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Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.

ABSTRACT: Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

SUBMITTER: Chen L

PROVIDER: S-EPMC8603309 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The epidermal growth factor receptor (EGFR) pathway is one of the most dysregulated molecular pathways in human cancers. Despite its well-established importance in tumor growth, progression and drug-resistant phenotype over the past several decades, targeted therapy designed to circumvent EGFR has yielded only modest clinical success in cancer patients, except those with non-small cell lung cancer (NSCLC) carrying EGFR activation mutations. However, almost all of these NSCLC patients eventually developed resistance to small molecule EGFR kinase inhibitors. These disappointing outcomes are, in part, due to the high complexity and the interactive nature of the EGFR signaling network. More recent compelling evidence further indicates that EGFR functionality can be dependent on its subcellular location. In this regard, EGFR undergoes translocation into different organelles where it elicits distinctly different functions than its best known activity as a plasma membrane-bound receptor tyrosine kinase. EGFR can be shuttled into the cell nucleus and mitochondrion upon ligand binding, radiation, EGFR-targeted therapy and other stimuli. Nuclear EGFR behaves as transcriptional regulator, tyrosine kinase, and mediator of other physiological processes. The role of mitochondrial EGFR remains poorly understood but it appears to regulate apoptosis and autophagy. While studies using patient tumors have shown nuclear EGFR to be an indicator for poor clinical outcomes in cancer patients, the impact of mitochondrial EGFR on tumor behavior and patient prognosis remains to be defined. Most recently, several lines of evidence suggest that mislocated EGFR may regulate tumor response to therapy and that plasma membrane-bound EGFR elicits survival signals independent of its kinase activity. In light of these recent progresses and discoveries, we will outline in this minireview an emerging line of research that uncovers and functionally characterizes several novel modes of EGFR signaling that take center stage in the cell nucleus, mitochondrion and other subcellular compartments. We will also discuss the clinical implications of these findings in the rationale design for therapeutic strategy that overcomes tumor drug resistance.

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Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.

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