Biophysics of Exposome: Molecular Mechanisms of Action
A special issue of Biology (ISSN 2079-7737).
Deadline for manuscript submissions: 31 August 2026 | Viewed by 40
Special Issue Editors
2. College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
Interests: protein structure; dynamics; function and interactions
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The exposome is a term used to describe the sum of all the environmental exposures that an individual experiences throughout their lifetime, including during prenatal stages. It encompasses a wide range of factors, including physical, chemical, and biological exposures, such as infections, air pollution, tobacco smoke, ultraviolet radiation, pesticides, heavy metals, asbestos, industrial solvents, endocrine-disrupting chemicals and allergens. It comprises not only passive exposures but also active exposures, such as those from specific environments or behaviors. The exposome has been linked to many adverse health outcomes because the molecular mixtures that are involved in the exposome are extremely complex, often involving thousands of molecules. When analyzing the biological effects of the individual constituents in isolation and in combination, antagonistic, synergistic and additive effects are observed. This complexity applies not only to pollutants but also to food and medicinal plant sources. Even the human body itself is an expression of the heterogeneity of its composition. To assist with understanding this complexity, we need to predict the biological effects that each of these molecules has. Biological effects are usually initiated by the interaction of a given exposome molecule with the wealth of biomolecular targets. This Special Issue focuses on the initiation of biological functions by the interaction of these molecules with proteins as the biomolecular targets. We invite papers on all relevant aspects to the exposome research, from identification of complex mixtures to large scale prediction of their effects, to prioritizing the study of specific molecules and experimental testing of their biological effects. We invite reviews and original contributions aimed at delineating these interconnections. Given the interdisciplinary nature of this topic, submissions may also be focused on areas of research and/or methodology that may be applicable to address the topic, even when it has not yet been applied to the exposome explicitly.
Specific Topics
- Protein–ligand interactions for environmental molecules;
- Computational docking and screening of exposome constituents;
- Molecular initiating events in toxicology (AOP framework);
- Binding kinetics and structural biology studies of exposome–protein interactions;
- Predictive modeling of molecular effects (QSAR, AI/ML models);
- Network toxicology: linking molecular targets to pathways and disease ;
- Multi-omics integration (genomics, proteomics, metabolomics, epigenomics) for exposome research;
- Big data approaches for large-scale exposome analysis;
- Exposome–disease associations: cardiovascular, respiratory, neurological, metabolic, cancer;
- Cumulative and combined effects of multiple exposures;
- Life-course exposome research (prenatal to aging);
- In vitro and in vivo models for studying complex mixtures;
- High-throughput screening for toxicological profiling;
- Prioritization strategies for experimental validation of predicted effects;
- Statistical and bioinformatics methods for mixture analysis;
- Causal inference in exposome studies;
- Standards for reproducibility and FAIR data principles;
- State-of-the-art reviews of exposome research in specific domains;
- Methodological challenges and opportunities.
Prof. Dr. Judith Klein-Seetharaman
Dr. Madhavi K. Ganapathiraju
Guest Editors
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Keywords
- computational biology
- systems biology
- mass spectrometry
- protein–ligand networks
- protein–protein interaction networks
- metabolomics
- proteomics
- surface plasmon resonance
- protein structure prediction
- molecular docking
- target fishing
- machine learning
- artificial intelligence
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