Molecular Docking And ADMET Predictions Of Substituted Cordyheptapeptide Derivatives: Evaluating Antifungal Potential Against The 5U32 Fungal RNA Kinase Complex

Fungi, as ubiquitous micro-organisms, inhabit diverse environments and engage in symbiotic or endophytic relationships with a wide array of host organisms, utilizing various resources for their survival. Among these, marine fungal strains stand out for their ecological significance, owing to their capacity to produce a diverse range of specialized metabolites. These metabolites, which include alkaloids, polyketides, terpenoids, and peptides, play pivotal roles in ecological interactions and have garnered attention for their potential applications in various fields, particularly in medicine[1]. Peptide-based therapies have emerged as promising avenues for treating a multitude of diseases, including fungal infections. The unique characteristics of peptides, such as high affinity, specificity, and low toxicity, position them as advantageous alternatives to traditional chemical drugs. Among these peptides, cordyheptapeptide exemplifies beneficial properties that underscore the potential of peptides in therapeutic applications [2] Bioactive peptides, defined as specific fragments of proteins whose activity stems from their amino acid composition and sequence, are abundant in both terrestrial and marine organisms. In recent years, there has been a growing interest in microorganism-derived peptides (MdPs) due to their distinctive molecular structures and a wide spectrum of associated bioactivities. These MdPs have demonstrated antimicrobial [3], antimalarial [4,5], antibacterial [6], cytotoxic [7,8], and antiviral activities [9], making them valuable targets for further exploration and development in biomedical research. In the peptide production process, the formation of the peptide bond is a crucial step, requiring the activation of carboxylic acid. This activation is typically achieved using peptide coupling reagents. If the activation of carboxylic acid is slow, these coupling reagents may degrade, rendering them ineffective in activating the carboxyl function. Common coupling reagents include carbodiimides, phosphonium and ammonium salts, fluoroformamidinium coupling reagents, organophosphorus reagents, and triazine coupling reagents [10]. In the investigation of the relationship between molecular size and cell permeability, researchers synthesized and studied the properties of cordyheptapeptide A . Cordyheptapeptide A, originally found in Cordyceps, a fungus with pharmaceutical potential, belongs to a family of peptides that exhibit toxicity against bacteria, fungi, and various cancer cell lines. While its crystal structure and total synthesis have been reported, its biological targets and mechanism of action have remained unknown [11]. Fungal tRNA ligase (Trl1) is crucial for repairing RNA breaks with specific end structures, such as 2',3'-cyclic-PO4, and 5'-OH ends, occurring during tRNA splicing and non-canonical mRNA splicing in the fungal unfolded protein response. Trl1 comprises C-terminal cyclic phosphodiesterase and central polynucleotide kinase domains, which work together to generate the necessary termini for sealing by an N-terminal ligase domain. Trl1 enzymes are present in all human fungal pathogens and are promising targets for antifungal drug discovery due to their unique domain compositions and mechanisms compared to mammalian RtcB-type tRNA splicing enzymes. Trl1 exhibits a distinctive preference for using GTP as a phosphate donor for the RNA kinase reaction [12]. In a recent study, the crystal structure of the kinase domain of Trl1 from Candida albicans has been elucidated, unveiling the presence of GDP and Mg2+ bound within the active site. This kinase domain showcases a P-loop phosphotransferase fold, characterized by a distinct 'G-loop' element that plays a pivotal role in conferring specificity towards guanine nucleotides. Notably, mutations observed in amino acids involved in interactions with the guanine nucleobase have been found to abrogate kinase activity in vitro and impede Trl1 function in vivo. These findings underscore the potential of Trl1 kinase as a viable target for antifungal interventions [13].

MATERIAL AND METHODS:

The study of molecular docking was carried out on a system with computational specifications (HP Pavilion AMD RyzenTM 5 Hexa Core 5500 APU @ 2.1GHz with turbo boost up to 4GHz Processor version 5500U and 16.00 GB RAM with 64-bit Windows-11 operating system).

• PubChem webserver
• Protein Data Bank
• BioviaDiscovery Studio visualizer
• PyRx, PubChem, Webserver
• Swiss dock

The Swiss ADME study methodology involves the following steps:

1. Input Compound Structure:

Input the chemical structure of the compound of interest using SMILES notation.

2. Calculation of Pharmacokinetics Parameters:

SwissADME calculates various pharmacokinetic parameters such as molecular weight, lipophilicity, water solubility, and permeability.

3. Prediction of Drug-Likeness:

Evaluate adherence to Lipinski's Rule of Five and other drug-likeness rules:

3. 1. Molecular weight < 500>
   2. LogP (miLogP) < 5>
   3. Hydrogen bond donors < 5>
   4. Hydrogen bond acceptors < 10>
   5. Rule violations ? 2
4. Calculation of Physicochemical Properties:

Compute additional physicochemical properties such as polar surface area and number of rotatable bonds.

5. Visualization and Interpretation:

Present results in a user-friendly format for easy interpretation. The methodology of molecular docking involves several crucial steps to predict the binding orientation and affinity between a ligand and a receptor. Here's an overview of the main steps involved:

1. Preparation of Ligand and Receptor:

This step involves preparing the 3D structures of both the ligand (small molecule) and the receptor (typically a protein) for docking. This may include adding hydrogen atoms, assigning partial charges, and optimizing geometry.

2. Grid box generation:

A grid is generated around the receptor to define the search space for the ligand. This grid typically encompasses the active site or binding pocket of the receptor where ligand binding is expected to occur.

3. Scoring function selection:

Various scoring functions are available to evaluate the fitness of ligand binding within the receptor's binding site. These scoring functions estimate binding affinity based on factors such as electrostatic interactions, van der Waals forces, hydrogen bonding, and desolvation energies.

4. Docking algorithm selection:

Different algorithms are employed to explore the conformational space of the ligand and predict its optimal binding pose within the receptor's binding site. These algorithms include stochastic methods like genetic algorithms, simulated annealing, or deterministic methods like Lamarckian genetic algorithm (LGA) and Monte Carlo-based techniques.

5. Docking simulation:

The ligand is systematically positioned and oriented within the receptor's binding site using the chosen docking algorithm. The ligand's conformational flexibility may be accounted for by allowing it to rotate, translate, and adapt its shape during the docking process.

6. Scoring and ranking:

After docking, the conformations generated are scored using the selected scoring function to assess the binding affinity of each ligand-receptor complex. Ligands are ranked based on their predicted binding energies or scores.

7. Analysis and visualization:

The docked complexes are analyzed to identify key interactions such as hydrogen bonding, hydrophobic interactions, and ?-? stacking between the ligand and receptor residues. Visualization tools like Biovia Discovery Studio or PyMOL are commonly used for this purpose. By following these steps, molecular docking enables the prediction and analysis of ligand-receptor interactions, facilitating drug discovery and structure-based drug design efforts.

RESULT AND DISCUSSION:

The ADME study and its correlation with Lipinski's rule provides valuable insights into the drug-likeness and potential pharmacokinetic behavior of cordyheptapeptide compounds and their derivatives. Firstly, the ADME study is crucial for assessing the safety and efficacy of pharmaceutical substances by evaluating their absorption, distribution, metabolism, and excretion within an organism's body. Lipinski's rule, a widely accepted guideline in drug discovery, outlines specific molecular properties that are important for drug pharmacokinetics. These properties include molecular weight, hydrophobicity, hydrogen bond donors and acceptors, and the number of violations of Lipinski's rule. According to Lipinski's rule, for a compound to exhibit oral activity, it should ideally adhere to certain criteria, such as having a molecular weight less than 500 g/mol, a hydrophobicity (miLogP) less than 5, fewer than 5 hydrogen bond donors, fewer than 10 hydrogen bond acceptors, and no more than 2 rule violations. The analysis of the ADME study, particularly focusing on the adherence of cordyheptapeptide compounds and derivatives to Lipinski's rule, reveals important findings. While the compounds generally follow Lipinski's rule to some extent, it's observed that the first three derivatives strictly adhere to the rule, while the last two derivatives exhibit moderate adherence. This suggests that the molecular properties of the compounds may vary, influencing their pharmacokinetic behavior. However, it's noted that some discrepancies are observed in the adherence to Lipinski's rule. Specifically, the molecular weight of the compounds exceeds the recommended threshold of 500 g/mol, and the accuracy of the molecular polar surface (TPSA) falls outside the expected range of 76.36 to 122.18. These deviations from Lipinski's rule may raise concerns regarding the drug-likeness and potential oral activity of the compounds. In conclusion, while the ADME study provides valuable insights into the pharmacokinetic properties of cordyheptapeptide compounds and derivatives, the observed deviations from Lipinski's rule highlight the importance of further investigation and optimization in drug development. Addressing these discrepancies and optimizing the molecular properties of the compounds could enhance their potential as viable drug candidates with improved oral activity and pharmacokinetic profiles.

Table 2: In silico ADME properties of Cordyheptapeptide and derivatives

Molecular docking:

The cordyheptapeptide compounds created in this study have been thoroughly investigated through in-silico ADME research, indicating their potential as lead molecules. Molecular docking investigations revealed that among the derivatives, cordyheptapeptide C (cordy C) emerged as the most potent inhibitor. ADME analysis further supported the suitability of these compounds for drug-likeness, highlighting their effective digestive and dermal absorption capabilities. Notably, the compounds, particularly cordy C, exhibited strong inhibitory activity against the target 5U32, positioning them as promising candidates for antifungal agents. These findings underscore the potential of cordyheptapeptide compounds as valuable candidates for further development and exploration in the quest for novel antifungal therapeutics.

Fig. 2: 3D interaction binding modes of cordy heptapeptide C with 5U32