Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address...

Full description

Saved in:
Bibliographic Details
Published in:International journal of molecular sciences 2024-04, Vol.25 (7), p.3698
Main Authors: Gorostiola González, Marina, Rakers, Pepijn R J, Jespers, Willem, IJzerman, Adriaan P, Heitman, Laura H, van Westen, Gerard J P
Format: Article
Language:English
Subjects:
Biomarkers
Cancer
Care and treatment
Comparative analysis
Cross Reactions
Drug Discovery
Forecasts and trends
G proteins
Genomes
Health aspects
Humans
Kinases
Ligands
Lipids
Machine Learning
Membrane Proteins
Mortality
Mutation
Neoplasms - drug therapy
Pertuzumab
Proteins
R&D
Research & development
Simulation
Tyrosine
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25073698