPI non-redundant and curated data referred to six model organisms | A useful tool to provide comprehensive PPI information through integrated visualization of PPI data and construction, filtering, and data analysis | [40,41] |
SPRINT (Scoring Protein INTeractions) | www.csd.uwo.ca/faculty/ilie/SPRINT (accessed on 3 September 2022) | This algorithm enables the computational organization of existing PPI networks to the level of species interactomes | It simplifies the interpretation of the results and makes them more objective through a PPI score | [42] |
Path2PPI | http://bioconductor.org/packages/release/bioc/html/Path2PPI.html (accessed on 3 September 2022) | This algorithm analyzes the homology between protein sequences of multiple organisms or a single target organism. | It allows users to combine sequence similarity searches of the examined proteins with their functional information about a particular pathway | [43] |
Paralog Matching | https://github.com/Mirmu/ParalogMatching.jl (accessed on 3 September 2022) | This algorithm predicts interacting paralogs between two distinct protein families. It enables homology analysis of all members of two protein families that belong to the same species and predicts PPIs, maximizing the detectable coevolutionary signal. | It provides a direct correlation by amino acid occurrences between multiple sequence alignment (MSA) and interprotein residue–residue contacts in the PPI | [44] |
RaptorX-ComplexContact server | http://raptorx.uchicago.edu/ (accessed on 3 September 2022) | This server analyzes the interfacial contacts between two potentially interacting heterodimeric protein sequences using deep-learning techniques. | A useful tool for protein docking analysis, protein–protein interaction prediction, and protein interaction network construction | [45] |
COZOID (Contact Zone Identifier) | http://decibel.fi.muni.cz/cozoid (accessed on 3 September 2022) | This algorithm analyzes several docking structures covering the three major types of PPIs (coiled-coil, pocket-string, and surface–surface interactions) and their contact zones with different levels of detail. | It provides docking models of interacting proteins and enables the selection of the best docking structures based on their similarity to a conserved structure from reference homologous proteins | [46] |
Path-LZerD | https://kiharalab.org/proteindocking/pathlzerd.php (accessed on 3 September 2022) | This software predicts the assembly order of multimeric proteins starting from single subunit structures. | A useful tool to design drugs that target crucial interactions within a specific complex | [47] |
ReactomeFIViz | https://reactome.org/tools/reactome-fiviz (accessed on 3 September 2022) | This Cytoscape application facilitates the pathway- and network-based analysis of RNA-seq and other omics datasets using the Reactome pathway database. | It allows users to link the PPI dataset reported in two or more databases | [48] |
KeyPathwayMiner | https://apps.cytoscape.org/apps/keypathwayminer (accessed on 3 September 2022) | This Cytoscape application detects highly connected PPI networks in which genes show similar expression. | A useful tool to combine interaction network data with omics datasets in order to identify novel functional peptide modules | [49] |
VieClus (Vienna Graph Clustering) | http://vieclus.taa.univie.ac.at/ (accessed on 3 September 2022) | This software enables the visualization of PPI clusters showing similar functional modules. | It allows users to identify functional modules by searching for sets of proteins whose interactions are dense within the sets but sparse between the sets | [50] |
TD-WGcluster (Time Delayed Weighted Edge Clustering portal) | https://www.r-project.org/ (accessed on 3 September 2022) | This algorithm integrates the three-dimensional topology of PPIs. | It combines PPI topology with a dynamics component derived from time series data | [51] |
SANA (Simulated Annealing Network Aligner) | https://sana.ics.uci.edu/ (accessed on 3 September 2022) | This alignment software compares PPI motifs between different species. | A useful tool to perform comparative analyses of PPI networks to reveal evolutionary relationships between species | [52] |
PEPPI (Predicted Protein-protein Interactions) | https://zhanggroup.org›PEPPI (accessed on 3 September 2022) | This alignment software predicts the exact peptide modules involved in binary interactions between two amino acid sequences. | It integrates multiple independent prediction and analysis methods of protein sequence similarity, structural homology, functional association, and machine learning-based classification | [53] |
CPDB (Consensus PathDB) | http://cpdb.molgen.mpg.de/ (accessed on 3 September 2022) | A comprehensive database for studying human PPI networks and related information (biochemical pathway, genetic, metabolic, signaling data, and drug–target interactions). | A useful tool to provide a correct interpretation of the massive quantities of PPI molecular data | [54] |
IID (Integrated Interactions Database) | http://iid.ophid.utoronto.ca/ (accessed on 3 September 2022) | A curated database containing comprehensive information on PPIs detected and predicted in 18 species, including humans. | A useful tool to study PPIs in specific conditions (e.g., tissues, developmental stages), conservation across species, directionality of the interaction, and druggability | [55] |
HIPPIE (Human Integrated Protein-Protein Interaction rEference) | http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/ (accessed on 3 September 2022) | This resource integrates multiple human PPI databases. | It allows users to overlay gene expression data and other annotation resources to construct protein networks specific to a tissue, disease, or subcellular localization | [56] |
MIPS (Mammalian Protein-Protein Interaction Database) | http://mips.helmholtz-muenchen.de/proj/ppi/ (accessed on 3 September 2022) | A manually curated database of high-quality PPI data from the scientific literature. | A useful tool for the metanalysis of current scientific literature on mammalian PPIs | [57] |
OncoPPi Portal | https://oncoppi.emory.edu/ (accessed on 3 September 2022) | A comprehensive PPI network database concerning cancer; | This tool is used in cancer research to provide genetic, pharmacological, clinical, and structural data and combine them with the network of cancer-associated PPIs experimentally found in tumor cells | [38,39] |
BioGRID | https://thebiogrid.org/ (accessed on 3 September 2022) | A comprehensive repository of PPI data providing information on their druggability; | A useful tool to study oncoprotein-chemical compound associations, based on experimental data (freely available in a variety of standardized formats) | [58] |
IntAct | https://www.ebi.ac.uk/intact/home (accessed on 3 September 2022) | This is an open-source PPI database. IntAct data are organized in three clusters of information (proteomes, datasets, and mutations) that simplify the search for database entries. | A useful tool to analyze current experimentally derived PPI data from the published scientific literature; it also offers free tools for integration and analysis purposes | [59] |
Name of In Silico Resource | Website | Description | Technical Advantages | Refs |
---|---|---|---|---|
PIPE (Protein–Protein Interaction Prediction Engine) | https://pipe.rcc.fsu.edu/ (accessed on 4 September 2022) | This algorithm predicts the binding sites involved in PPIs based on query protein sequences and a database of known binary interaction data. The outcome is a three-dimensional graph where the peaks signify a high co-occurrence of the corresponding sequences among known interacting proteins | A useful tool to identify PPI consensus motifs. The PIPE method is based on re-occurrences of peptide sequences that mediate a large number of PPIs. | [62] |
MnM (Minimotif Miner database) | http://minimotifminer.org or http://mnm.engr.uconn.edu (accessed on 4 September 2022) | This comprehensive database reports over 300,000 functional SLiMs in protein queries. | A database designed to improve prediction accuracy, allowing users to search for SLiMs with a set of false-positive filters and linear regression scoring. | [63] |
LMPID (Linear Motif Mediated Protein Interaction Database) | http://bicresources.jcbose.ac.in/ssaha4/lmpid (accessed on 4 September 2022) | This manually curated database reports experimentally validated data about SLiM-mediated PPIs from any organism. It contains comprehensive information about 1762 unique SLiMs mediating 2215 PPIs among 1187 bait and 559 prey proteins. | This tool is mainly used to improve knowledge of the patterns of SLiMs binding to a specific domain and to formulate PPI inhibitors/modulators of interest. | [64] |
ADAN (protein-protein interAction of moDular domAiN database) | https://adan-embl.ibmc.umh.es/ (accessed on 4 September 2022) | This manually integrated and curated database is used for the prediction of PPI-mediating SLiMs. It currently contains 3505 entries comprising structural and functional SLiM information (biochemical data, sequence files, and alignments), which is cross-referenced to other databases. The in silico prediction method is based on position-specific scoring matrices. | A useful tool to predict exact SLiMs and the best ligand and putative binding partner candidates of a protein of interest. | [65] |
ELM (Eukaryotic Linear Motif) | http://elm.eu.org/ (accessed on 4 September 2022) | This manually curated platform contains different types of experimentally validated SLiM data from current literature. The classification of ELM entries is based on motif type, functional site, and ELM class. The ELM class is a specific list of experimentally validated SLiMs matching the examined query sequence. | A very versatile resource that is useful for various purposes in SLiM-related studies. It provides both a database of annotated SLiM data and an exploratory tool to predict them. | [15] |
SLiMAN (is a recent database) | https://sliman.cbs.cnrs.fr (accessed on 4 September 2022) | This web server contains complementary information from the Uniprot (https://www.uniprot.org/; accessed on 4 September 2022), ELM (http://elm.eu.org/; accessed on 4 September 2022), IUpred2 (https://iupred2a.elte.hu/; accessed on 4 September 2022), BioGrid (https://thebiogrid.org/; accessed on 4 September 2022), and PhosphoSitePlus (https://www.phosphosite.org/; accessed on 4 September 2022) databases. These databases have been integrated to provide a comprehensive analysis of SLiM sequences (annotated in the ELM database), motif disorder scores (annotated in the IUpred2 server), predicted and experimentally validated PPIs (annotated in the BioGrid database), and PTMs (annotated in the Uniprot and PhosphoSitePlus databases). | A useful tool designed to overcome the various limitations related to the complex characteristics intrinsic to SLiMs (i.e., their typical localization in disordered regions or loops, their short but variable length, the varying conservation of their sequence, and their slightly bent structure). | [60] |
Uniprot Acc. #, Gene, Entry Name | SLiM Start | SLiM End | SLiM Sequence | No. of Evidence | Experimental Evidence | Refs |
---|---|---|---|---|---|---|
Q9BYG3, MKI67IP MK67I_HUMAN | 227 231 | 234 238 | LDTPEKTVDSQGPTPVCTPT EKTVDSQGPTPVCTPTFLER | 3 3 | Protein kinase assay; mass spectrometry; mutation analysis | [86,87] |
Q92731, ESR2 ESR2_HUMAN | 5 | 12 | MDIKNSPSSLNSPSSYNCSQ | 3 | Inhibitor; western blotting; mutation analysis; | [88,89] |
Q5JSP0, FGD3 FGD3_HUMAN | 73 77 | 80 84 | GSLKIPNRDSGIDSPSSSVA IPNRDSGIDSPSSSVAGENF | 5 2 | Protein kinase assay; mutation analysis; co-immunoprecipitation; alanine scanning | [90,91] |
Q15797, SMAD1 SMAD1_HUMAN | 199 207 | 206 214 | PNSPGSSSSTYPHSPTSSDP STYPHSPTSSDPGSPFQMPA | 4 4 | Protein kinase assay; radiolabeling; mutation analysis; western blotting | [92] |
Q00613, HSF1 HSF1_HUMAN | 300 | 307 | LVRVKEEPPSPPQSPRVEEA | 2 | Protein kinase assay; mutation analysis; | [93,94] |
P98174, FGD1 FGD1_HUMAN | 280 | 287 | DGEKVPNRDSGIDSISSPSN | 1 | Inhibitor | [95,96] |
P84022, SMAD3 SMAD3_HUMAN | 201 | 208 | QMNHSMDAGSPNLSPNPMSP | 3 | Knock out; mutation analysis; protein kinase assay | [72,97] |
P54252 ATXN3 ATX3_HUMAN | 253 | 260 | ADLRRAIQLSMQGSSRNISQ | 2 | Protein kinase assay; mutation analysis | [98,99] |
P35222, CTNNB1 CTNB1_HUMAN | 30 | 37 | HWQQQSYLDSGIHSGATTTA | 4 | Protein kinase assay; inhibitor | [100,101,102] |
P24864, CCNE1 CCNE1_HUMAN | 373 388 392 | 380 395 399 | EQNRASPLPSGLLTPPQSGKEQ EQNRASPLPSGLLTPPQSGK ASPLPSGLLTPPQSGKKQSS | 4 4 1 | Protein kinase assay; mutation analysis; two-dimensional phosphopeptide mapping | [83] |
P05412, JUN JUN_HUMAN | 236 | 243 | QTVPEMPGETPPLSPIDMES | 1 | Two-dimensional phosphopeptide mapping | [103] |
P04637, TP53 P53_HUMAN | 30 | 37 | KLLPENNVLSPLPSQAMDDL | 2 | Protein kinase assay; mutation analysis | [104] |
P01106, MYC MYC_HUMAN | 55 | 62 | IWKKFELLPTPPLSPSRRSG | 2 | Co-immunoprecipitation; western blotting | [105,106] |
O95863, SNAI1 SNAI1_HUMAN | 93 | 100 | ELTSLSDEDSGKGSQPPSPP | 3 | Co-immunoprecipitation; western blotting; alanine scanning | [107,108] |
O95644, NFATC1 NFAC1_HUMAN | 238 287 | 245 294 | GSPRHSPSTSPRASVTEESW HSPTPSPHGSPRVSVTDDSW | 2 2 | Protein kinase assay; mutation analysis | [109,110] |
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Fasano, C.; Grossi, V.; Forte, G.; Simone, C. Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis. Cells 2022, 11, 3739. https://doi.org/10.3390/cells11233739
Fasano C, Grossi V, Forte G, Simone C. Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis. Cells. 2022; 11(23):3739. https://doi.org/10.3390/cells11233739
Chicago/Turabian StyleFasano, Candida, Valentina Grossi, Giovanna Forte, and Cristiano Simone. 2022. "Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis" Cells 11, no. 23: 3739. https://doi.org/10.3390/cells11233739
APA StyleFasano, C., Grossi, V., Forte, G., & Simone, C. (2022). Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis. Cells, 11(23), 3739. https://doi.org/10.3390/cells11233739