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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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Chen H, Chen TY. From Monomers to Hexamers: A Theoretical Probability of the Neighbor Density Approach to Dissect Protein Oligomerization in Cells. Anal Chem 2024; 96:895-903. [PMID: 38156958 PMCID: PMC10842889 DOI: 10.1021/acs.analchem.3c04728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Deciphering the oligomeric state of proteins within cells is pivotal to understanding their role in intricate cellular processes. With the recent advances in single-molecule localization microscopy, previous efforts have harnessed protein location density approaches, coupled with simulations, to extract membrane protein oligomeric states in cells, highlighting the value of such techniques. However, a comprehensive theoretical approach that can be universally applied across different proteins (e.g., membrane and cytosolic proteins) remains elusive. Here, we introduce the theoretical probability of neighbor density (PND) as a robust tool to discern protein oligomeric states in cellular environments. Utilizing our approach, the theoretical PND was validated against simulated data for both membrane and cytosolic proteins, consistently aligning with experimental baselines for membrane proteins. This congruence was maintained even when adjusting for protein concentrations or exploring proteins of various oligomeric states. The strength of our method lies not only in its precision but also in its adaptability, accommodating diverse cellular protein scenarios without compromising the accuracy. The development and validation of the theoretical PND facilitate accurate protein oligomeric state determination and bolster our understanding of protein-mediated cellular functions.
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Affiliation(s)
- Huanhuan Chen
- Department of Chemistry, University of Houston, Houston, Texas 77204
| | - Tai-Yen Chen
- Department of Chemistry, University of Houston, Houston, Texas 77204
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Šlachtová V, Chovanec M, Rahm M, Vrabel M. Bioorthogonal Chemistry in Cellular Organelles. Top Curr Chem (Cham) 2023; 382:2. [PMID: 38103067 PMCID: PMC10725395 DOI: 10.1007/s41061-023-00446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/12/2023] [Indexed: 12/17/2023]
Abstract
While bioorthogonal reactions are routinely employed in living cells and organisms, their application within individual organelles remains limited. In this review, we highlight diverse examples of bioorthogonal reactions used to investigate the roles of biomolecules and biological processes as well as advanced imaging techniques within cellular organelles. These innovations hold great promise for therapeutic interventions in personalized medicine and precision therapies. We also address existing challenges related to the selectivity and trafficking of subcellular dynamics. Organelle-targeted bioorthogonal reactions have the potential to significantly advance our understanding of cellular organization and function, provide new pathways for basic research and clinical applications, and shape the direction of cell biology and medical research.
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Affiliation(s)
- Veronika Šlachtová
- Department of Bioorganic and Medicinal Chemistry, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic
| | - Marek Chovanec
- Department of Bioorganic and Medicinal Chemistry, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic
- University of Chemistry and Technology, Technická 5, 166 28, Prague 6, Czech Republic
| | - Michal Rahm
- Department of Bioorganic and Medicinal Chemistry, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic
- University of Chemistry and Technology, Technická 5, 166 28, Prague 6, Czech Republic
| | - Milan Vrabel
- Department of Bioorganic and Medicinal Chemistry, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10, Prague 6, Czech Republic.
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