Protein subcellular localization prediction

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Protein subcellular localization prediction (or just protein localization prediction) involves the prediction of where a protein resides in a cell, its subcellular localization.

Contents

In general, prediction tools take as input information about a protein, such as a protein sequence of amino acids, and produce a predicted location within the cell as output, such as the nucleus, Endoplasmic reticulum, Golgi apparatus, extracellular space, or other organelles. The aim is to build tools that can accurately predict the outcome of protein targeting in cells.

Prediction of protein subcellular localization is an important component of bioinformatics based prediction of protein function and genome annotation, and it can aid the identification of drug targets.

Background

Experimentally determining the subcellular localization of a protein can be a laborious and time consuming task. Immunolabeling or tagging (such as with a green fluorescent protein) to view localization using fluorescence microscope are often used. A high throughput alternative is to use prediction.

Through the development of new approaches in computer science, coupled with an increased dataset of proteins of known localization, computational tools can now provide fast and accurate localization predictions for many organisms. This has resulted in subcellular localization prediction becoming one of the challenges being successfully aided by bioinformatics, and machine learning.

Many prediction methods now exceed the accuracy of some high-throughput laboratory methods for the identification of protein subcellular localization. [1] [2] [3] Particularly, some predictors have been developed [4] that can be used to deal with proteins that may simultaneously exist, or move between, two or more different subcellular locations. Experimental validation is typically required to confirm the predicted localizations.

Tools

In 1999 PSORT was the first published program to predict subcellular localization. [5] Subsequent tools and websites have been released using techniques such as artificial neural networks, support vector machine and protein motifs. Predictors can be specialized for proteins in different organisms. Some are specialized for eukaryotic proteins, [6] some for human proteins, [7] and some for plant proteins. [8] Methods for the prediction of bacterial localization predictors, and their accuracy, have been reviewed. [9] In 2021, SCLpred-MEM, a membrane protein prediction tool powered by artificial neural networks was published. [10] SCLpred-EMS is another tool powered by Artificial neural networks that classify proteins into endomembrane system and secretory pathway (EMS) versus all others. [11] Similarly, Light-Attention uses machine learning methods to predict ten different common subcellular locations. [12]

The first model to generalize protein subcellular localization to all cell line does so by leveraging images of subcellular landmark stains (i.e., nuclear, plasma membrane, and endoplasmic reticulum markers) across multiple cell stains. Coupling multimodal data of landmark stains along with a pre-trained protein language model, the Prediction of Unseen Proteins' Subcellular Localization (PUPS) model is capable of generative subcellular localization prediction of any protein in any cell line given the protein's amino acid sequence and reference stains of the cell line. [13]

The development of protein subcellular location prediction has been summarized in two comprehensive review articles. [14] [15] Recent tools and an experience report can be found in a recent paper by Meinken and Min (2012).

Application

Knowledge of the subcellular localization of a protein can significantly improve target identification during the drug discovery process. For example, secreted proteins and plasma membrane proteins are easily accessible by drug molecules due to their localization in the extracellular space or on the cell surface.

Bacterial cell surface and secreted proteins are also of interest for their potential as vaccine candidates or as diagnostic targets. Aberrant subcellular localization of proteins has been observed in the cells of several diseases, such as cancer and Alzheimer's disease. Secreted proteins from some archaea that can survive in unusual environments have industrially important applications.

By using prediction a high number of proteins can be assessed in order to find candidates that are trafficked to the desired location.

Databases

The results of subcellular localization prediction can be stored in databases. Examples include the multi-species database Compartments, FunSecKB2, a fungal database; [16] PlantSecKB, a plant database; [17] MetazSecKB, an animal and human database; [18] and ProtSecKB, a protist database. [19]

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PSORT is a bioinformatics tool used for the prediction of protein localisation sites in cells. It receives the information of an amino acid sequence and its taxon of origin as inputs. Then it analyses the input sequence by applying the stored rules for various sequence features of known protein sorting signals. Finally, it reports the possibility for the input protein to be localised at each candidate site with additional information.

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References

  1. ↑ Kaleel, M; Zheng, Y; Chen, J; Feng, X; Simpson, JC; Pollastri, G; Mooney, C (6 March 2020). "SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks". Bioinformatics. 36 (11): 3343–3349. doi:10.1093/bioinformatics/btaa156. hdl: 10197/12182 . PMID   32142105.
  2. ↑ Rey S, Gardy JL, Brinkman FS (2005). "Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria". BMC Genomics. 6: 162. doi: 10.1186/1471-2164-6-162 . PMC   1314894 . PMID   16288665.
  3. ↑ Kaleel, Manaz; Ellinger, Liam; Lalor, Clodagh; Pollastri, Gianluca; Mooney, Catherine (2021). "SCLpred-MEM: Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks". Proteins: Structure, Function, and Bioinformatics. 89 (10): 1233–1239. doi: 10.1002/prot.26144 . hdl: 2346/90320 . PMID   33983651. S2CID   234484678.
  4. ↑ Chou KC, Shen HB (2008). "Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms". Nature Protocols. 3 (2): 153–62. doi:10.1038/nprot.2007.494. PMID   18274516. S2CID   226104.
  5. ↑ "Protein Subcellular Localization Prediction". www.ncbi.nlm.nih.gov. Retrieved 2016-12-31.
  6. ↑ Chou KC, Wu ZC, Xiao X (2011). "iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins". PLOS ONE. 6 (3): e18258. Bibcode:2011PLoSO...618258C. doi: 10.1371/journal.pone.0018258 . PMC   3068162 . PMID   21483473.
  7. ↑ Shen HB, Chou KC (Nov 2009). "A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0". Analytical Biochemistry. 394 (2): 269–74. doi:10.1016/j.ab.2009.07.046. PMID   19651102.
  8. ↑ Chou KC, Shen HB (2010). "Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization". PLOS ONE. 5 (6): e11335. Bibcode:2010PLoSO...511335C. doi: 10.1371/journal.pone.0011335 . PMC   2893129 . PMID   20596258.
  9. ↑ Gardy JL, Brinkman FS (Oct 2006). "Methods for predicting bacterial protein subcellular localization". Nature Reviews. Microbiology. 4 (10): 741–51. doi:10.1038/nrmicro1494. PMID   16964270. S2CID   62781755.
  10. ↑ Kaleel, Manaz; Ellinger, Liam; Lalor, Clodagh; Pollastri, Gianluca; Mooney, Catherine (2021). "SCLpred-MEM: Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks". Proteins: Structure, Function, and Bioinformatics. 89 (10): 1233–1239. doi: 10.1002/prot.26144 . hdl: 2346/90320 . PMID   33983651. S2CID   234484678.
  11. ↑ Kaleel, Manaz; Zheng, Yandan; Chen, Jialiang; Feng, Xuanming; Simpson, Jeremy C; Pollastri, Gianluca; Mooney, Catherine (1 June 2020). "SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks". Bioinformatics. 36 (11): 3343–3349. doi:10.1093/bioinformatics/btaa156. hdl: 10197/12182 . PMID   32142105.
  12. ↑ Rost, Stark; Heinzinger, Dallago (26 April 2021). "Light Attention Predicts Protein Location from the Language of Life". Biorxiv. doi:10.1101/2021.04.25.441334. S2CID   233449747.
  13. ↑ Zhang, Xinyi; Tseo, Yitong; Bai, Yunhao; Chen, Fei; Uhler, Caroline (25 July 2024). "Prediction of protein subcellular localization in single cells". Biorxiv. doi:10.1101/2024.07.25.605178.
  14. ↑ Nakai, K. Protein sorting signals and prediction of subcellular localization. Adv. Protein Chem., 2000, 54, 277-344.
  15. ↑ Chou, K. C.; Shen, H. B. Review: Recent progresses in protein subcellular location prediction" Anal. Biochem 2007, 370, 1-16.
  16. ↑ "FunSecKB2 (The Fungal Secretome and Subcellular Proteome KnowledgeBase 2.1)". bioinformatics.ysu.edu. Archived from the original on 2016-04-10. Retrieved 2017-09-17.
  17. ↑ "PlantSecKB (The Plant Secretome and Subcellular Proteome KnowledgeBase)". bioinformatics.ysu.edu. Archived from the original on 2016-04-06. Retrieved 2017-09-17.
  18. ↑ "MetazSecKB (The Metazoa (Human & Animal) Protein Subcelluar Location, Secretome and Subcellular Proteome Database)". bioinformatics.ysu.edu. Archived from the original on 2016-04-06. Retrieved 2017-09-17.
  19. ↑ "ProtSecKB (The Protist Secretome and Subcellular Proteome KnowledgeBase)". proteomics.ysu.edu. Retrieved 2017-09-17.

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