When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy

Abstract

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein–protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

Many Roles of Carbohydrates: A Computational Spotlight on the Coronavirus S Protein Binding

“… 65 The SARS-CoV-2 variants show different binding energy, with the Delta variant being the strongest among Alpha, Beta, Gamma, and Delta variants when compared without glycans. 66 , 67 …”

“…65 The SARS-CoV-2 variants show different binding energy, with the Delta variant being the strongest among Alpha, Beta, Gamma, and Delta variants when compared without glycans. 66,67 O-Linked Glycans of the S Protein. The N-linked glycan sites and site-specific composition analysis have been studied in detail.…”

Binding affinity estimation from restrained umbrella sampling simulations

“…In the geometrical route, these collective variables are often subjected to restrictions, and the amount of reversible work required to impose each constraint is determined by a sequence of very accurate PMF simulations. The collective variables used here are the RMSDs of the two proteins' backbone distances from the reference, native conformation, the three Euler angles (Θ, Φ and Ψ) that describe Article https://doi.org/10.1038/s43588-022-00389-9 their relative orientation and the polar (θ) and azimuth angles (φ) that describe their relative position 27,81 . The geometrical path consists of a sequence of separate PMF computations performed sequentially with the gradual inclusion of restrictions (RMSD, Θ, Φ, Ψ, θ and φ), as shown in Supplementary Table 2.…”

Achieving Accurate Standard Protein–Protein Binding Free Energy Calculations through the Geometrical Route and Ergodic Sampling

“…Perthold and Oostenbrink put forth a new type of restraint that can effectively reduce the change in configurational entropy upon binding and, thus, help improve the reliability of free energy calculations . A variety of success stories confirm that the methodology, when correctly utilized, is able to reproduce experimental standard protein–protein binding free energies, albeit at the price of costly, microsecond-timescale computational simulationsan investment still considerable by any standard on commodity, general-purpose servers. − …”