Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

Bioactivity predictions and virtual screening using machine learning predictive model

Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.

Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations

The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse side effects of existing DPP4 medications. The present study implemented a combined approach to machine learning and structure-based virtual screening to identify DPP4 inhibitors. Two ML models were trained based on DPP4 IC50 datasets. The ML models random forest (RF) and multilayer perceptron (MLP) neural network showed good accuracy, with the area under the curve being 0.93 and 0.91, respectively. The natural compound library was screened through ML models, and 1% (217) of compounds were selected for further screening. Structure-based virtual screening was performed along with positive control sitagliptin to obtain more specific and selective leads for DPP4. Based on binding affinity, drug-likeness properties, and interaction with DPP4, Z-614 and Z-997 compounds showed high binding affinity and specificity in the catalytic pocket of DPP4. Finally, the stability conformation of the DPP4 enzyme complex was checked by a molecular dynamics (MD) simulation. The MD simulation showed that both compounds bind better in the catalytic pocket, but the Z-614 compound altered the DPP4 native conformation. Therefore, Z-614 showed a high deviation in the backbone. This combined approach (ML and structure-based) study reported that Z-997 binds most stably to DPP4 in their catalytic pocket with a binding free energy of -70.3 kJ/mol, suggesting its therapeutic potential as a treatment option for T2DM disease.Communicated by Ramaswamy H. Sarma.

Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation

Hypoglycemic foods have attracted increasing research interest. This study prepared a hypoglycemic product from Bacillus subtilis fermented with Pyropia (PBP), which has promising industrial potential, and elucidated its hypoglycemic mechanism. The aqueous PBP solution was orange, with protein as the main functional component. In vivo experiments demonstrated that PBP could increase insulin secretion and inhibit α-glucosidase activity, resulting in a hypoglycemic effect superior to that of acarbose at the same dose. Molecular docking revealed that the peptides APPVDID, GPPDSPY, PPSSPRP, and SPPPPPA from PBP could inhibit both α-glucosidase and dipeptidyl peptidase-IV (DPP-IV) activities. Pro residues promoted PBP peptide binding to the hydrophobic pocket S1 of DPP-IV. Additionally, PBP reduced inflammation and promoted the growth of beneficial gut bacteria (Prevotellaceae_UCG_003, Lachnospiraceae_UCG_001). This study presents a novel approach for the high-value utilization of Pyropia and a new option for the production of hypoglycemic functional foods and medicines.

Hit Selection of Dipeptidyl Peptidase-4 Inhibitors Bearing Thieno[2,3-d]Pyrimidine Scaffold

The thieno[2,3-d]pyrimidine fragment is in the structure of many drug-like candidate derivatives with a wide range of biological activities. However, very few dipeptidyl peptidase-4 (DPP-4) inhibitors with this building block are currently known. Here, the selection of a novel DPP-4 inhibitor based on the thienopyrimidine scaffold is reported. In the performed study, ethyl 4-amino-5-methyl-2-(3-(trifluoromethyl)phenyl)thieno[2,3-d]pyrimidine-6-carboxylate (compound 22) demonstrated DPP-4 inhibitory potential about twice higher than that of the reference inhibitor diprotin A. The manner of inhibition is noncompetitive, a rare type among DPP-4 inhibitors. The molecular docking highlighted the importance of Ser349, Asn377, Glu378, Phe396, and Asp588 in forming the inhibitor/DPP-4 complex. Compound 22 might be useful as a source of ideas contributing to the design and further changes and optimizations of thieno[2,3-d]pyrimidine-based DPP-4 inhibitors.

Angular-Substituted [1,4]Thiazino[3,4-a]Isoquinolines: Biological Evaluation and In Silico Studies on DPP-IV Inhibition

Recent studies have discovered that aryl-substituted pyrido[2,1-a]isoquinolines have the potential to be highly active DPP IV inhibitors. In previous studies, we reported a novel synthetic approach for the construction of their sulfur-containing bioisosteric [1,4]thiazino[3,4-a]isoquinolines analogues, incorporating an additional aryl substituent. The present study aims to investigate the DPP IV inhibitory activity and cytotoxicity of the synthesized molecules by in vitro assay. The geometry optimization and molecular docking of the synthesized compounds were used to determine their binding modes to the active site of DPP IV. The docking analysis revealed that the energy-minimized poses of the studied compounds are close to the most important selectivity cliffs for DPP IV inhibition, forming hydrogen bonds and hydrophobic interactions with them. These results can be considered as a preliminary step towards further structural activity modifications.

A sensitive fluorescence assay of serum dipeptidyl peptidase IV activity to predict the suitability of its inhibitors in patients with type 2 diabetes mellitus

DPP-IV inhibitors, which are close to the natural hypoglycemic pathway of human physiology and have few side effects, have been extensively employed in the management of type 2 diabetes mellitus (T2DM). However, there are currently no specific blood indicators that can indicate or predict a patient's suitability for DPP-IV inhibitors. In this study, based on the self-developed high-specificity fluorescent substrate glycyl-prolyl-N-butyl-4-amino-1, 8-naphthimide (GP-BAN), a detection method of human serum DPP-IV activity was established and optimized. The method demonstrates a favorable lower limit of detection (LOD) at 0.32 ng/mL and a satisfactory lower limit of quantification (LOQ) of 1.12 ng/mL, and can be used for the detection of DPP-IV activity in trace serum (2 μL). In addition, Vitalliptin and Sitagliptin showed similar IC50 values when human recombinant DPP-IV and human serum were used as enzyme sources, and the intra-day and inter-day precision obtained by the microplate analyzer were less than 15 %. These results indicate that the microplate reader based detection technique has good accuracy, repeatability and reproducibility. A total of 700 volunteers were recruited, and 646 serum samples were tested for DPP-IV activity. The results showed that serum DPP-IV activity was higher in patients with T2DM than in controls (P < 0.01). However, the statistical data of family history of diabetes, gender and age of diabetic patients showed no statistical significance, and there was no contrast difference. The DPP-IV activity of serum in T2DM patients ranged from 2.4 μmol/min/L to 78.6 μmol/min/L, with a huge difference of up to 32-fold. These results suggest that it is necessary to test DPP-IV activity in patients with T2DM when taking DPP-IV inhibitors to determine the applicability of DPP-IV inhibitors in T2DM patients. These results suggest that it is necessary to detect the activity of DPP-IV in blood before taking DPP-IV inhibitors in patients with T2DM to judge the applicability of DPP-IV inhibitors in patients with T2DM.

Bis(benzimidazol-2-yl)amine-Based DPP-4 Inhibitors Potentially Suitable for Combating Diabetes and Associated Nervous System Alterations

Bis(benzimidazol-2-yl)amine scaffold is not present in dipeptidyl peptidase-4 (DPP-4) inhibitors published so far. Herein, the inhibitory potential of bis(benzimidazol-2-yl)amine derivatives against DPP-4 was evaluated. In non-competitive inhibition mode, three representatives 5, 6, and 7 inhibited DPP-4 in vitro with IC50 values below 50 μM. The assessed binding pocket of DPP-4 for these benzimidazoles includes the S2 extensive subsite's residues Phe357 and Arg358. None of the lead compounds showed cytotoxicity to human neuroblastoma SH-SY5Y cells at concentrations lower than 10 μM. None showed significant binding affinity at dopamine D2, D3, and histamine H1, H3 receptors, at concentrations lower than 10 μM, leading to preferable outcomes due to mutually opposite effects of these neurotransmitters on each other. The potential beneficial effects on dopamine synthesis and the survival of dopaminergic neurons could be mediated by DPP-4 inhibition. These effective noncompetitive DPP-4 inhibitors, with inhibitory potential better than reference diprotin A (relative inhibitory potency compared to diprotin A is 3.39 and 1.54 for compounds 7 and 5, respectively), with the absence of cytotoxicity to SH-SY5Y cells, are valuable candidates for further evaluation for the treatment of diabetes and associated disruption of neuronal homeostasis.

Precooked Jack Bean [Canavalia ensiformis (L.) DC] Sprout: Generation of Dipeptidyl Peptidase-IV Inhibitory Peptides during Simulated Digestion

Bioactive peptides generated from jack bean sprouts are reported to function as dipeptidyl peptidase IV (DPP-IV) inhibitors. However, no studies have investigated the effect of precooking followed by simulated digestion using pepsin-pancreatin to increase DPP-IV inhibitory peptide generation in jack bean sprouts. Therefore, the present study aimed to explore the generation of DPP-IV inhibitory peptides from precooked jack bean [Canavalia ensiformis (L.) DC] sprouts during simulated digestion with pepsin-pancreatin. The results showed that peptide fractions of the sample hydrolysate with molecular weight <1 kDa exhibited the strongest DPP-IV inhibitory activity (84.77%±0.49%) after simulated digestion. This activity was slightly greater than that (74.12%±0.85%) observed prior to simulated digestion. These findings demonstrate that the DPP-IV inhibitory activity of precooked jack bean sprouts can be retained following simulated digestion. Moreover, our investigation revealed the sequences of two novel peptides following simulated digestion with critical amino acids. The presence of alanine and glycine at the penultimate N-terminus of AAGPKP and LGDLLK confirmed the presence of DPP-IV inhibitors. Both peptide sequences are nontoxic and interact with the catalytic sites of enzymes through hydrogen bonds.

DPP-IV Inhibitory Peptide against In Vitro Gastrointestinal Digestion Derived from Goat's Milk Protein and Its Activity Enhancement via Amino Acid Substitution

Goat milk protein can release a variety of bioactive peptides after digestion, while most of them are digested into free amino acids or dipeptides via the GI tract. We investigated the peptide profiles of goat milk protein following in vitro gastrointestinal digestion using LC-MS/MS and identified 683 bioactive peptides, including 105 DPP-IV inhibitory peptides. Among these peptides, ILDKVGINY (IL), derived from β-lactoglobulin, was found to be high in content and resistance to digestion. Herein, we explore the effect of amino acid residue substitution at the second N-terminus on its DPP-IV inhibitory activity. Three 9 polypeptide fragments (peptide IL, IP, and II) were synthesized and subjected to molecular docking and activity analysis. The peptide IL demonstrated the highest affinity for DPP-IV with a binding energy of -8.4 kcal/mol and a moderate IC50 value of 1.431 mg/mL determined based on the Caco-2 cell model. The replacement of specific amino acid residues by Pro and Leu led to an increase in the hydrophobic force interaction between the inhibitor peptide and DPP-IV. The inhibition rates of the three peptides were significantly different (p < 0.05). Peptide II containing an Ile residue instead of Leu resulted in a significant enhancement of DPP-IV inhibitory activity, with an IC50 value of 0.577 mg/mL. The GRAVY changes in the three peptides were consistent with the trend of the inhibitory rates. Therefore, the GRAVY of peptides and branch-chain amino acids should be considered in its activity improvement. The present study revealed the presence and activity of DPP-IV inhibitory peptides in goat milk, providing important insights for further investigation of their potential food functionality and health benefits.

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Encapsulation of Tenebrio molitor Hydrolysate with DPP-IV Inhibitory Activity by Electrospraying and Spray-Drying

This study investigates the encapsulation of Tenebrio molitor hydrolysate exhibiting DPP-IV inhibitory activity by spray-drying and electrospraying techniques. First, we optimized the feed formulation and processing conditions required to obtain nano-microcapsules by electrospraying when using Arabic gum as an encapsulating agent and pullulan and Tween 20 as additives. The optimum formulation was also dried by spray-drying, where the removal of the additives was also assayed. Morphology analysis reveals that electrosprayed capsules have a smaller size (1.2 ± 0.5 µm vs. 12.4 ± 8.7 µm) and greater uniformity compared to those obtained by spray-drying. Regarding the surface nitrogen content and DPP-IV inhibitory activity, our results show no significant difference between the electrosprayed capsules and spray-dried capsules containing additives (IC50 of ~1.5 mg protein/mL). Therefore, it was concluded that adding additives during spray-drying allows for a similar encapsulation efficiency and reduced degradation during processing, as achieved by electrospraying technique but providing higher productivity. On the other hand, spray-dried capsules without additives displayed a higher surface nitrogen content percentage, which was mainly due to the absence of Tween 20 in the feed formulation. Consequently, these capsules presented a higher IC50 value (IC50 of 1.99 ± 0.03 mg protein/mL) due to the potential degradation of surface-exposed peptides.

Computational Modeling of the Interactions between DPP IV and Hemorphins

Type 2 diabetes is a chronic metabolic disorder characterized by high blood glucose levels due to either insufficient insulin production or ineffective utilization of insulin by the body. The enzyme dipeptidyl peptidase IV (DPP IV) plays a crucial role in degrading incretins that stimulate insulin secretion. Therefore, the inhibition of DPP IV is an established approach for the treatment of diabetes. Hemorphins are a class of short endogenous bioactive peptides produced by the enzymatic degradation of hemoglobin chains. Numerous in vitro and in vivo physiological effects of hemorphins, including DPP IV inhibiting activity, have been documented in different systems and tissues. However, the underlying molecular binding behavior of these peptides with DPP IV remains unknown. Here, computational approaches such as protein-peptide molecular docking and extensive molecular dynamics (MD) simulations were employed to identify the binding pose and stability of peptides in the active site of DPP IV. Findings indicate that hemorphins lacking the hydrophobic residues LVV and VV at the N terminal region strongly bind to the conserved residues in the active site of DPP IV. Furthermore, interactions with these critical residues were sustained throughout the duration of multiple 500 ns MD simulations. Notably, hemorphin 7 showed higher binding affinity and sustained interactions by binding to S1 and S2 pockets of DPP IV.

Identification, characterization, and insights into the mechanism of novel dipeptidyl peptidase-IV inhibitory peptides from yak hemoglobin by in silico exploration, molecular docking, and in vitro assessment

Dipeptidyl peptidase IV (DPP-IV) inhibitory peptides were screened and identified from yak hemoglobin for the first time by in silico analysis, molecular docking, and in vitro evaluation. Results showed that yak hemoglobin had a high potential to produce DPP-IV inhibitory peptides based on the sequence alignment and bioactive potential evaluation. Furthermore, "pancreatic elastase + stem bromelain" was the optimal combined-enzymatic strategy by simulated proteolysis. Additionally, 25 novel peptides were found from its simulated hydrolysate, among which 10 peptides had high binding affinities with DPP-IV by molecular docking. Most of these peptides were also in silico characterized with favorable physicochemical properties and biological potentials, including relatively low molecular weight, high hydrophobicity, several net charges, good water solubility, nontoxicity, acceptable sensory quality, and good human intestinal absorption. Finally, six novel DPP-IV inhibitory peptides were identified via in vitro assessment, among which EEKA (IC50 = 235.26 μM), DEV (IC50 = 339.45 μM), and HCDKL (IC50 = 632.93 μM) showed the strongest capacities. The hydrogen bonds and electrostatic attractions formed with core residues within the S2 pocket of DPP-IV could be mainly responsible for their inhibition performances. This work provided a time-saving method and broadened application for yak by-products development as sources of functional foods.

Formation of dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides from Jack Bean (Canavalia ensiformis (L.) DC.) sprout in simulated digestion

Bean sprouts are potential plant proteins that produce DPP-IV inhibitory peptides. These peptides must be stable and active in the brush border membrane of the small intestine to inhibit DPP-IV. The purpose of this research is to evaluate the DPP-IV inhibitory activity of jack bean sprouts using pepsin-pancreatin during simulated digestion, as well as the absorption of these peptides through the everted gut sac method. The results showed that after 180 min of digestion simulation, the Mw < 1 kDa peptide fraction of jack bean hydrolysate, which germinated for 60 h (HG60), had the highest inhibitory activity. The duodenum absorbs most of the peptides with inhibitory activity of 61.77%, which is slightly lower than activity after digestion (62.19%). These outcomes suggest that the DPP-IV inhibitory activity of HG60 can be maintained after digestion and absorption. Two novel peptides KAVGDPI and QGVVLRP identified after absorption contain crucial amino acids confirming as DPP-IV inhibitor.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01343-9.

GLP-1/GIP Agonist as an Intriguing and Ultimate Remedy for Combating Alzheimer's Disease through its Supporting DPP4 Inhibitors: A Review

BACKGROUND

Alzheimer's disease (AD) is a widespread neurological illness in the elderly, which impacted about 50 million people globally in 2020. Type 2 diabetes has been identified as a risk factor. Insulin and incretins are substances that have various impacts on neurodegenerative processes. Preclinical research has shown that GLP-1 receptor agonists decrease neuroinflammation, tau phosphorylation, amyloid deposition, synaptic function, and memory formation. Phase 2 and 3 studies are now occurring in Alzheimer's disease populations. In this article, we present a detailed assessment of the therapeutic potential of GLP-1 analogues and DPP4 inhibitors in Alzheimer's disease.

AIM

This study aimed to gain insight into how GLP-1 analogues and associated antagonists of DPP4 safeguard against AD.

METHODS

This study uses terms from search engines, such as Scopus, PubMed, and Google Scholar, to explore the role, function, and treatment options of the GLP-1 analogue for AD.

RESULTS

The review suggested that GLP-1 analogues may be useful for treating AD because they have been linked to anti-inflammatory, neurotrophic, and neuroprotective characteristics. Throughout this review, we discuss the underlying causes of AD and how GLP signaling functions.

CONCLUSION

With a focus on AD, the molecular and pharmacological effects of a few GLP-1/GIP analogs, both synthetic and natural, as well as DPP4 inhibitors, have been mentioned, which are in the preclinical and clinical studies. This has been demonstrated to improve cognitive function in Alzheimer's patients.

Development of Long-Acting Dipeptidyl Peptidase-4 Inhibitors: Structural Evolution and Long-Acting Determinants

Considerable effort has been made to achieve less frequent dosing in the development of DPP-4 inhibitors. Enthusiasm for long-acting DPP-4 inhibitors is based on the promise that such agents with less frequent dosing regimens are associated with improved patient adherence, but the rational design of long-acting DPP-4 inhibitors remains a major challenge. In this Perspective, the development of long-acting DPP-4 inhibitors is comprehensively summarized to highlight the evolution of initial lead compounds on the path toward developing long-acting DPP-4 inhibitors over nearly three decades. The determinants for long duration of action are then examined, including the nature of the target, potency, binding kinetics, crystal structures, selectivity, and preclinical and clinical pharmacokinetic and pharmacodynamic profiles. More importantly, several possible approaches for the rational design of long-acting drugs are discussed. We hope that this information will facilitate the design and development of safer and more effective long-acting DPP-4 inhibitors and other oral drugs.

Food-derived dipeptidyl peptidase IV inhibitory peptides: Production, identification, structure-activity relationship, and their potential role in glycemic regulation

Dipeptidyl Peptidase IV (DPP-IV) inhibitory peptides are attracting increasing attention, owing to their potential role in glycemic regulation by preventing the inactivation of incretins. However, few reviews have summarized the current understanding of DPP-IV inhibitory peptides and their knowledge gaps. This paper reviews the production, identification and structure-activity relationships (SAR) of DPP-IV inhibitory peptides. Importantly, their bioavailability and hypoglycemic effects are critically discussed. Unlike the traditional method to identifying peptides after separation step by step, the bioinformatics approach identifies peptides via virtual screening that is more convenient and efficient. In addition, the bioinformatics approach was also used to investigate the SAR of peptides. Peptides with proline (Pro) or alanine (Ala) residue at the second position of N-terminal are exhibit strong DPP-IV inhibitory activity. Besides, the bioavailability of DPP-IV inhibitory peptides is related to their gastrointestinal stability and cellular permeability, and in vivo studies showed that the glucose homeostasis has been improved by these peptides. Especially, the intestinal transport of DPP-IV inhibitory peptides and cell biological assays used to evaluate their potential role in glycemic regulation are innovatively summarized. For further successful development of DPP-IV inhibitory peptides in glycemic regulation, future study should elucidate their SAR and in vivo hypoglycemic effects .

Rapid Screening of Novel Dipeptidyl Peptidase-4 Inhibitory Peptides from Pea (Pisum sativum L.) Protein Using Peptidomics and Molecular Docking

Pea protein hydrolysates (PPHs) possess good hypoglycemic effects; however, their dipeptidyl peptidase-4 (DPP-4) inhibitory activity is poorly understood, and none of the DPP-4 inhibitory peptides have been identified from PPHs. This paper aims to rapidly screen these peptides from PPHs by combining peptidomics and molecular docking. In this study, 543 peptides were identified by peptidomics, and four peptides (IPYWTY, IPYWT, LPNYN, and LAFPGSS) with DPP-4 half-maximal inhibitory concentration (IC50) values <100 μM were screened for the first time. Significantly, peptide IPYWTY exhibited the most potent DPP-4 inhibitory activity (IC50 = 11.04 μM) mainly because it formed hydrophobic interactions with the S1 pocket in DPP-4. These results indicated that combining peptidomics and molecular docking is an effective strategy for rapidly screening DPP-4 inhibitory peptides.

Drug Chemical Space as a Guide for New Herbicide Development: A Cheminformatic Analysis

Herbicides are critical resources for meeting agricultural demand. While similar in structure and function to pharmaceuticals, the development of new herbicidal mechanisms of action and new scaffolds against known mechanisms of action has been much slower than in pharmaceutical sciences. We hypothesized that this may be due in part to a relative undersampling of possible herbicidal chemistries and set out to test whether this difference in sampling existed and whether increasing the diversity of possible herbicidal chemistries would be likely to result in more efficacious herbicides. To conduct this work, we first identified databases of commercially available herbicides and clinically approved pharmaceuticals. Using these databases, we created a two-dimensional embedding of the chemical, which provides a qualitative visualization of the degree to which each chemotype is distributed within the combined chemical space and shows a moderate degree of overlap between the two sets. Next, we trained several machine learning models to classify herbicides versus drugs based on physicochemical characteristics. The most accurate of these models has an accuracy of 93% with the key differentiating characteristics being the number of polar hydrogens, number of amide bonds, LogP, and polar surface area. We then used several types of scaffold decomposition to quantitatively evaluate the chemical diversity of each molecular family and showed herbicides to have considerably fewer unique structural fragments. Finally, we used molecular docking as an in silico evaluation of further structural diversification in herbicide development. To this end, we identified herbicides with well-characterized binding sites and modified those scaffolds based on similar structural subunits from the drug dataset not present in any commercial herbicide while using the machine-learned model to ensure that required herbicide properties were maintained. Redocking the original and modified scaffolds of several herbicides showed that even this simple design strategy is capable of yielding new molecules with higher predicted affinity for the target enzymes. Overall, we show that herbicides are distinct from drugs based on physicochemical properties but less diverse in their chemistry in a way not governed by these properties. We also demonstrate in silico that increasing the diversity of herbicide scaffolds has the potential to increase potency, potentially reducing the amount needed in agricultural practice.

Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum

Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC₅₀ values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

Identification and structure-activity relationship exploration of uracil-based benzoic acid and ester derivatives as novel dipeptidyl Peptidase-4 inhibitors for the treatment of type 2 diabetes mellitus

Our previously reported carboxyl-containing DPP-4 inhibitors were highly potent but were poorly bioavailable. Esters of the carboxyl analogs exhibited a significant DPP-4 potency loss albeit with enhanced oral absorption. Herein, we described identification and structure-activity relationship (SAR) exploration of a novel series of benzoic acid and ester derivatives as low single-digit nanomolar DPP-4 inhibitors. Importantly, the esters displayed comparable activities to the acids counterparts. Molecular simulation revealed that ester adopts a similar binding mode to acid. Moreover, the selected esters and acids demonstrated high selectivity and low cytotoxicity, as well as good metabolic stability. And more importantly, the esters possessed excellent pharmacokinetic profiles for oral administration. The best compound ester 19b demonstrated long DPP-4 inhibition in vivo, and robustly improved the glucose tolerance in normal and db/db mice while ensuring glucose-lowering potency in chronic treatment. Our results supported that the compound 19b can be served as a potential candidate for the treatment of type 2 diabetes.

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.

Discovery, synthesis and in combo studies of Schiff's bases as promising dipeptidyl peptidase-IV inhibitors

Diabetes mellitus is a main global health apprehension. Macrovascular illnesses, neuropathy, retinopathy, and nephropathy are considered some of its severe hitches. Gliptins are a group of hypoglycemic agents that inhibit dipeptidyl peptidase-IV (DPP-IV) enzyme and support blood glucose-lowering effect of incretins. In the current research, synthesis, characterization, docking, and biological evaluation of fourteen Schiff's bases 5a-f and 9a-h were carried out. Compound 9f revealed the best in vitro anti-DPP-IV activity of 35.7% inhibition at a concentration of 100 μM. Compounds 9c and 9f with the highest in vitro DPP-IV inhibition were subjected to the in vivo glucose-lowering test using vildagliptin as a positive inhibitor. Vildagliptin, 9c, and 9f showed significant reduction in the blood glucose levels of the treated mice after 30 min of glucose administration. Moreover, induced fit docking showed that these derivatives accommodated the enzyme binding site with comparable docking scores. Schiff's bases can serve as promising lead for the development of new DPP-IV inhibitors.

Structure-Activity Relationship Analysis of Cocrystallized Gliptin-like Pyrrolidine, Trifluorophenyl, and Pyrimidine-2,4-Dione Dipeptidyl Peptidase-4 Inhibitors

Approved and potent reported dipeptidyl peptidase-4 (DPP-4) inhibitors with gliptin-like structures are classified here according to their structures and mechanisms of the inhibition in three groups: (i) those with pyrrolidine or analogs as P1 fragment with α-aminoacyl linker, (ii) structures with trifluorophenyl moiety or analogs as P1 fragment with β-aminobutanoyl linker, and (iii) DPP-4 inhibitors with pyrimidine-2,4-dione or analogs as P1' fragment. The structure-activity relationship analysis was performed for those whose cocrystallized structures with the enzyme were published. While inhibitors with pyrrolidine and trifluorophenyl moiety or analogs as P1 fragment bind in a similar way in S1, S2 and S2 extensive domains of the enzyme, the binding mode of pyrimidine-2,4-dione derivatives/analogs differs with additional interactions in S1' and S2' pockets. Three general schemes of fragmented gliptins and gliptin-like structures with the enzyme and protein-ligand interaction fingerprints were made, which might be useful in the creation of DPP-4 inhibitor's design strategies.

Novel Potent and Selective DPP-4 Inhibitors: Design, Synthesis and Molecular Docking Study of Dihydropyrimidine Phthalimide Hybrids

BACKGROUND

Dipeptidyl peptidase-4 (DPP-4) inhibitors have emerged as anti-hyperglycemic agents that improve glycemic control in type 2 diabetic patients, either as monotherapy or in combination with other antidiabetic drugs.

METHODS

A novel series of dihydropyrimidine phthalimide hybrids was synthesized and evaluated for their in vitro and in vivo DPP-4 inhibition activity and selectivity using alogliptin as reference. Oral glucose tolerance test was assessed in type 2 diabetic rats after chronic treatment with the synthesized hybrids ± metformin. Cytotoxicity and antioxidant assays were performed. Additionally, molecular docking study with DPP-4 and structure activity relationship of the novel hybrids were also studied.

RESULTS

Among the synthesized hybrids, 10g, 10i, 10e, 10d and 10b had stronger in vitro DPP-4 inhibitory activity than alogliptin. Moreover, an in vivo DPP-4 inhibition assay revealed that 10g and 10i have the strongest and the most extended blood DPP-4 inhibitory activity compared to alogliptin. In type 2 diabetic rats, hybrids 10g, 10i and 10e exhibited better glycemic control than alogliptin, an effect that further supported by metformin combination. Finally, 10j, 10e, 10h and 10d had the highest radical scavenging activity in DPPH assay.

CONCLUSIONS

Hybrids 10g, 10i and 10e are potent DPP-4 inhibitors which may be beneficial for T2DM treatment.

DPP-4 Cleaves α/β-Peptide Bonds: Substrate Specificity and Half-Lives

The incorporation of β-amino acids into a peptide sequence has gained particular attention as β- and α/β-peptides have shown remarkable proteolytic stability, even after a single homologation at the scissile bond. Several peptidases have been shown to cleave such bonds with high specificity but at a much slower rate compared to α-peptide bonds. In this study, a series of analogs of dipeptidyl peptidase-4 (DPP-4) substrate inhibitors were synthesized in order to investigate whether β-amino acid homologation at the scissile bond could be a valid approach to improving peptide stability towards DPP-4 degradation. DPP-4 cleaved the α/β-peptide bond after the N-terminal penultimate Pro with a broad specificity and retained full activity regardless of the β³ -amino acid side chain and peptide length. Significantly improved half-lives were observed for β³ Ile-containing peptides. Replacing the penultimate Pro with a conformationally constrained Pro mimetic led to proteolytic resistance. DPP-4 cleavage of α/β-peptide bonds with a broad promiscuity represents a new insight into the stability of peptide analogs containing β-amino acids as such analogs were thought to be stable towards enzymatic degradation.

Benzo[4,5]thieno[2,3-d]pyrimidine phthalimide derivative, one of the rare noncompetitive inhibitors of dipeptidyl peptidase-4

A small library of benzo[4,5]thieno[2,3-d]pyrimidine phthalimide and amine derivatives was evaluated for inhibitory activity against dipeptidyl peptidase-4 (DPP-4). The phthalimide derivatives exhibited better activity than the amine precursors, with 2-(2-(3-chlorobenzyl)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4-yl)isoindoline-1,3-dione (compound 14) as the most effective inhibitor (IC₅₀  = 34.17 ± 5.11 μM). The five most potent selected inhibitors did not show cytotoxicity to a greater extent on Caco-2 cells, even at a concentration of 250 μM. Compound 14 is considered as a novel representative of the rare noncompetitive DPP-4 inhibitors. Molecular docking and dynamics simulation indicated the importance of the Tyr547, Lys554, and Trp629 residues of DPP-4 in the formation of the enzyme-inhibitor complex. These observations could be potentially utilized for the rational design and optimization of novel (structurally similar, with phthalimide moiety, or different) noncompetitive DPP-4 inhibitors, which are anyway rare, but favorable in terms of the saturation of substrate competition.

In Vitro Metabolic Stability of a Casein-Derived Dipeptidyl Peptidase-IV (DPP-IV) Inhibitory Peptide VPYPQ and Its Controlled Release from Casein by Enzymatic Hydrolysis

The aim of this study was to investigate the dipeptidyl peptidase-IV (DPP-IV) inhibition and metabolic stability of a casein-derived peptide Val-Pro-Tyr-Pro-Gln (VPYPQ) and its fragments as well as their release from casein following hydrolysis. Results showed that VPYPQ was the most potent DPP-IV inhibitory peptide among them with an IC₅₀ value of 41.45 μM. This might be due to its two internal Pro residues at positions 2 and 4. Moreover, VPYPQ was resistant to hydrolysis by gastrointestinal enzymes and was relatively more stable to hydrolysis by DPP-IV and peptidases in plasma compared with its fragments. Additionally, oral administration of VPYPQ at a dose of 90 μmol/kg body weight could reduce the postprandial blood glucose levels in mice. More importantly, VPYPQ could be released efficiently from casein following hydrolysis by a combination of papain and in vitro digestion, reaching up to 3211.15 μg/g. Therefore, VPYPQ was a promising casein-derived DPP-IV inhibitor.

Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods

Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning–based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein–ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein–ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning–based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.

Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Bioactive Peptides from Germinated Soybean with Anti-Diabetic Potential by Inhibition of Dipeptidyl Peptidase-IV, α-Amylase, and α-Glucosidase Enzymes

Functional foods containing peptides offer the possibility to modulate the absorption of sugars and insulin levels to prevent diabetes. This study investigates the potential of germinated soybean peptides to modulate postprandial glycaemic response through inhibition of dipeptidyl peptidase IV (DPP-IV), salivary α-amylase, and intestinal α-glucosidases. A protein isolate from soybean sprouts was digested by pepsin and pancreatin. Protein digest and peptide fractions obtained by ultrafiltration (<5, 5⁻10 and >10 kDa) and subsequent semipreparative reverse phase liquid chromatography (F1, F2, F3, and F4) were screened for in vitro inhibition of DPP-IV, α-amylase, maltase, and sucrase activities. Protein digest inhibited DPP-IV (IC50 = 1.49 mg/mL), α-amylase (IC50 = 1.70 mg/mL), maltase, and sucrase activities of α-glucosidases (IC50 = 3.73 and 2.90 mg/mL, respectively). Peptides of 5⁻10 and >10 kDa were more effective at inhibiting DPP-IV (IC50 = 0.91 and 1.18 mg/mL, respectively), while peptides of 5⁻10 and <5 kDa showed a higher potency to inhibit α-amylase and α-glucosidases. Peptides in F1, F2, and F3 were mainly fragments from β-conglycinin, glycinin, and P34 thiol protease. The analysis of structural features of peptides in F1⁻F3 allowed the tentative identification of potential antidiabetic peptides. Germinated soybean protein showed a promising potential to be used as a nutraceutical or functional ingredient for diabetes prevention.

In Silico Approaches Applied to the Study of Peptide Analogs of Ile-Pro-Ile in Relation to Their Dipeptidyl Peptidase IV Inhibitory Properties

Inhibition of dipeptidyl peptidase IV (DPP-IV) may be exploited to maintain the incretin effect during the postprandial phase. As a result, glycemic regulation and energy homeostasis may be improved. Food protein-derived peptides have been identified as natural agents capable of inhibiting DPP-IV. Ile-Pro-Ile is the most potent DPP-IV inhibitory peptide identified to date. A minimum analog peptide set approach was used to study peptide analogs of Ile-Pro-Ile. The DPP-IV half maximal inhibitory concentration (IC50) values of the 25 peptides evaluated ranged from 3.9 ± 1.0 µM (Ile-Pro-Ile) to 247.0 ± 32.7 µM (Phe-Pro-Phe). The presence of Pro at position 2 of tripeptides was required to achieve high DPP-IV inhibition. Most peptides behaved as competitive inhibitors of DPP-IV with the exception of peptides with a N-terminal Trp, which were mixed-type inhibitors. While possessing the structure of preferred DPP-IV substrates, most peptides studied were particularly stable during 30 min incubation with DPP-IV. Molecular docking revealed that Ile-Pro-Ile and its peptide analogs interacted in a very similar manner with the active site of DPP-IV. In addition, no correlation was found between the Hydropathic INTeraction score and the DPP-IV IC50 values of the peptides studied. This outcome suggests that free energy may not be directly responsible for enzyme inhibition by the peptides. Finally, novel DPP-IV inhibitory peptides were identified using the strategy employed herein. These results may be relevant for the development of food protein-derived peptides with serum glucose lowering and food intake regulatory properties in humans.