Molecular Crowding by Computational Approaches
Abstract
Molecular crowding plays a crucial role in biological and medicinal systems, impacting the structure, behavior, and function of biomolecules within the densely packed environments of cells. This chapter provides an overview of the implications of molecular crowding, exploring how the high concentration of macromolecules such as proteins, nucleic acids, and other biological entities impacts biochemical reactions and cellular processes. The discussion highlights the challenges associated with experimental studies of molecular crowding, including challenges in creating accurate in vitro models, controlling concentrations, and isolating crowding effects from other interactions. To address these challenges, the chapter emphasizes the importance of computational techniques. Various computational approaches, including molecular dynamics simulations, Monte Carlo simulations, Brownian dynamics, lattice-models, finite element analysis, coarse-grained modeling, quantum mechanics/molecular mechanics simulations, and multi-scale modeling, are discussed in detail. Each of these techniques contributes unique insights into the molecular-level impacts of crowding, enhancing our understanding of biophysical processes critical for therapeutic development and biological function. The chapter discusses also quantum computing, machine learning and classical simulations hybrid approaches for future directions around molecular crowding studies.
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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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