Foundations of Biomolecular Simulations: A Critical Introduction to Homology Modeling, Molecular Dynamics Simulations, and Free Energy Calculations of Membrane Proteins

Hénin, Jérôme; Baaden, Marc; Taly, Antoine

doi:10.1007/978-1-4939-0662-8_13

Cited by 1 publication

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“…When the crystal or NMR structure of the key molecule is known, model building follows a standard procedure. Typical approaches and setups are described in [2,3], highlighting specific issues that may arise with membrane proteins. In a typical setup, it is required to choose among potential alternative conformations, correct for mutations that may have been required to achieve crystallization, and possibly predict the protonation states of ionizable amino acids including His, Asp, Glu, Arg and Lys residues.…”

Section: Learning From Molecular Modelsmentioning

confidence: 99%

Computer Simulations Provide Guidance for Molecular Medicine Through Insights on Dynamics and Mechanisms at the Atomic Scale

Baaden

2019

IFMBE Proceedings

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Computer simulations provide crucial insights and rationales for the design of molecular approaches in medicine. Several case studies illustrate how molecular model building and molecular dynamics simulations of complex molecular assemblies such as membrane proteins help in that process. Important aspects relate to build relevant molecular models with and without a crystal structure, to model membrane aggregates, then to link (dynamic) models to function, and finally to understand key disease-triggering phenomena such as aggregation. Through selected examples-including key signaling pathways in neurotransmission-the links between a molecular-level understanding of biological mechanisms and original approaches to treat disease conditions will be illuminated. Such treatments may be symptomatic, e.g. by better understanding the function and pharmacology of macromolecular key players, or curative, e.g. through molecular inhibition of disease-inducing molecular processes.

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Section: Learning From Molecular Modelsmentioning

confidence: 99%