In silico rational design and virtual screening of antixoidant tripeptides based on 3D-QSAR modeling

Special Issue: "Rational Design and Synthesis of Bioactive Molecules"

The rational design of novel bioactive molecules is a critical but challenging task in drug discovery [...].

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides

The comparative molecular similarity indices analysis (CoMSIA) method is a widely used 3D-quantitative structure-activity relationship (QSAR) approach in the field of medicinal chemistry and drug design. However, relying solely on the Partial Least Square algorithm to build models using numerous CoMSIA indices has, in some cases, led to statistically underperforming models. This issue has also affected 3D-CoMSIA models constructed for the ferric thiocyanate (FTC) dataset from linoleic antioxidant measurements. In this study, a novel modeling routine has been developed incorporating various machine learning (ML) techniques to explore different options for feature selection, model fitting, and tuning algorithms with the ultimate goal of arriving at optimal 3D-CoMSIA models with high predictivity for the FTC activity. Recursive Feature Selection and SelectFromModel techniques were applied for feature selection, resulting in a significant improvement in model fitting and predictivity (R2, RCV2, and R2_test) of 24 estimators. However, these selection methods did not fully address the problem of overfitting and, in some instances, even exacerbated it. On the other hand, hyperparameter tuning for tree-based models resulted in dissimilar levels of model generalization for four tree-based models. GB-RFE coupled with GBR (hyperparameters: learning_rate = 0.01, max_depth = 2, n_estimators = 500, subsample = 0.5) was the only combination that effectively mitigated overfitting and demonstrated superior performance (RCV2 of 0.690, R2_test of 0.759, and R2 of 0.872) compared to the best linear model, PLS (with RCV2 of 0.653, R2_test of 0.575, and R2 of 0.755). Therefore, it was subsequently utilized to screen potential antioxidants among a range of Tryptophyllin L tripeptide fragments, leading to the synthesis and testing of three peptides: F-P-5Htp, F-P-W, and P-5Htp-L. These peptides exhibited promising activity levels, with FTC values of 4.2 ± 0.12, 4.4 ± 0.11, and 1.72 ± 0.15, respectively.

Investigation of the Persistence, Toxicological Effects, and Ecological Issues of S-Triazine Herbicides and Their Biodegradation Using Emerging Technologies: A Review

S-triazines are a group of herbicides that are extensively applied to control broadleaf weeds and grasses in agricultural production. They are mainly taken up through plant roots and are transformed by xylem tissues throughout the plant system. They are highly persistent and have a long half-life in the environment. Due to imprudent use, their toxic residues have enormously increased in the last few years and are frequently detected in food commodities, which causes chronic diseases in humans and mammals. However, for the safety of the environment and the diversity of living organisms, the removal of s-triazine herbicides has received widespread attention. In this review, the degradation of s-triazine herbicides and their intermediates by indigenous microbial species, genes, enzymes, plants, and nanoparticles are systematically investigated. The hydrolytic degradation of substituents on the s-triazine ring is catalyzed by enzymes from the amidohydrolase superfamily and yields cyanuric acid as an intermediate. Cyanuric acid is further metabolized into ammonia and carbon dioxide. Microbial-free cells efficiently degrade s-triazine herbicides in laboratory as well as field trials. Additionally, the combinatorial approach of nanomaterials with indigenous microbes has vast potential and considered sustainable for removing toxic residues in the agroecosystem. Due to their smaller size and unique properties, they are equally distributed in sediments, soil, water bodies, and even small crevices. Finally, this paper highlights the implementation of bioinformatics and molecular tools, which provide a myriad of new methods to monitor the biodegradation of s-triazine herbicides and help to identify the diverse number of microbial communities that actively participate in the biodegradation process.

Alanine Substitution to Determine the Effect of LR5 and YR6 Rice Peptide Structure on Antioxidant and Anti-Inflammatory Activity

The relationship between the structure of peptides LR5 (LHKFR) and YR6 (YGLYPR) and their antioxidant and anti-inflammatory activity remains unclear. Herein, leucine, tyrosine, proline, and phenylalanine at different positions in the peptides were replaced by Alanine (Ala), and two new pentapeptides (AR5 and LAR5) and four hexapeptides (AGR6, YAR6, YLR6, and YGR6) were obtained. The effect of Ala replacement on the hydrophobicity, cytotoxicity, NO inhibition rate, and active oxygen radical scavenging ability of these peptides and their antioxidant and anti-inflammatory abilities were investigated. The results indicated that the hydrophobicity of the peptides was associated with their amino acid composition and their specific sequence. However, hydrophobicity had no significant effect on cytotoxicity. Ala replacement was shown to enhance hydrophobicity and consequently increased the antioxidant and anti-inflammatory activity of the peptides. The molecular docking studies indicated that the amino acid interactions of the peptide with the Keap1 protein influenced the hydrophobicity and thus affected the antioxidant activity of the peptide.

Field and atom-based 3D-QSAR models of chromone (1-benzopyran-4-one) derivatives as MAO inhibitors

Monoamine oxidases (MAOs) are flavo-enzymes that aid in the oxidative deamination of neurotransmitters like dopamine, serotonin, and epinephrine. MAO inhibitors are antidepressants that work by preventing the breakdown of brain neurotransmitters and regulating mood. MAO inhibitors that use the chromone (1-benzopyran-4-one) structure have been found to be quite effective in studies. The current study involves the creation of pharmacophore models, 3-D QSAR, virtual screening, and docking investigations, all of which are evaluated using various criteria. The investigation included 39 ligands that emerged pharmacophore AHRRR_1, as the best pharmacophore model with a survival score of 5.6485. The 3D QSAR investigation revealed a significant model with the values of R2 = 0.9064 and Q2 = 0.8239. Docking study revealed that compound 18 had the highest docking (-10.402 kcal/mol) score in the series and showed interactions with the essential amino acid TYR398 required for MAO inhibitory activity. ZINC compounds were screened using the created pharmacophore model, which was followed up with a virtual screening study. The ZINC compounds with the best XP docking scores are ZINC03113255, ZINC07777127, ZINC05166353 and ZINC09341502 (with docking scores -10.021, -9.486, -8.031 and -7.792 kcal/mol, respectively). ZINC03113255, which showed the best score, has binding interactions with amino acid residues, TYR326, TYR398 and LYS296 of monoamine oxidase B. The ADME analysis demonstrated the compound's drug-like characteristics. The findings of this study may be used in the development of chromone compounds that target the MAO inhibitor.Communicated by Ramaswamy H. Sarma.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Structure-guided discovery of antioxidant peptides bounded to the Keap1 receptor as hunter for potential dietary antioxidants

Human health can be damaged by free radicals, and antioxidant peptides are excellent radical scavengers. Antioxidant tripeptides data set based on 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulofnic acid) (ABTS) assay was created, 9 types of descriptors were integrated and 4 quantitative structure-activity relationship (QSAR) models were constructed in this study. Several structural factors influencing the activity of antioxidant tripeptides and the dominant amino acids at each position of tripeptides were revealed by the optimal model. Ten food-derived tripeptides with higher activity were selected for synthesis and activity determination. Molecular docking results demonstrated that these tripeptides were stably bound to the Keap1 receptor, further elucidating the antioxidant mechanism. It was known from the simulation of gastrointestinal digestion experiments that the model results possessed a guiding effect on the selection of proteins with high antioxidant activity. The performance of the model was proved to be robust after validation.

Virtual screening and rational design of antioxidant peptides based on tryptophyllin L structures isolated from the Litoria rubella frog

Discovery of natural antioxidants has been carried out for decades relying mainly on experimental approaches that are commonly associated with time and cost demanding biochemical assays. The maturation of quantitative structure activity relationship (QSAR) modelling has provided an alternative approach for searching and designing antioxidant compounds with alleviated costs. As a contribution to this approach, this work aimed to establish a fragment-based 3D-QSAR procedure to discover and design potential antioxidants based on tryptophyllin L structures isolated from the red tree frog Litoria rubella. A force field and a Gaussian 3D-QSAR model were built to screen for potential antioxidants from tripeptide fragments covering all sequences of tryptophyllin L database. Among those, PWY(NH₂ ) corresponding tryptophyllin L 4.1 was predicted to have the highest 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) radical cation (ABTS⁺ ·) scavenging capability. Two newly designed peptides PYW and PYW(NH₂ ) together with PWY(NH₂ ), tryptophyllin L 4.1, and the reference peptide PWY were synthesized and subjected to two antioxidant assays including ABTS scavenging and ferric reducing antioxidant power assays. Although the experimental TEAC values of the five peptides were roughly similar to those from predictions, the activity order was not in agreement with the predictions. The dissimilarities were accounted by the difference in the experimental procedures, the deviation of modelling regression, and the synergetic effect of structural and experimental features. The ABTS radical scavenging assays revealed that all the tested peptides were strong ABTS⁺ · scavengers with the antioxidant capabilities approximately twice as high as trolox and higher than glutathione. The ferric reducing activities of the peptides were, on the other hand, much weaker than that of trolox suggesting different antioxidant mechanisms inserted by trolox and the peptides. This work was a demonstration that 3D-QSAR methods can be employed in conjunction with experimental methods to effectively detect and design antioxidant peptides.

The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella)

It is usually held that good-quality models for the biological activity of peptides must take into account their 3D architecture and descriptors of quantum mechanics. However, the present study shows that it is possible to build up models without these complex calculations. The structure of tripeptides represented by sequences of one-symbol abbreviations of the corresponding amino acids serves to build up quantitative structure-activity relationships for the antioxidant activity of tripeptides from frog skin. The statistical quality of the best model for the validation set is n = 27, r² = 0.93, RMSE = 0.15.

Exploring Molecular Insights of Cereal Peptidic Antioxidants in Metabolic Syndrome Prevention

The prevalence of metabolic syndrome (MetS) is presently an alarming public health problem globally. Oxidative stress has been postulated to be strongly correlated with MetS, such as type 2 diabetes, obesity, hypertension, cardiovascular diseases, and certain cancers. Cereals are important staple foods which account for a huge proportion of the human diet. However, owing to recent growing demand and the search for natural antioxidants for the prevention and management of MetS, cereal peptides have gained increasing attention for developing functional ingredients or foods with substantial antioxidant properties. This review explores the current production techniques for cereal peptidic antioxidants and their potential mechanism of action in the prevention and management of MetS.

Antioxidant activities of major tryptophyllin L peptides: A joint investigation of Gaussian-based 3D-QSAR and radical scavenging experiments

The red tree frog Litoria rubella from Australia has been studied for several decades showing that their dorsal skin glands secrete a number of small peptides containing a Pro-Trp sequence, known as tryptophyllin L peptides. Although peptides from many genera of Australian frogs have been reported to possess a variety of biological activities, the bioactivities of this peptide family have remained to be discovered. In this study, we investigated the antioxidant potency of a number of tryptophyllin L peptides for the first time using a joint statistical and experimental approach in which predictions based on Gaussian three-dimensional quantitative structure-activity relationship (3D-QSAR) models were employed to guide an in vitro experimental investigation. Two tryptophyllin tripeptides P-W-L (OH) and P-W-L (NH₂ ) were predicted to have the Trolox equivalent antioxidant capacity (TEAC) values of 0.80 and 0.87 μM Trolox/μM peptide, respectively. With those promising results, antioxidant capabilities of five tryptophyllin L peptides with the common core Pro-Trp-Leu were synthesized and subjected to 1,1-diphenyl-2-picrylhydrazyl (DPPH), ferric reducing ability of plasma (FRAP) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) radical cation (ABTS˙⁺ ) radical scavenging assays. The tests indicated that all the tested tryptophyllin L peptides, noticeably S-P-W-L (OH) and F-P-W-L (NH₂ ), are strong ABTS˙⁺ radical scavengers and moderate scavengers in the other two assays. The results, thus, suggested that the tryptophyllin L peptides are likely to be a part of the skin antioxidant system helping the frog to cope with drastic change in oxygen exposure and humidity, as they inhabit over a large area of Australia with a wide climate variation.

PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints

We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.

Advances on Food-Derived Peptidic Antioxidants-A Review

The oxidation process is considered to be the main reason behind human aging, human degenerative diseases and food quality degradation. Food-derived peptidic antioxidants (PAs) have wide sources and great activity, and have broad application prospects in removing excess reactive oxygen species in the body, anti-aging and preventing and treating diseases related to oxidative stress. On the other hand, PAs are expected to inhibit the lipid peroxidation of foods and increase the stability of the food system in the food industry. However, the production pathways and action mechanism of food-derived PAs are diverse, which makes it is difficult to evaluate the performance of PAs which is why the commercial application of PAs is still in its infancy. This article focuses on reviewing the preparation, purification, and characterization methods of food-derived PAs, and expounds the latest progress in performance evaluation and potential applications, in order to provide an effective reference for subsequent related research of PAs.

In silico design novel (5-imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine derivatives as inhibitors for glycogen synthase kinase 3 based on 3D-QSAR, molecular docking and molecular dynamics simulation

Glycogen Synthase Kinase 3 (GSK-3) is a member of cellular kinase with various functions, such as glucose regulation, cellular differentiation, neuronal function and cell apoptosis. It has been proved as an important therapeutic target in type 2 diabetes mellitus and Alzheimer's disease. To better understand their structure-activity relationships and mechanism of action, an integrated computational study, including three dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD), was performed on 79 (5-Imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine GSK-3 inhibitors. In this paper, we constructed 3D-QSAR using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) method. The results showed that the CoMFA model (q² = 0.743,  r² = 0.980) and the CoMSIA model (q² = 0.813,  r² = 0.976) had stable and reliable predictive ability. The electrostatic and H-bond donor fields play important roles in the models. The contour maps of the model visually showed the relationship between the activity of compounds and their three-dimensional structure. Molecular docking was used to identify the key amino acid residues at the active site of GSK-3 and explore its binding mode with ligands. Based on 3D-QSAR models, contour maps and the binding feature between GSK-3 and inhibitor, we designed 10 novel compounds with good potential activity and ADME/T profile. Molecular dynamics simulation results validated that Ile62, Val70 and Lys85 located in the active site play a key role for GSK-3 complexed with inhibitors. These results might provide important information for designing GSK-3 inhibitors with high activity.

In silico design of novel benzohydroxamate-based compounds as inhibitors of histone deacetylase 6 based on 3D-QSAR, molecular docking, and molecular dynamics simulations

In silico design of benzohydroxamate-based selective HDAC6 inhibitors.