Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders
Abstract
Neuropsychiatric diseases are characterized by complex molecular underpinnings that remain challenging to fully elucidate. Molecular dynamics (MD) simulations have emerged as a powerful computational tool, providing a crucial bridge between static genetic data and the dynamic functional consequences of molecular alterations. This review offers a comprehensive overview of the application of MD simulations in studying the molecular basis of neuropsychiatric disorders. We highlight key applications, including the assessment of mutation pathogenicity in disease-associated proteins, the influence of post-translational modifications on protein function, folding, misfolding, and aggregation, and the characterization of psychopharmacological drug-target interactions at atomic resolution. Through relevant examples from research on psychiatric and neurodegenerative diseases, we illustrate how these computational methods are implemented to gain mechanistic insights. Importantly, this review traces the historical development of MD simulations in biological applications, critically examines the method's limitations, and outlines future perspectives for simulating long-timescale physiological processes, large molecular ensembles, and even whole-cell environments. Ultimately, this work highlights MD simulations as a useful and complementary tool for modern neuropsychiatry research, capable of revealing disease mechanisms and guiding the development of novel therapeutic strategies.
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Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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