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Journal ArticleDOI

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor.

TL;DR: In this article, all-atom molecular dynamics simulations were used to investigate the role of thermal fluctuations in structure of SARS-CoV-2 and showed that the difference in binding affinity between CoV and SARS CoV was not explained from X-ray structures.
Abstract: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV-2's spike protein stronger than SARS-CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X-ray structures. To go beyond insights gained from X-ray structures and investigate the role of thermal fluctuations in structure, we employ all-atom molecular dynamics simulations. Microseconds-long simulations reveal that while CoV and CoV-2 spike-ACE2 interfaces have similar conformational binding modes, CoV-2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non-interfacial residues. These results recommend that ongoing efforts to design spike-ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the Spike receptor binding domain (S-RBD) from SARS-CoV-2, a crucial protein for the entrance of the virus into target cells is known to cause infection by binding to a cell surface protein.

61 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used molecular dynamics simulations, machine learning, and free energy perturbation (FEP) calculations to elucidate the differences in binding by the two SARS-CoV-2 viruses, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but slightly decreased by K417N.
Abstract: SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

37 citations

Journal ArticleDOI
TL;DR: A novel sarbecovirus (RhGB01) is identified and sequenced from a British horseshoe bat, at the western extreme of the rhinolophid range, which presents opportunity for SARS-CoV-2 and other sarBecovirus homologous recombination.
Abstract: The source of the COVID-19 pandemic is unknown, but the natural host of the progenitor sarbecovirus is thought to be Asian horseshoe (rhinolophid) bats We identified and sequenced a novel sarbecovirus (RhGB01) from a British horseshoe bat, at the western extreme of the rhinolophid range Our results extend both the geographic and species ranges of sarbecoviruses and suggest their presence throughout the horseshoe bat distribution Within the spike protein receptor binding domain, but excluding the receptor binding motif, RhGB01 has a 77% (SARS-CoV-2) and 81% (SARS-CoV) amino acid homology While apparently lacking hACE2 binding ability, and hence unlikely to be zoonotic without mutation, RhGB01 presents opportunity for SARS-CoV-2 and other sarbecovirus homologous recombination Our findings highlight that the natural distribution of sarbecoviruses and opportunities for recombination through intermediate host co-infection are underestimated Preventing transmission of SARS-CoV-2 to bats is critical with the current global mass vaccination campaign against this virus

25 citations

Journal ArticleDOI
TL;DR: In this paper , an immunosensor was used to detect spike proteins from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and showed a sensitivity of 0.83 μA∗mL/μg and a limit of detection of 22.5 ng/mL of spike protein.

19 citations

Journal ArticleDOI
TL;DR: In this article, the authors analyzed the receptor-binding domain (RBD) of six coronavirus' S proteins and found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses.
Abstract: COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus' S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an "anchor-locker" recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.

17 citations

References
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Journal Article
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

47,974 citations

Journal ArticleDOI
TL;DR: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms.
Abstract: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented. The method is based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms. Timings and accuracies are presented for three large crystalline ionic systems.

24,332 citations

Journal ArticleDOI
TL;DR: The dynamical steady-state probability density is found in an extended phase space with variables x, p/sub x/, V, epsilon-dot, and zeta, where the x are reduced distances and the two variables epsilus-dot andZeta act as thermodynamic friction coefficients.
Abstract: Nos\'e has modified Newtonian dynamics so as to reproduce both the canonical and the isothermal-isobaric probability densities in the phase space of an N-body system. He did this by scaling time (with s) and distance (with ${V}^{1/D}$ in D dimensions) through Lagrangian equations of motion. The dynamical equations describe the evolution of these two scaling variables and their two conjugate momenta ${p}_{s}$ and ${p}_{v}$. Here we develop a slightly different set of equations, free of time scaling. We find the dynamical steady-state probability density in an extended phase space with variables x, ${p}_{x}$, V, \ensuremath{\epsilon}\ifmmode \dot{}\else \.{}\fi{}, and \ensuremath{\zeta}, where the x are reduced distances and the two variables \ensuremath{\epsilon}\ifmmode \dot{}\else \.{}\fi{} and \ensuremath{\zeta} act as thermodynamic friction coefficients. We find that these friction coefficients have Gaussian distributions. From the distributions the extent of small-system non-Newtonian behavior can be estimated. We illustrate the dynamical equations by considering their application to the simplest possible case, a one-dimensional classical harmonic oscillator.

17,939 citations

Journal ArticleDOI
TL;DR: GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules, and provides a rich set of calculation types.

12,985 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a new molecular dynamics algorithm for sampling the canonical distribution, where the velocities of all the particles are rescaled by a properly chosen random factor.
Abstract: The authors present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.

11,327 citations