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An in silico LLPS perturbation approach in the design of a novel SARS-CoV-2 spike receptor-binding domain inhibitor

ABSTRACT: The reversible process where a homogenous fluid de-mixes into two distinctively separate liquid phases is referred to as LLPS (Liquid-liquid phase separation). The resulting liquid is made up of one dilute phase and one condensed phase. An increasing number of studies have shown that the liquid-liquid phase separation is an important principle that underlies intracellular organization in biological systems, forming liquid condensates without a membrane envelope, otherwise known as MLOs (membraneless organelles). Such organelles include the P bodies, nucleolus and stress granules. Moreover, the regulation of many other biological processes such as signal transduction, chromatin rearrangement and RNA metabolism have been linked to the liquid-liquid phase separation.

SUBMITTER: Durojaye O

PROVIDER: S-EPMC7685779 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Binder design for targeting SARS-CoV-2 spike protein: An in silico perspective.

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| S-EPMC8616691 | biostudies-literature

Evolutionary remodelling of N-terminal domain loops fine-tunes SARS-CoV-2 spike.

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Project description: Not available

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Drug repurposing for identification of potential inhibitors against SARS-CoV-2 spike receptor-binding domain: An <i>in silico</i> approach.

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In-silico design of a potential inhibitor of SARS-CoV-2 S protein.

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Project description:The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

| S-EPMC8370119 | biostudies-literature

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