Application of Molecular Modeling Techniques to Investigate Phytochemicals as Prospective Anti-Malarial Agents

Introduction

Malaria caused by Plasmodium falciparum infections can be life-threatening when it is left untreated. According to the World Health Organization (WHO), malaria caused an estimated 608,000 deaths worldwide in 2022, with the majority of these deaths caused by Plasmodium falciparum (https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022.oNE). One of the main challenges in the fight against malaria is the origin of drug-resistant strains of Plasmodium falciparum to several antimalarial drugs. Over time, the parasite has developed resistance to several anti-malarial drugs. For example, Chloroquine resistance is primarily mediated by mutations in the parasite's chloroquine resistance transporter gene (PfCRT). Sulfadoxine-pyrimethamine resistance (SP) is mainly due to mutations in the dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS) genes. Artemisinin resistance has emerged in the region of Southeast Asia, particularly in Cambodia, Thailand, Vietnam, and Myanmar. Resistance to individual antimalarial drugs, multidrug resistance, in which the parasite shows resistance to several classes of antimalarial drugs, reducing the effectiveness of the approved drugs (Wongsrichanalai and Meshnick, 2008; Mwai et al., 2009; Ashley et al., 2014; Menard and Dondorp, 2017). Compounds sourced from plants have been extensively researched for their potential as antimalarial agents, owing to their wide-ranging chemical structures and biological activities. Several well-known phytochemicals have been investigated for their potential antimalarial properties. These include artemisinin, derived from the herb Artemisia annua, quinine obtained from the bark of the cinchona tree, and curcumin, a polyphenolic compound found in turmeric (Curcuma longa) (Tu, 2011; Mueller et al., 2000; Mishra et al., 2008). However, due to the great diversity of plants and the molecules they produce, identification of the potential anti-malarial compounds is a difficult task. In this context, molecular modeling techniques are proved as the game changer in addressing such obstacles. Although molecular modeling is a broad field, but especially, structure prediction, molecular docking, molecular dynamics simulation, and quantitative structure-activity relationship (QSAR) modeling methods represent the most widely used components of computational modeling and are crucial for the identification of lead compounds. While molecular docking-based virtual screening identifies hit compounds with the highest binding affinity and correct binding mode, it is often disadvantaged by the lack or improper simulation of receptor flexibilities. Due to the dynamic nature of proteins, the conformational changes they undergo are critical for the recognition of ligands, making their flexibility essential in simulations to accurately depict biological systems. Molecular Dynamics (MD) simulation is adept at capturing this complexity, revealing the time-dependent dynamics of protein-ligand interactions. MD simulation operates at an atomistic level, treating proteins, ligands, water molecules, and ions as particles governed by force fields derived from Newton's classical laws of motion. Essentially, MD simulation serves as a molecular microscope, examining the stability of ligands within receptor targets' active pockets, thereby validating the outcomes of molecular docking-based virtual screening processes (Cheng et al., 2013; Adelusi et al., 2022; Glaab, 2-016; Salo-Ahen et al., 2020).

This chapter aims to illustrate the various methods and applications of molecular modeling methods in the discovery of phytochemicals as anti-malarial compounds. The techniques, ranging from protein structure prediction to virtual screening and molecular dynamic simulations, are covered with relevant examples from the recent literature.