Computational study of the binding orientation and affinity of noncovalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-1 considering the protein flexibility by using molecular dynamics and cross-docking

Abstract

The papain-like protease (PLpro) from zoonotic coronaviruses (CoVs) has been identified as a target with an essential role in viral respiratory diseases caused by Severe Acute Respiratory Syndrome-associated coronaviruses (SARS-CoVs). The design of PLpro inhibitors has been proposed as an alternative to developing potential drugs against this disease. In this work, 67 naphthalene-derived compounds as noncovalent PLpro inhibitors were studied using molecular modeling methods. Structural characteristics of the bioactive conformations of these inhibitors and their interactions at the SARS-CoV-1 PLpro binding site were reported here in detail, taking into account the flexibility of the protein residues. Firstly, a molecular docking protocol was used to obtain the orientations of the inhibitors. After this, the orientations were compared, and the recurrent interactions between the PLpro residues and ligand chemical groups were described (with LigRMSD and interaction fingerprints methods). In addition, efforts were made to find correlations between docking energy values and experimentally determined binding affinities. For this, the PLpro was sampled by using Gaussian Accelerated Molecular Dynamics (GaMD), generating multiple conformations of the binding site. Diverse protein conformations were selected and a cross-docking experiment was performed, yielding models of the 67 naphthalene-derived compounds adopting different binding modes. Representative complexes for each ligand were selected to obtain the highest correlation between docking energies and activities. A good correlation (R2 = 0.948) was found when this flexible docking protocol was performed.

Modeling of noncovalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 considering the protein flexibility by using molecular dynamics and cross-docking

“…It is pertinent to compare the model obtained in this work for SARS-CoV-2 PLpro inhibitors with the model recently reported for naphthalene derivatives as SARS-CoV-1 PLpro inhibitors (Castillo-Campos et al, 2023). Both works were approached with the same methodology, with the only difference being that, in the current study, we decided to use shorter GaMD simulations to demonstrate the feasibility of applying the method with a brief sampling, which is particularly suitable when correlating a large number of compounds.…”

“…Both modes together were employed to get high-quality solutions, although priority was given to the more rigorous solutions in XP mode ( Friesner et al, 2006 ). The method was employed with parameters similar to those used in our previous study of naphthalene-derived inhibitors ( Castillo-Campos et al, 2023 ). In the process of selecting the optimal solution for each ligand, we considered the ten best poses based on the most negative docking scoring energy values, coupled with a thorough comparison between these poses and those observed in the reference PDBs.…”

“…In a recent study, a computational analysis was conducted, successfully establishing a correlation between the activity of 67 non-covalent SARS-CoV-1 PLpro inhibitors and their energies, determined by docking while considering the flexibility of the binding sites ( Castillo-Campos et al, 2023 ). Applying this methodology to the new inhibitory compounds of the latest PLpro variant will enable us to comprehend the intricacies involved in incorporating a larger number of compounds with greater structural variability.…”