Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	5.6	9.3	2011	16.6 Days	CHF 2700	Submit
Biophysica biophysica	1.8	2.6	2021	19.1 Days	CHF 1200	Submit
International Journal of Molecular Sciences ijms	5.6	10.0	2000	17.5 Days	CHF 2900	Submit
Life life	3.9	7.1	2011	15.3 Days	CHF 2600	Submit
Pharmaceutics pharmaceutics	6.9	12.5	2009	16.3 Days	CHF 2900	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Biomolecules Biophysica Pharmaceutics IJMS Life

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

attachment

Supplementary material:
Supplementary File 1 (ZIP, 3068 KB)

18 pages, 5223 KB  

Open AccessArticle

gCoSRNA: Generalizable Coaxial Stacking Prediction for RNA Junctions Using Secondary Structure

by Shasha Li, Qianqian Xu, Ya-Lan Tan, Jian Jiang, Bengong Zhang and Ya-Zhou Shi

Biomolecules 2026, 16(2), 230; https://doi.org/10.3390/biom16020230 - 2 Feb 2026

Cited by 1 | Viewed by 924

Abstract

Coaxial stacking between adjacent stems is a key tertiary interaction that defines the spatial organization of RNA junctions, which are core structural motifs in folded RNAs. The accurate prediction of coaxial stacking is critical for RNA 3D structure modeling, yet existing computational tools [...] Read more.

Coaxial stacking between adjacent stems is a key tertiary interaction that defines the spatial organization of RNA junctions, which are core structural motifs in folded RNAs. The accurate prediction of coaxial stacking is critical for RNA 3D structure modeling, yet existing computational tools remain limited, especially for junctions with variable numbers of branches or complex topologies. Here, we present gCoSRNA, a generalizable computational framework for predicting coaxial stacking configurations using RNA sequence and secondary structure as input. Instead of developing separate models for each junction type, gCoSRNA decomposes multi-way junctions into all possible adjacent stem pairs, termed pseudo two-way junctions, and uses a unified RF classifier to evaluate stacking probabilities. Global stacking configurations are inferred by integrating these pairwise predictions, eliminating the need for explicit junction type classification. Benchmarking on two independent test sets (297 RNA junctions), including CASP15/16 and RNA-Puzzles targets, shows that gCoSRNA achieves consistently high accuracy (mean ~0.89) across junctions with two to seven branches, outperforming existing junction-specific methods. These results highlight the model’s ability to capture higher-order structural features and its potential utility in RNA tertiary structure prediction pipelines. Full article

(This article belongs to the Topic Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges)

►▼ Show Figures

Figure 1

attachment

Supplementary material:
Supplementary File 1 (ZIP, 109738 KB)

15 pages, 2560 KB  

Open AccessArticle

Coupling Molecular and Cellular Dynamics in a Large-Scale Monte Carlo Simulation

by Jonah Chaiken, Amit Ifrach, Julia Sajman and Eilon Sherman

Int. J. Mol. Sci. 2025, 26(21), 10763; https://doi.org/10.3390/ijms262110763 - 5 Nov 2025

Viewed by 1217

Abstract

Cells change their shape to survive, proliferate, and function. Such changes are both driven by stochastic molecular interactions and affect them in return. Recent Monte-Carlo simulations, such as MCell4, can explicitly capture the interactions of millions of molecules, yet cannot dynamically couple these [...] Read more.

Cells change their shape to survive, proliferate, and function. Such changes are both driven by stochastic molecular interactions and affect them in return. Recent Monte-Carlo simulations, such as MCell4, can explicitly capture the interactions of millions of molecules, yet cannot dynamically couple these interactions with changes in morphology. Here, we extend the MCell4 simulation platform by incorporating physical forces that allow bidirectional feedback between dynamic molecular interactions and outer or intracellular membranes. We start with some simple examples such as a moving piston and a fluctuating membrane. We then simulate the spreading of T cells on antigen-presenting cells or an activating surface due to cognate interactions of surface molecules, such as receptors and their ligands or integrins. The coupled simulation quantitatively accounts for the expected correlation of molecular interactions and the spreading dynamics of the cell surface. Thus, our approach provides a versatile foundation for simulating a variety of dynamic cell systems and processes. Full article

(This article belongs to the Topic Physics-Based Computational Approaches for Soft Matter and Biophysics Challenges)

►▼ Show Figures

Figure 1

attachment

Supplementary material:
Supplementary File 1 (ZIP, 3411 KB)

16 pages, 4740 KB  

Open AccessArticle

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

Cited by 1 | Viewed by 2638

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)

►▼ Show Figures

Figure 1

attachment

Supplementary material:
Supplementary File 1 (ZIP, 320 KB)

28 pages, 9484 KB  

Open AccessReview

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 11 | Viewed by 4421

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)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-4