Exploration of ligand-induced protein conformational alteration, aggregate formation, and its inhibition: A biophysical insight

The effect of novel antihypertensive drug valsartan on lysozyme aggregation: A combined in situ and in silico study

Protein misfolding can result in amyloid fiber aggregation, which is associated with various types of diseases. Therefore, preventing or treating abnormally folded proteins may provide therapeutic intervention for these diseases. Valsartan (VAL) is an angiotensin II receptor blocker (ARB) that is used to treat hypertension. In this study, we examine the anti-aggregating effect of VAL against hen egg-white lysozyme (HEWL) amyloid fibrils through spectroscopy, docking, and microscopic analysis. In vitro formation of HEWL amyloid fibrils was indicated by increased turbidity, RLS (Rayleigh light scattering), and ThT fluorescence intensity. 10 μM VAL, amyloid/aggregation was inhibited up to 83% and 72% as measured by ThT and RLS respectively. In contrast, 100 μM VAL significantly increases the fibril aggregation of HEWL. CD spectroscopy results show a stabilization of HEWL α-helical structures in the presence of 10 μM VAL while the increase in β-sheet was detected at 100 μM concentration of VAL. The hydrophobicity of HEWL was increased at 100 μM VAL, suggesting the promotion of aggregation via its self-association. Steady-state quenching revealed that VAL and HEWL interact spontaneously via hydrogen bonds and van der Waals forces. Transmission electron microscopy (TEM) images illustrate that the needle-like fibers of HEWL amyloid were reduced at 10 μM VAL, while at 100 μM the fibrils of amyloid were increased. Additionally, our computational studies showed that VAL could bind to two binding sites within HEWL. In the BS-1 domain of HEWL, VAL binds to ASN⁵⁹, ILE⁹⁸, ILE⁵⁸, TRP¹⁰⁸, VAL¹⁰⁹, SER⁵⁰, ASP⁵², ASN⁵⁹, ALA¹⁰⁷, and TRP¹⁰⁸ residues with a binding energy of -9.72 kcal mol^-1. Also, it binds to GLU⁷, ALA¹⁰, ALA¹¹, CYS⁶, ARG¹²⁸, and ARG¹⁴ in the BS-2 domain with a binding energy of -5.89 kcal mol^-1. VAL, therefore, appears to have dual effect against HEWL aggregation. We suggest that VAL stabilizes HEWL's aggregation-prone region (APR) at 10 μM, preventing aggregation. Also, we assume that at 100 μM, VAL occupies BS-2 beside BS-1 and destabilizes the folding structure of HEWL, resulting in aggregation. Further studies are needed to investigate the mechanism of action and determine its potential side effects.

Allergens and their associated small molecule ligands-their dual role in sensitization

Many allergens feature hydrophobic cavities that allow the binding of primarily hydrophobic small‐molecule ligands. Ligand‐binding specificities can be strict or promiscuous. Serum albumins from mammals and birds can assume multiple conformations that facilitate the binding of a broad spectrum of compounds. Pollen and plant food allergens of the family 10 of pathogenesis‐related proteins bind a variety of small molecules such as glycosylated flavonoid derivatives, flavonoids, cytokinins, and steroids in vitro. However, their natural ligand binding was reported to be highly specific. Insect and mammalian lipocalins transport odorants, pheromones, catecholamines, and fatty acids with a similar level of specificity, while the food allergen β‐lactoglobulin from cow's milk is notably more promiscuous. Non‐specific lipid transfer proteins from pollen and plant foods bind a wide variety of lipids, from phospholipids to fatty acids, as well as sterols and prostaglandin B2, aided by the high plasticity and flexibility displayed by their lipid‐binding cavities. Ligands increase the stability of allergens to thermal and/or proteolytic degradation. They can also act as immunomodulatory agents that favor a Th2 polarization. In summary, ligand‐binding allergens expose the immune system to a variety of biologically active compounds whose impact on the sensitization process has not been well studied thus far.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.