Analysis of non-peptidic compounds as potential malarial inhibitors against Plasmodial cysteine proteases via integrated virtual screening workflow

Abstract

Falcipain-2 and falcipain-3, haemoglobin degrading enzymes in Plasmodium falciparum, are validated drug targets for the development of effective inhibitors against malaria. However, no commercial drug targeting falcipains has been developed despite their central role in the life-cycle of the parasites. In this work, in silico approaches are used to identify key structural elements that control the binding and selectivity of a diverse set of non-peptidic compounds onto FP-2, FP-3 and homologs from other Plasmodium species as well as human cathepsins. Hotspot residues and the underlying non-covalent interactions, important for the binding of ligands, are identified by interaction fingerprint analysis between the proteases and 2-cyanopyridine derivatives (best hits). It is observed that the size and chemical type of substituent groups within 2-cyanopyridine derivatives determine the strength of protein-ligand interactions. This research presents novel results that can further be exploited in the structure-based molecular guided design of more potent antimalarial drugs.

Predicting binding modes of reversible peptide-based inhibitors of falcipain-2 consistent with structure-activity relationships

“…A similar conclusion, in particular referring to N173, was drawn by Musyoka et al . based on per‐residue binding free energy decomposition for the predicted complex structures of FP‐2 and various 2‐cyanopyrimidine nitrile (CP) derivatives . However, G83 does not seem to be energetically favorable for the binding of CPs to FP‐2, according to the authors' predictions, which underscores the structural differences of these nonpeptidic compounds and the inhibitors studied here, especially in their respective P3 sites.…”

“…Three FP‐2 residues, that is, Q36, G83, and N173, were predicted to form hydrogen bonds with the backbone nitrogen and oxygen atoms at P2 to P1′ sites of the ligands with relatively high occupancies (Supporting Information, Table S2). Equivalent hydrogen bonds occur at the interfaces of crystal structures of C1 cysteine proteases in complex with peptide‐based inhibitors, and have been predicted for FP‐2 and other homologous enzymes complexed with nonpeptidic compounds . Remarkably, G83 and N173 each formed up to two hydrogen bonds with the inhibitors ((a–d), Fig.…”

“…In this sense, the Molecular Mechanics Generalized‐Born (Poisson–Boltzmann) Surface Area (MM‐GB(PB)SA) methods, applied to either single structures or ensembles generated by conventional molecular dynamics (cMD) simulations, have proven useful as postdocking refinement tools to identify near‐native binding modes of ligands to the target protein . Remarkably, the combination of molecular docking, cMD simulations, and MM‐PBSA has been previously applied to study the interaction of FP‐2 with nonpeptidic and peptidomimetic inhibitors . However, none of these works employed MM‐PBSA free energy calculations as a rescoring step to guide pose selection, but to assess the binding energetics of preselected single poses of the studied ligands.…”

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis

“…It can be used to check if a protein structure remains stable after the introduction of one or more SNPs. Similarly, it can be used to determine the stability of protein-ligand complexes after docking [90]. While molecular docking predicts how well a compound docks to a receptor, molecular dynamics can predict how stably bound the compound is and whether it will stay bound over a specified period.…”

“…Molecular docking simulations are often used in combination with homology modeling and virtual screening [90,91]. In terms of computational SNP analysis, molecular dynamics can be used to determine whether introducing a SNP will destabilize a protein or perhaps cause the protein to move or fold in a different way [92].…”