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Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges
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Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan 430079, China
Dr. Wang Jian
Department of Pharmacology, Penn State College of Medicine, Hershey, PA 17033, USA
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Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges

Abstract submission deadline
30 September 2026
Manuscript submission deadline
3 January 2027
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3436

Topic Information

Dear Colleagues,

Rapid advancements in computational methods have profoundly transformed research on soft matter and biophysics. These groundbreaking methods provide innovative solutions to accelerate our understanding and optimization of complex systems. They encompass various technologies, from molecular modeling and simulations to artificial intelligence and machine learning algorithms, each playing a crucial role in addressing multiple soft matter and biophysics challenges. In recent years, physics-based computational methods have surged in areas such as predicting the structures of proteins, RNA, drug molecules, and their complexes, identifying new targets, and optimizing molecular interactions. Computational methods offer efficient alternatives and significantly enhance our understanding of complex biological systems. They enable the efficient screening of large chemical libraries, thereby reducing the time and cost associated with traditional experimental approaches.

This Topic aims to compile the latest research and reviews on physics-based computational strategies pertaining to soft matter and biophysics. We invite submissions showcasing innovative computational techniques, including but not limited to computer-aided design, molecular docking and scoring, virtual screening, and the application of various machine learning approaches in soft matter and biophysics research. By highlighting these advancements, we aim to elucidate the transformative potential of computational methods in developing novel and practical solutions.

We welcome submissions discussing the challenges and opportunities in this rapidly evolving field, particularly those interdisciplinary studies that combine computational methods with experimental validation. This Topic will provide an up-to-date overview of physics-based computational approaches in soft matter and biophysics for future research directions.

Prof. Dr. Yunjie Zhao
Dr. Jian Wang
Topic Editors

Keywords

  • computational modeling
  • DNA/RNA and protein
  • drug discovery and design
  • soft matter and biophysics
  • channels and membranes
  • biomedical data analysis
  • artificial intelligence
  • molecule docking and scoring

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biomolecules
biomolecules 		4.8 9.4 2011 18.4 Days CHF 2700 Submit
Biophysica
biophysica 		- 1.6 2021 16.1 Days CHF 1000 Submit
Pharmaceutics
pharmaceutics 		4.9 7.9 2009 15.5 Days CHF 2900 Submit
International Journal of Molecular Sciences
ijms 		4.9 8.1 2000 16.8 Days CHF 2900 Submit
Life
life 		3.2 4.3 2011 17.8 Days CHF 2600 Submit

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Published Papers (2 papers)

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16 pages, 4740 KiB  
Article
Molecular Dynamics of Apolipoprotein Genotypes APOE4 and SNARE Family Proteins and Their Impact on Alzheimer’s Disease
by Yuqing Wang, Xuefeng Liu, Pengtao Zheng, Qing Xie, Chenxiang Wang and Chaoyang Pang
Life 2025, 15(2), 223; https://doi.org/10.3390/life15020223 - 2 Feb 2025
Viewed by 807
Abstract
Alzheimer’s disease is a chronic neurodegenerative disorder characterized by progressive memory loss and a significant impact on quality of life. The APOE ε4 allele is a major genetic contributor to AD pathogenesis, with synaptic dysfunction being a central hallmark in its pathophysiology. While [...] Read more.
Alzheimer’s disease is a chronic neurodegenerative disorder characterized by progressive memory loss and a significant impact on quality of life. The APOE ε4 allele is a major genetic contributor to AD pathogenesis, with synaptic dysfunction being a central hallmark in its pathophysiology. While the role of APOE4 in reducing SNARE protein levels has been established, the underlying molecular mechanisms of this interaction remain obscure. Our research employs molecular dynamics simulations to analyze interactions between APOE4 and APOE3 isoforms and the synaptic proteins VAMP2, SNAP25, and SYNTAXIN1, which play crucial roles in the presynaptic membrane. Our findings reveal that APOE4 significantly destabilizes the SNARE complex, suppresses its structural dynamics, and reduces hydrogen bonding, consequently partially hindering neurotransmitter release—a very likely discovery for elucidating synaptic dysfunction in Alzheimer’s disease. We identified that APOE4 exhibits a diminished affinity for the SNARE complex in comparison to APOE3. This observation suggests that APOE4 may play a role in modulating the stability of the SNARE complex, potentially impacting the progression and occurrence of Alzheimer’s disease through free energy analysis. This work highlights the perturbations in synaptic function mediated by APOE4, which may offer novel insights into the molecular underpinnings of AD. By elucidating the molecular interplay between APOE4 and the SNARE complex, our study not only enhances our comprehension of AD’s synaptic pathology but also paves the way for devising innovative therapeutic interventions, such as targeting the APOE4–SNARE complex interaction or to restore neurotransmitter release. Full article
(This article belongs to the Topic Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges)
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28 pages, 9484 KiB  
Review
Advances and Challenges in Scoring Functions for RNA–Protein Complex Structure Prediction
by Chengwei Zeng, Chen Zhuo, Jiaming Gao, Haoquan Liu and Yunjie Zhao
Biomolecules 2024, 14(10), 1245; https://doi.org/10.3390/biom14101245 - 1 Oct 2024
Cited by 2 | Viewed by 1641
Abstract
RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to [...] Read more.
RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA–protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA–protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA–protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA–protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA–protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field. Full article
(This article belongs to the Topic Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges)
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