Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

The ability to computationally predict changes in protein thermostability upon mutation is crucial for advancing protein design and engineering, with applications ranging from therapeutics to biocatalysis. This review provides a comprehensive overview of the significant challenges and diverse computational strategies for predicting protein stability and understanding epistatic interactions across protein variants. A primary obstacle to this goal is the scarcity of high-quality, large-scale thermodynamic datasets, which are often biased toward single-point, destabilizing mutations and lack standardized experimental metrics. This limitation directly impacts the performance and generalizability of data-driven methods, from early machine learning approaches to modern deep learning architectures such as ThermoMPNN and protein language models. Physics-based approaches, such as those employing Rosetta and FoldX energy functions, offer valuable insights but are often limited by their reliance on static structures and oversimplified representations of the unfolded state. While molecular dynamics simulations can capture the critical role of protein flexibility and dynamics in thermostabilization, their computational cost restricts their application in high-throughput screening. Accurately predicting the effects of multiple mutations is further complicated by epistasis, where nonadditive interactions can significantly alter stability and function. Overcoming these hurdles requires a synergistic approach, integrating AI-driven predictions with physics-based simulations and accurate conformational sampling methods. Promising future directions include the development of more comprehensive and unbiased datasets, and improved modeling of epistasis and the (un)folded states and their ensembles. Such advancements are essential for enhancing the reliability of thermostability predictions and navigating the complex stability–activity trade-offs inherent in protein optimization and design.

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Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross-disease comparisons can reveal both disease-specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing. In recent years, the growth of multi-omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross-disease analyses. New methodological frameworks have emerged for integrating and comparing disease-specific molecular signatures, from gene-level analyses to complex network-based approaches. Here, we present a comprehensive framework for computational cross-disease comparison and integration of omics data, covering established and emerging methodologies. These include gene-level comparative analyses, pathway-based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross-disease analysis. Finally, we explore current trends in cross-disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field. This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross-disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs.

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Fluid interfaces are fundamental to numerous natural and industrial processes, making their study crucial for both academic and practical purposes. Molecular dynamics (MD) simulations have become an indispensable tool for investigating the structures and molecular-level phenomena occurring at these interfaces. This review explores various computational strategies employed to model fluid interfaces, including classical force fields, quantum mechanical (QM) methods, and neural network potentials. The review begins by discussing the choice of potential energy functions, followed by a discussion of boundary conditions and their importance in simulating systems like the air-water and water–oil interfaces. The review then shifts to comparing nonpolarizable and polarizable force fields, highlighting when electronic polarization becomes necessary for accurately modeling the interface systems. The use of ab initio molecular dynamics (AIMD) is also examined, particularly for its ability to capture electronic effects, albeit with significant computational costs. Finally, we explore the growing role of machine learning, particularly neural network potentials, in simulating complex interface systems. By reviewing key studies on air-water and water–oil interfaces, we summarize the latest advancements in modeling fluid interfaces, with particular attention to chemical reactions near these interfaces. This review provides a concise and approachable overview of the computational approaches that are advancing our understanding of fluid interfaces at the molecular scale.

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Molecular dynamics simulations are widely used across chemistry, physics, and biology, providing quantitative insight into complex processes with atomic detail. However, their limited timescale of a few microseconds is a significant obstacle in describing phenomena such as conformational transitions of biomolecules and polymorphism in molecular crystals. Recently, stochastic resetting, that is, randomly stopping and restarting the simulations, emerged as a powerful enhanced sampling approach, which is collective variable-free, highly parallelized, and easily implemented in existing molecular dynamics codes. Resetting expedites sampling rare events while enabling the inference of kinetic observables of the underlying process. It can be employed as a standalone tool or in combination with other enhanced sampling methods, such as Metadynamics, with each technique compensating for the drawbacks of the other. Here, we comprehensively describe resetting and its theoretical background, review recent developments in stochastic resetting for enhanced sampling, and provide instructive guidelines for practitioners.

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The cover image is basThe cover image is based on the article Dissipative Particle Dynamics Modeling in Polymer Science and Engineering by Sousa Javannikkhah et al., https://doi.org/10.1002/wcms.70018