DOI: 10.1002/minf.202300055 ISSN:

A multi‐tier computational screening framework to effectively search the mutational space of SARS‐CoV‐2 receptor binding motif to identify mutants with enhanced ACE2 binding abilities

Sandipan Chakraborty, Chiranjeet Saha
  • Organic Chemistry
  • Computer Science Applications
  • Drug Discovery
  • Molecular Medicine
  • Structural Biology


SARS‐CoV‐2 gained crucial mutations at the receptor binding domain (RBD) that often changed the course of the pandemic leading to new waves with increased case fatality. Variants are observed with enhanced transmission and immune invasion abilities. Thus, predicting future variants with enhanced transmission ability is a problem of utmost research interest. Here, we have developed a multi‐tier exhaustive SARS‐CoV‐2 mutation screening platform combining MM/GBSA, extensive molecular dynamics simulations, and steered molecular dynamics to identify RBD mutants with enhanced ACE2 binding capability. We have identified four RBM mutations (F490K, S494K, G504F, and the P499L) with significantly higher ACE2 binding abilities than wild‐type RBD. Compared to wild‐type RBD, they all form stable complexes with more hydrogen bonds and salt‐bridge interactions with ACE2. Our simulation data suggest that these mutations allosterically alter the packing of the RBM interface of the RBD‐ACE2 complex. As a result, the rupture force required to break the RBD‐ACE2 contacts is significantly higher for these mutants.

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