Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations

HIV-1 protease (PR) is thought to be efficient targets of anti-AIDS drug design. Molecular dynamics (MD) simulations and multiple post-processing analysis technologies were applied to decipher molecular mechanism underlying binding of three drugs Lopinavir (LPV), Nelfinavir (NFV) and Atazanavir (ATV) to the PR. Binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) suggest that compensation between binding enthalpy and entropy plays a vital role in binding of drugs to PR. Dynamics analyses show that binding of LPV, NFV and ATV highly affects structural flexibility, motion modes and dynamics behaviour of the PR, especially for two flaps. Computational alanine scanning and interaction network analysis verify that although three drugs have structural difference, they share similar binding modes to the PR and common interaction clusters with the PR. The current findings also confirm that residues located interaction clusters, such as Asp25/Asp25', Gly27/Gly27', Ala28/Ala28', Asp29, Ile47/Ile47', Gly49/Gly49', Ile50/Ile50', Val82/Val82' and Ile84/Ile84, can be used as efficient targets of clinically available inhibitors towards the PR.

Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

The main protease, M^pro/3CL^pro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, '13b' (α-ketoamide inhibitor), opened up opportunities to understand the M^pro mechanism of action. A comparative analysis of both forms of M^pro was carried out to elucidate the binding site architectural differences in the presence and absence of '13b'. Molecular dynamics simulations suggest that the presence of '13b' enhances the stability of M^pro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of M^pro. In comparison to monomer, the relatively high affinity of '13b' is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of '13b' binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent M^pro inhibitors.Communicated by Ramaswamy H. Sarma.

Binding Modes of Small-Molecule Inhibitors to the EED Pocket of PRC2

Polycomb Polycomb repressive complex 2 (PRC2) plays a key role in silencing epigenetic gene through trimethylation of lysine 27 on histone 3 (H3K27). Dysregulations of PRC2 caused by overexpression and mutations of the core subunits of PRC2 have been implicated in many cancers. The core subunits EZH1/2 are histone-lysine N-methyltransferases that function as the enzymatic component of PRC2. While the core subunit EED is a scaffolding protein to support EZH1/2 and binds JARID2K116me3/H3K27me3 to enhance the enzymatic activity of PRC2 through allosteric activation. Recently, several small molecules that compete with JARI2K116me3 and H3K27me3 have been reported. These molecules selectively bind to the JARID2K116me3/H3K27me3-binding pocket of EED, thereby preventing the allosteric regulation of PRC2. These first-in-class PRC2 inhibitors show robust suppression in DLBCL cell lines, demonstrating anticancer drugs that target the EED subunit of PRC2 are viable. In this study, we used the recently developed MM/GBSA_IE and the alanine scanning method to analyze the hot spots in EED/inhibitor interactions. The analysis of these hot and warm spots helps us to understand the fundamental differences between inhibitors. Our results give a quantitative explanation on why the binding affinities of EED/A-395 interactions are stronger than that of EED/EED226 while their binding modes are similar and provide valuable insights for rational design of novel EED inhibitors.

Identification of Key Residues Associated with the Interaction between Plutella xylostella Sigma-Class Glutathione S-Transferase and the Inhibitor S-Hexyl Glutathione

Glutathione S-transferases (GSTs) are important detoxification enzymes involved in the development of metabolic resistance in Plutella xylostella. Uncovering the interactions between representative PxGSTs and the inhibitor S-hexyl glutathione (GTX), helps in the development of effective PxGST inhibitors for resistance management. As the PxGST most severely inhibited by GTX, PxGSTσ (sigma-class PxGST) adopts the canonical fold of insect GSTs. The formation of the PxGSTσ-GTX complex is mainly driven by H-bond and hydrophobic interactions derived from the side chains of favorable residues. Of the residues composing the active site of PxGSTσ, Lys43 and Arg99 are two hot spots, first reported in the binding of GSH derivatives to GSTs. Such differences indicate the metabolism discrimination of different insect GSTs. Unfavorable interactions between the PxGSTσ active site and GTX are depicted as well. The research guides the discovery and optimization of PxGSTσ inhibitors.

Key Residues Involved in the Interaction between Cydia pomonella Pheromone Binding Protein 1 (CpomPBP1) and Codlemone

Codlemone exhibited high affinity to CpomPBP1; studying their binding mode can provide insights into the rational design of active semiochemicals. Our findings suggested that residues including Phe12, Phe36, Trp37, Ile52, Ile 94, Ala115, and Phe118 were favorable to the binding of codlemone to CpomPBP1, whereas residues providing unfavorable contributions such as Ser56 were negative to the binding. van der Waals energy and electrostatic energy, mainly derived from the side chains of favorable residues, contributed most to the formation and stability of the CpomPBP1-codlemone complex. Of the residues involved in the interaction between CpomPBP1 and codlemone, Phe12 and Trp37, the mutation of which into Ala caused a significant decrease of CpomPBP1 binding ability, were two key residues in determining the binding affinity of codlemone to CpomPBP1. This study shed light on discovering novel active semiochemicals as well as facilitating chemical modification of lead semiochemicals.

PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots

BACKGROUND

Modeling protein-protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein-protein interfaces, which contribute more in stabilizing a protein-protein interface.

RESULTS

We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate 'native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning.

CONCLUSIONS

PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein-protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.