Fighting COVID-19 Using Molecular Dynamics Simulations

Arantes, Pablo Ricardo; Saha, Aakash; Palermo, Giulia

doi:10.1021/acscentsci.0c01236

Cited by 59 publications

(55 citation statements)

References 14 publications

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“…[30] and Xiong et al . [34] or taken into consideration in several other structural studies [20-24]. As such, we employ the two-state model shown in Figure 2, with one state representing all three RBD domains closed and the second state representing one RBD open.…”

Section: Resultsmentioning

confidence: 99%

“…4, unclear if any additional conformational states other than those with either all three RBD domains in the closed state or only one RBD open state are biologically relevant. Specifically, Yurkovetskiy et al [44] observed an occupancy for states with two or three RBD domains in the open conformation, but these were not observed by Gobeil et al [30] and Xiong et al [34] or taken into consideration in several other structural studies [20][21][22][23][24]. As such, we employ the two-state model shown in Figure 2 [30] and Xiong et al [34].…”

Section: Conformational State Occupanciesmentioning

confidence: 97%

“…Several aspects of the dynamics of the Spike protein are being currently studied, with a range of particular goals: to evaluate the docking of small molecules to the RBD domain [19], to search for alternative target binding-sites for vaccine development [20], to understand residue-residue interactions and their effects on conformational plasticity [21] and to investigate the flexibility of different domains in particular conformational states [22].…”

Section: Introductionmentioning

confidence: 99%

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Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

Teruel

Mailhot

Najmanovich

2020

Preprint

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The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 as part of the viral entry mechanism. We utilise coarse-grained normal-mode analyses to model the dynamics of Spike and calculate transition probabilities between states for 17081 Spike variants. Our results correctly model an increase in open-state occupancy for the more infectious D614G via an increase in flexibility of the closed-state and decrease of flexibility of the open-state. We predict the same effect for several mutations on Glycine residues (404, 416, 504, 252) as well as residues K417, D467 and N501, including the N501Y mutation, explaining the higher infectivity of the B.1.1.7 and 501.V2 strains. This is, to our knowledge, the first use of normal-mode analysis to model conformational state transitions and the effect of mutations thereon. The specific mutations of Spike identified here may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms and guide public health in their surveillance efforts.

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Section: Resultsmentioning

confidence: 99%

Section: Conformational State Occupanciesmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

Teruel

Mailhot

Najmanovich

2020

Preprint

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“…In this sense, evaluate this process from an atomistic point of view can provide important information about the role of these specific residues and the use of in silico strategies to simulate the interaction of proteins is ubiquitous, mainly using molecular docking and molecular dynamics simulations, since the advance of both hardware and software has allowed the study of bigger and larger systems 15 . In the case of the pandemic of the new coronavirus, molecular docking and classical molecular simulation are used in the some works to evaluate different aspects of the virus [16][17][18] . Considering the specific differences in the ACE2, we performed homology modeling, molecular docking and molecular dynamics simulations to observe the behavior of the interaction interface involving the RBD and ACE2 proteins for human, cat, dog, and ferret systems and, this is, as far we know, the first study of this kind.…”

Section: Figurementioning

confidence: 99%

A computational study of the interface interaction between SARS-CoV-2 RBD and ACE2 from human, cat, dog and ferret.

Soté

Franca

Hora

et al. 2021

Preprint

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The total impact of the worldwide COVID-19 pandemic is still emerging, changing all relationships as a result, including those with pet animals. In the infection process, the use of Angiotensin-converting enzyme 2 (ACE2) as a cellular receptor to the spike protein of the new coronavirus is a fundamental step. In this sense, understanding which residue plays what role in the interaction between SARS-CoV-2 spike glycoprotein and ACE2 from cats, dogs, and ferrets is an important guide for helping to choose which animal model can be used to study the pathology of COVID-19 and if there are differences between these interactions and those occurring in the human system. Hence, trying to help to answer these questions, we performed classical molecular dynamics simulations to evaluate, from an atomistic point of view, the interactions in these systems. Our results show that there are significant differences in the interacting residues between the systems from different animal species, and the role of ACE2 key residues are different in each system and can assist in the search for different inhibitors for each animal.

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“…Being the RBD the most variable part of the viral genome [15], several studies have focused on understanding the effects mutations have on the binding affinity to ACE2 [16][17][18]. Atomistic molecular dynamics (MD) simulations have been used since early in the pandemic to study and visualize the binding between the S and the ACE2 receptor and explore potential targets for drugs [19][20][21]. An important limit of these tools is the exceptional computational burden necessary to carry out the simulations to an extent that is meaningful to answer biological questions.…”

Section: Introductionmentioning

confidence: 99%

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

Capponi

Wang

Navarro

et al. 2021

Preprint

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We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy mutations with low binding affinity from mutations with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the the SARS-CoV-2 spike protein and the human receptor ACE2.

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Fighting COVID-19 Using Molecular Dynamics Simulations

Cited by 59 publications

References 14 publications

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

A computational study of the interface interaction between SARS-CoV-2 RBD and ACE2 from human, cat, dog and ferret.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

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