Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Identification of Microbial-Based Natural Products as Potential CYP51 Inhibitors for Eumycetoma Treatment: Insights from Molecular Docking, MM-GBSA Calculations, ADMET Analysis, and Molecular Dynamics Simulations

Background/Objectives: Eumycetoma, caused by Madurella mycetomatis, is a chronic fungal infection with limited treatment options and increasing drug resistance. CYP51, a key enzyme in ergosterol biosynthesis, is a well-established target for azole antifungals. However, existing azole drugs demonstrate limited efficacy in treating eumycetoma. Microbial-based natural products, with their structural diversity and bioactivity, offer a promising source for novel CYP51 inhibitors. This study aimed to identify potential Madurella mycetomatis CYP51 inhibitors from microbial natural products using molecular docking, MM-GBSA calculations, ADMET analysis, and molecular dynamics (MD) simulations. Methods: Virtual screening was conducted on a library of microbial-based natural products using an in-house homology model of Madurella mycetomatis CYP51, with itraconazole as the reference drug. The top compounds from initial docking were refined through Standard and Extra Precision docking. MM-GBSA calculations assessed binding affinities, and ADMET analysis evaluated drug-like properties. Compounds with favorable properties underwent MD simulations. Results: The computational investigations identified 34 compounds with better docking scores and binding affinity than itraconazole. Of these, 9 compounds interacted with the heme group and key residues in the active site of Madurella mycetomatis CYP51. In silico pharmacokinetic profiling identified 3 compounds as promising candidates, and MD simulations confirmed their potential as CYP51 inhibitors. Conclusions: The study highlights microbial-derived natural products, particularly monacyclinone G, H, and I, as promising candidates for Madurella mycetomatis CYP51 inhibition, with the potential for treating eumycetoma, requiring further experimental validation.

Updating the Pharmacological Effects of α-Mangostin Compound and Unraveling Its Mechanism of Action: A Computational Study Review

α-Mangostin, initially identified in 1855, is a xanthone derivative compound predominantly located in the pericarp of the mangosteen fruit (Garcinia mangostana L). This compound is known for its beneficial properties as an antioxidant and anti-inflammatory agent, still holding promise for potential benefits in other related pathologies. In the investigative process, computational studies have proven highly valuable in providing evidence and initial screening before progressing to preclinical and clinical studies. This review aims to present the pharmacological findings and mechanisms of action of α-mangostin based on computational studies. The compilation of this review is founded on the analysis of relevant articles obtained from PubMed, Scopus, and ScienceDirect databases. The study commences with an elucidation of the physicochemical characteristics, drug-likeness, pharmacokinetics, and toxicity profile of α-mangostin, which demonstrates that α-mangostin complies with the Lipinski's Rule of Five, exhibits favorable profiles of absorption, distribution, metabolism, and excretion, and presents low toxicity. Subsequent investigations have revealed that computational studies employing various software tools including ArgusLab, AutoDock, AutoDock Vina, Glide, HEX, and MOE, have been pivotal to comprehend the pharmacology of α-mangostin. Beyond its well established roles as an antioxidant and anti-inflammatory agent, α-mangostin is now recognized for its pharmacological effects in Alzheimer's disease, diabetes, cancer, chronic kidney disease, chronic periodontitis, infectious diseases, and rheumatoid arthritis. Moreover, α-mangostin is projected to have applications in pain management and as a potent mosquito larvicide. All of these findings are based on the attainment of adequate binding affinity to specific target receptors associated with each respective pathological condition. Consequently, it is anticipated that these findings will serve as a foundation for future scientific endeavours, encompassing both in vitro and in vivo studies, as well as clinical investigations, to better understand the pharmacological effects of α-mangostin.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.