Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Abstract

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Keywords: CADD; COVID-19; SARS-CoV-2; SBDD; docking; homology modeling; molecular dynamics; pharmacophore; protein–ligand interaction fingerprints; rational drug design.

Figure 1

(A) Crystal structure of spike RBD (teal) in complex with hACE2 (pink), deposited in the Protein Data Bank with accession code 6M0J. (B) Close-up view of interface contacts between the spike RBD and hACE2: hydrogen bonds are represented as black dashed lines.

Figure 2

(A) Crystal structure of SARS-CoV-2 M^pro (PDB ID: 6Y2E): the first protomer is colored in salmon, while the second protomer is colored in pink, and the active site position is highlighted with a blue circle. (B) Close-up view of the catalytic site: the H41-C145 dyad is highlighted, alongside the conserved water molecule that substitutes the third member of the canonical catalytic triad diffused in several cysteine proteases.

Figure 3

(A) Three-dimensional depiction of Nirmatrelvir orientation within the catalytic site of SARS-CoV-2 M^pro (PDB ID: 7RFW). (B) Bidimensional representation of intermolecular interactions of Nirmatrelvir–SARS-CoV-2 M^pro 7RFW complex.

Figure 4

Workflow of a Thermal Titration Molecular Dynamics (TTMD) simulation. The time-dependent conservation of the native binding mode within a protein–ligand complex of interest is monitored with a scoring function based on interaction fingerprint through a series of short molecular dynamics simulations performed at progressively increasing temperatures. The simulation is carried out until the target temperature is reached or the dissociation process is completed. A coefficient called MS is then calculated and used to rank ligands based on the persistence of their native binding mode.

Figure 5

Workflow of a Supervised Molecular Dynamics (SuMD) simulation. The ligand is dynamically docked within a user-defined binding site through a series of short, unbiased molecular dynamics simulations. At the end of each step, the distance of mass between the ligand and the receptor binding site is computed for each trajectory frame and is fed to a tabu-like algorithm. If the slope of the straight line that interpolates the data is negative, indicating the ligand is approaching the binding site, the step is retained, and the simulation continues with the next “SuMD-step”. If not, the step is discarded and repeated, randomly reassigning particles’ velocities through the Langevin thermostat. This cycle is repeated until a threshold distance is reached or other user-defined termination criteria are met.