Exploring Fungal Natural Products as Potential H1 Receptor Ligands: A Computational Approach to Allergy-Related Research

Introduction: Exploring new possibilities for allergy-related research

Allergic diseases are widespread health concerns involving complex immune responses and the release of inflammatory mediators, including histamine. Histamine exerts several physiological effects through different receptor subtypes, among which the histamine H1 receptor (H1R) plays an important role in allergic responses. Consequently, H1R remains an important pharmacological target for research into allergy-related conditions and the development of antihistamine agents.

Although several H1R-targeting medications are already available, exploring structurally diverse chemical compounds remains valuable for understanding receptor–ligand interactions and identifying potential new molecular candidates. Natural products represent an interesting source of chemical diversity, offering a broad range of molecular scaffolds that may provide new directions for pharmacological investigation.

Among natural-product sources, fungi have attracted considerable scientific interest because of their diverse secondary metabolites and structurally distinctive compounds. However, systematically investigating such a large chemical space through experimental approaches alone can be challenging. Computational methods offer an opportunity to prioritize promising compounds and guide subsequent experimental investigations.

With this motivation, our study explored fungal-derived natural products as potential H1R ligand candidates using an integrated computational approach.

Why explore fungal natural products?

Natural products have historically contributed to pharmacological research by providing structurally diverse molecules and chemical frameworks. Fungi, in particular, produce a wide variety of secondary metabolites with different structural and physicochemical characteristics.

Despite their chemical diversity, fungal-derived compounds remain an interesting area for further computational exploration in receptor-targeted research. Investigating these compounds against H1R could help identify previously unexplored molecular structures with potentially favourable receptor-interaction profiles.

Rather than assuming that natural origin guarantees biological activity, our objective was to apply a systematic computational screening strategy to identify and prioritize compounds that warranted further investigation.

Behind the scenes: From a large chemical library to prioritized candidates

One of the central challenges of this research was handling a relatively large collection of fungal-derived natural products. We used the Natural Products Atlas (NPAtlas) database to obtain a library of 13,381 fungal-derived compounds.

Screening thousands of molecules requires a structured approach because computational docking scores alone cannot establish whether a compound will demonstrate biological activity. Therefore, we designed an integrated workflow to evaluate candidates through multiple complementary computational techniques.

Acrivastine was selected as the reference compound for comparison. Using this established H1R-targeting compound provided a reference point for interpreting the predicted docking profiles of the screened molecules.

The initial screening identified two particularly interesting candidates: CID: 145975830 and CID: 145976597. Their predicted docking scores were −13.654 and −13.997 kcal/mol, respectively, compared with −9.071 kcal/mol for acrivastine.

These results indicated more favourable predicted docking scores for the two prioritized fungal-derived compounds within the applied computational protocol. However, docking scores should be interpreted cautiously because they do not independently establish binding affinity, receptor antagonism, or pharmacological effectiveness.

An integrated computational investigation

To obtain a broader understanding of the prioritized compounds, we extended the investigation beyond molecular docking.

Molecular docking and interaction analysis: Molecular docking was used to examine the predicted binding poses and interaction profiles of the fungal-derived candidates within the H1R binding site. Comparing their docking profiles with acrivastine helped identify compounds worthy of further computational assessment.

Absorption, distribution, metabolism, excretion and toxicity (ADMET): Predicted ADMET properties were evaluated to obtain preliminary insights into the pharmacokinetic and toxicity-related characteristics of the selected compounds. Such assessments are useful during early-stage prioritization, although computational predictions cannot replace experimental pharmacokinetic and toxicological studies.

PASS prediction: PASS analysis indicated potential allergy-related activities for both candidates, including antieczematic, antipruritic, and antipruritic-allergic effects, alongside mucomembranous protection and vasoprotection. Compared with the reference compound, which also showed predicted antihistaminic and H1R antagonist activities, these findings provide additional insights into the candidates' potential biological relevance and warrant further experimental investigation.

Density functional theory (DFT): Quantum-chemical calculations were incorporated to investigate the electronic characteristics of the selected compounds. These analyses complemented the structural and molecular interaction assessments.

Molecular dynamics (MD) simulation: Molecular docking provides a relatively static representation of molecular interactions. To investigate the dynamic behaviour of the selected receptor–ligand complexes, 100 ns molecular dynamics simulations were performed. These simulations offered additional insights into conformational behaviour and interaction patterns over the simulated trajectories.

MM/GBSA and DCCM analyses: Molecular mechanics/generalized Born surface area (MM/GBSA) calculations and dynamic cross-correlation matrix (DCCM) analyses were also incorporated. These complementary approaches helped examine predicted energetic contributions and correlated motions within the simulated molecular systems.

Together, these computational approaches provided multiple perspectives for evaluating the prioritized compounds rather than relying exclusively on their initial docking scores.

What did we find?

Among the screened fungal-derived natural products, CID: 145975830 and CID: 145976597 emerged as the two prominent candidates based on their predicted docking profiles.

Although CID: 145976597 produced the numerically more favourable docking score, CID: 145975830 demonstrated the most consistent overall computational profile across the different evaluation stages.

This distinction is important. The most favourable docking score does not necessarily indicate the strongest overall candidate when additional structural, electronic, pharmacokinetic and dynamic considerations are taken into account.

Our integrated assessment therefore prioritized CID: 145975830 for further investigation while retaining CID: 145976597 as another interesting candidate.

These findings highlight the value of combining multiple computational approaches to obtain a more comprehensive preliminary assessment of natural-product-derived molecular candidates.

Limitations and the next steps

Despite the encouraging computational observations, several limitations should be acknowledged.

First, molecular docking and molecular dynamics simulations provide predictions based on computational models. They cannot independently demonstrate experimentally confirmed receptor binding or functional antagonism.

Second, predicted ADMET characteristics and computationally derived energetic estimates require appropriate experimental verification. Similarly, the behaviour observed during molecular dynamics simulations should not be interpreted as direct evidence of biological activity.

Most importantly, experimental validation remains necessary to determine whether the prioritized compounds interact with H1R and whether they demonstrate antagonistic activity.

Future investigations could include receptor-binding experiments, functional assays and appropriate pharmacological studies. Such work would help establish whether these computationally prioritized fungal-derived compounds have meaningful biological relevance.

Why this research matters

This study demonstrates how large-scale natural-product screening can be integrated with complementary computational techniques to explore potential receptor-targeting molecular candidates.

For researchers working in computational pharmacology, natural-product chemistry, structural biology and GPCR research, it illustrates an approach for investigating chemically diverse compounds and prioritizing candidates for further experimental assessment.

More broadly, exploring fungal natural products may expand the chemical space considered in allergy-related pharmacological research. However, the transition from computational prediction to experimentally demonstrated pharmacological activity remains an essential step.

Read the full research article

The complete study is available as an open-access publication in Scientific Reports (2026):

https://doi.org/10.1038/s41598-026-75139-2

I welcome discussions, constructive suggestions and potential collaborations with researchers working in natural-product chemistry, computational pharmacology, GPCR biology, molecular modelling and allergy-related research.

I hope this work encourages further discussion about how computational approaches can help explore the largely diverse chemical space of natural products and support future experimental investigations.