The influence of single-point mutation D614G on the binding process between human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein-an atomistic simulation study† † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ra00198a

SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings. The spike aspartic acid-614 to glycine (D614G) substitution in the spike represents a "more transmissible form of SARS-CoV-2” and occurs in all SARS-CoV-2 mutants. However, the underlying mechanism of the D614G substitution in virus infectivity has remained unclear. In this paper, we adopt molecular simulations to study the contact processes of the D614G mutant and wild-type (WT) spikes with hACE2. The interaction areas with hACE2 for the two spikes are completely different by visualizing the whole binding processes. The D614G mutant spike moves towards the hACE2 faster than the WT spike. We have also found that the receptor-binding domain (RBD) and N-terminal domain (NTD) of the D614G mutant extend more outwards than those of the WT spike. By analyzing the distances between the spikes and hACE2, the changes of number of hydrogen bonds and interaction energy, we suggest that the increased infectivity of the D614G mutant is not possibly related to the binding strength, but to the binding velocity and conformational change of the mutant spike. This work reveals the impact of D614G substitution on the infectivity of the SARS-CoV-2, and hopefully could provide a rational explanation of interaction mechanisms for all the SARS-CoV-2 mutants. SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings.

The influence of single-point mutation D614G on the binding process between human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein-an atomistic simulation study.

SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings. The spike aspartic acid-614 to glycine (D614G) substitution in the spike represents a "more transmissible form of SARS-CoV-2" and occurs in all SARS-CoV-2 mutants. However, the underlying mechanism of the D614G substitution in virus infectivity has remained unclear. In this paper, we adopt molecular simulations to study the contact processes of the D614G mutant and wild-type (WT) spikes with hACE2. The interaction areas with hACE2 for the two spikes are completely different by visualizing the whole binding processes. The D614G mutant spike moves towards the hACE2 faster than the WT spike. We have also found that the receptor-binding domain (RBD) and N-terminal domain (NTD) of the D614G mutant extend more outwards than those of the WT spike. By analyzing the distances between the spikes and hACE2, the changes of number of hydrogen bonds and interaction energy, we suggest that the increased infectivity of the D614G mutant is not possibly related to the binding strength, but to the binding velocity and conformational change of the mutant spike. This work reveals the impact of D614G substitution on the infectivity of the SARS-CoV-2, and hopefully could provide a rational explanation of interaction mechanisms for all the SARS-CoV-2 mutants.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.