Structural studies of a vasorelaxant lectin from Dioclea reflexa Hook seeds: Crystal structure, molecular docking and dynamics

Glucose-Binding Dioclea bicolor Lectin (DBL): Purification, Characterization, Structural Analysis, and Antibacterial Properties

In this study, we purified a lectin isolated from the seeds of Dioclea bicolor (DBL) via affinity purification. Electrophoresis analysis revealed that DBL had three bands, α, β, and γ chains, with molecular masses of approximately 29, 14, and 12 kDa, respectively. Gel filtration chromatography revealed that the native form of DBL had a molecular mass of approximately 100 kDa, indicating that it is a tetramer. Interestingly, DBL-induced hemagglutination was inhibited by several glucosides, mannosides, ampicillin, and tetracycline with minimum inhibitory concentration (MIC) values of 1.56-50 mM. Analysis of the complete amino acid sequence of DBL revealed the presence of 237 amino acids with high similarity to other Diocleinae lectins. Circular dichroism showed the prominent β-sheet secondary structure of DBL. Furthermore, DBL structure prediction revealed a Discrete Optimized Protein Energy (DOPE) score of -26,642.69141/Normalized DOPE score of -1.84041. The DBL monomer was found to consist a β-sandwich based on its 3D structure. Molecular docking showed the interactions between DBL and α-D-glucose, N-acetyl-D-glucosamine, α-D-mannose, α-methyl-D-mannoside, ampicillin, and tetracycline. In addition, DBL showed antimicrobial activity with an MIC of 125 μg/mL and exerted synergistic effects in combination with ampicillin and tetracycline (fractional inhibitory concentration index ≤ 0.5). Additionally, DBL significantly inhibited biofilm formation and showed no toxicity in murine fibroblasts (p < 0.05). These results suggest that DBL exhibits antimicrobial activity and works synergistically with antibiotics.

Mitigating Effect of Dietary Dioclea reflexa (Hook F) Seed Inclusion in Experimental Colon Carcinogenesis

Rats exposed to 72-hour intrarectal instillations of N-methyl-N-nitrosourea (MNU) were fed with Dioclea reflexa seed-included diets (0, 2.5, 5.0, and 10%). Following sacrifice, organs and blood were collected and analyzed for indices of oxidative stress and carcinogenesis using spectrophotometric, ELISA, histological, and immunohistochemical techniques. Dioclea reflexa seed-included diets significantly ( $p<0.05$ ) prevented MNU-induced elevation of carcinoembryonic antigen (CEA), malondialdehyde, and neutrophil-to-lymphocyte ratio (NLR) and boosted the activities of glutathione s-transferase, superoxide dismutase, and catalase. It also prevented MNU-induced colonic mucosal ulceration/interglandular inflammations and protected the mismatch repair gene, Mutl homolog1, against MNU-induced damage. There was a strong negative relationship between CEA, NLR, and the antioxidant enzyme activities, as well as total polyphenols, total flavonoids, and crude fiber, while CEA correlated positively with malondialdehyde levels. These results suggest that Dioclea reflexa seed is endowed with constituents possessing a potent capacity to mitigate oxidative stress, as well as the initiation and promotion of chemically induced colon carcinogenesis.

The lectin DrfL inhibits cell migration, adhesion and triggers autophagy-dependent cell death in glioma cells

Glioblastoma multiforme (GBM) is the most aggressive type of glioma, displaying atypical glycosylation pattern that may modulate signaling pathways involved in tumorigenesis. Lectins are glycan binding proteins with antitumor properties. The present study was designed to evaluate the antitumor capacity of the Dioclea reflexa lectin (DrfL) on glioma cell cultures. Our results demonstrated that DrfL induced morphological changes and cytotoxic effects in glioma cell cultures of C6, U-87MG and GBM1 cell lines. The action of DrfL was dependent upon interaction with glycans, and required a carbohydrate recognition domain (CRD), and the cytotoxic effect was apparently selective for tumor cells, not altering viability and morphology of primary astrocytes. DrfL inhibited tumor cell migration, adhesion, proliferation and survival, and these effects were accompanied by activation of p38MAPK and JNK (p46/54), along with inhibition of Akt and ERK1/2. DrfL also upregulated pro-apoptotic (BNIP3 and PUMA) and autophagic proteins (Atg5 and LC3 cleavage) in GBM cells. Noteworthy, inhibition of autophagy and caspase-8 were both able to attenuate cell death in GBM cells treated with DrfL. Our results indicate that DrfL cytotoxicity against GBM involves modulation of cell pathways, including MAPKs and Akt, which are associated with autophagy and caspase-8 dependent cell death.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Capturing Dioclea Reflexa Seed Bioactives on Halloysite Nanotubes and pH Dependent Release of Cargo against Breast (MCF-7) Cancers In Vitro

In this work, optimization parameters were developed to capture plant metabolites from Dioclea Reflexa (DR) seed ex-tracts onto halloysites nanotubes (HNTs). A one-step pool of the crude extracts at neutral pH from the HNT lumen failed to elicit a reduction in breast cancer, Michigan Cancer Foundation-7 (MCF-7) cell viability. However, the pH-dependent elution of metabolites revealed that the acidic pH samples exhibited profound antiproliferative effects on the cancer cells compared to the basic pH metabolites using both trypan blue dye exclusion assay and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) viability test. pH~5.2 samples demonstrated by half-maximal inhibitory concentration (IC50) of 0.8 mg and a cyclic voltammetry oxidation peak potential and current of 234 mV and 0.45 µA, respectively. This indicates that the cancer cells death could be attributed to membrane polarization/depolarization effects of the sample. Fluorescence-activated cell sorting (FACS) studies confirmed that the plant metabolites affected breast cancer apoptotic signaling pathways of cell death. The studies proved that plant metabolites could be captured using simplified screening procedures for rapid drug discovery purposes. Such procedures, however, would require the integration of affordable analytical tools to test and isolate individual metabolites. Our approach could be an important strategy to create a library and database of bioactive plant metabolites based on pH values.

In depth analysis on the carbohydrate-binding properties of a vasorelaxant lectin from Dioclea lasiophylla Mart Ex. Benth seeds

Lectins are a class of proteins or glycoproteins capable of recognizing and interacting with carbohydrates in a specific and reversible manner. Owing to this property, these proteins can interact with glycoconjugates present on the cell surface, making it possible to decipher the glycocode, as well as elicit biological effects, such as inflammation and vasorelaxation. Here, we report a structural and biological study of the mannose/glucose-specific lectin from Dioclea lasiophylla seeds, DlyL. The study aimed to evaluate in detail the interaction of DlyL with Xman and high-mannose N-glycans (MAN3, MAN5 and MAN9) by molecular dynamics (MD) and the resultant in vitro effect on vasorelaxation using rat aortic rings. In silico analysis of molecular docking was performed to obtain the initial coordinates of the DlyL complexes with the carbohydrates to apply as inputs in MD simulations. The MD trajectories demonstrated the stability of DlyL over time as well as different profiles of interaction with Xman and N-glycans. Furthermore, aortic rings assays demonstrated that the lectin could relax pre-contracted aortic rings with the participation of the carbohydrate recognition domain (CRD) and nitric oxide (NO) when endothelial tissue is preserved. These results confirm the ability of DlyL to interact with high-mannose N-glycans with its expanded CRD, supporting the hypothesis that DlyL vasorelaxant activity occurs primarily through its interaction with cell surface glycosylated receptors.Communicated by Ramaswamy H. Sarma.

ConBr lectin modulates MAPKs and Akt pathways and triggers autophagic glioma cell death by a mechanism dependent upon caspase-8 activation

Glioblastoma multiforme is the most aggressive type of glioma, with limited treatment and poor prognosis. Despite some advances over the last decade, validation of novel and selective antiglioma agents remains a challenge in clinical pharmacology. Prior studies have shown that leguminous lectins may exert various biological effects, including antitumor properties. Accordingly, this study aimed to evaluate the mechanisms underlying the antiglioma activity of ConBr, a lectin extracted from the Canavalia brasiliensis seeds. ConBr at lower concentrations inhibited C6 glioma cell migration while higher levels promoted cell death dependent upon carbohydrate recognition domain (CRD) structure. ConBr increased p38^MAPK and JNK and decreased ERK1/2 and Akt phosphorylation. Moreover, ConBr inhibited mTORC1 phosphorylation associated with accumulation of autophagic markers, such as acidic vacuoles and LC3 cleavage. Inhibition of early steps of autophagy with 3-methyl-adenine (3-MA) partially protected whereas the later autophagy inhibitor Chloroquine (CQ) had no protective effect upon ConBr cytotoxicity. ConBr also augmented caspase-3 activation without affecting mitochondrial function. Noteworthy, the caspase-8 inhibitor IETF-fmk attenuated ConBr induced autophagy and C6 glioma cell death. Finally, ConBr did not show cytotoxicity against primary astrocytes, suggesting a selective antiglioma activity. In summary, our results indicate that ConBr requires functional CRD lectin domain to exert antiglioma activity, and its cytotoxicity is associated with MAPKs and Akt pathways modulation and autophagy- and caspase-8- dependent cell death.

Heterologous production of α-chain of Dioclea sclerocarpa lectin: Enhancing the biological effects of a wild-type lectin

Lectins from Diocleinae subtribe species (family Leguminosae) are of special interest since they present a wide spectrum of biological activities, despite their high structural similarity. During their synthesis in plant cells, these proteins undergo post-translational processing resulting in the formation of three chains (α, β, γ), which constitute the lectins' subunits. Furthermore, such wild-type proteins are presented as isolectins or with different combinations of these chains, which undermine their biotechnological potential. Thus, the present study aimed to produce a recombinant form of the lectin from Dioclea sclerocarpa seeds (DSL), exclusively constituted by α-chain. The recombinant DSL (rDSL) was successfully expressed in E. coli BL21 (DE3) and purified by affinity chromatography (Sephadex G-50), showing a final yield of 74 mg of protein per liter of culture medium and specificity for D-mannose, α-methyl-mannoside and melibiose, unlike the wild-type protein. rDSL presented an effective vasorelaxant effect in rat aortas up to 100% and also interacted with glioma cells C6 and U87. Our results demonstrated an efficient recombinant production of rDSL in a bacterial system that retained some biochemical properties of the wild-type protein, showing wider versatility in sugar specificities and better efficacy in its activity in the biological models evaluated in this work.

Exploring the carbohydrate-binding ability of Canavalia bonariensis lectin in inflammation models

Lectins are a group of proteins of non-immune origin recognized for their ability to bind reversibly to carbohydrates. Researchers have been intrigued by oligosaccharides and glycoconjugates for their involvement as mediators of complex cellular events and then many biotechnological applications of lectins are based on glycocode decoding and their activities. Here, we report a structural and biological study of a ConA-like mannose/glucose-specific lectin from Canavalia bonariensis seeds, CaBo. More specifically, we evaluate the binding of CaBo with α-methyl-D-mannoside (MMA) and mannose-1,3-α-D-mannose (M13) and the resultant in vivo effects on a rat model of acute inflammation. A virtual screening was also carried out to cover a larger number of possible bindings of CaBo. In silico analysis demonstrated the stability of CaBo interaction with mannose-type ligands, and the lectin was able to induce acute inflammation in rats with the participation of the carbohydrate recognition domain (CRD) and histamine release. These results confirm the ability of CaBo to interact with hybrid and high-mannose N-glycans, supporting the hypothesis that CaBo's biological activity occurs primarily through its interaction with cell surface glycosylated receptors.

Dioclea violacea lectin modulates the gentamicin activity against multi-resistant strains and induces nefroprotection during antibiotic exposure

Gentamicin is an aminoglycoside antibiotic used to treat infections of various origins. In the last few decades, the constant use of gentamicin has resulted in increased bacterial resistance and nephrotoxicity in some cases. In this study, we examined the ability of Dioclea violacea lectin (DVL) in modulate the antimicrobial activity of gentamicin and reduce the nephrotoxicity induced by this drug. The minimum inhibitory concentration (MIC) obtained for DVL against all strains studied was not clinically relevant (MIC ≥ 1024 μg/mL). However, when DVL was combined with gentamicin, a significant increase in antibiotic action was observed against Staphylococcus aureus and Escherichia coli. DVL also reduced antibiotic tolerance in S. aureus during 10 days of continuous treatment. In addition, DVL presented a nephroprotective effect, reducing sodium excretion, N-Gal expression and urinary protein, that are important markers of glomerular and tubular injuries. Taken together, studies of inhibition of hemagglutinating activity, fluorescence spectroscopy and molecular docking revealed that gentamicin can interact with DVL via the carbohydrate recognition domain (CRD), suggesting that the results obtained in this study may be directly related to the interaction of DVL-gentamicin and with the ability of the lectin to interact with glycans present in the cells of the peritoneum.

Characterization, inhibitory activity and mechanism of polyphenols from faba bean (gallic-acid and catechin) on α-glucosidase: insights from molecular docking and simulation study

The chemo-profiling of ethanolic extract of faba beans seeds was performed and explored as an α-glucosidase inhibitor. The inhibition of α-glucosidase is one of the alternatives approach to control postprandial hyperglycemia by, resulting in the delay of the carbohydrate digestion of absorbable monosaccharides. Ethanolic seed extract showed phenolic compounds, flavonoid such as gallic acid (m/z [M- H] = 169.0124,C₇H₆O₅) ellagic acid derivatives epigallocatechin (m/z [M- H = 305.0644,C₁₅H₁₄O₇),catechin (m/z [M- H] = 289.0656,C₁₅H₁₄O₆), epigallocatechin gallate (m/z [M- H] = 457.0578,C₂₂H₁₈O₁₁) and epicatechin monogallate (m/z [M- H] = 441.081, C₂₂H₁₈O₁₀). The extract was found to exert inhibitory activity (88.28 ± 2.67%) (IC₅₀ value of 2.30 ± 0.032 mg/mL) with a mixed mode of inhibition (Km, apparent = 0.54 ± 0.020 mM and Vmax, apparent 0.136 ± 0.04 mM/min). Molecular docking studies of gallic acid and catechin on α-glucosidase proposed productive binding modes having binding energy (-6.58 kcal/mol and -7.25 kcal/mol) with an effective number of hydrogen bonds and binding energy. Tyr63, Arg197, Asp198, Glu 233, Asn324, Asp 326 of α-glucosidase participated in binding events with gallic acid and catechin. Molecular dynamics simulation studies were performed for both complexes i.e. gal:α-glucosidase and cat:α-glucosidase along with apo state of α-glucosidase, which revealed stable systems during the simulation. These findings of the present study may give an insight into the further development of the novel antidiabetic drug from the seeds of faba beans.

Overview of the Structure⁻Function Relationships of Mannose-Specific Lectins from Plants, Algae and Fungi

To date, a number of mannose-binding lectins have been isolated and characterized from plants and fungi. These proteins are composed of different structural scaffold structures which harbor a single or multiple carbohydrate-binding sites involved in the specific recognition of mannose-containing glycans. Generally, the mannose-binding site consists of a small, central, carbohydrate-binding pocket responsible for the "broad sugar-binding specificity" toward a single mannose molecule, surrounded by a more extended binding area responsible for the specific recognition of larger mannose-containing N-glycan chains. Accordingly, the mannose-binding specificity of the so-called mannose-binding lectins towards complex mannose-containing N-glycans depends largely on the topography of their mannose-binding site(s). This structure⁻function relationship introduces a high degree of specificity in the apparently homogeneous group of mannose-binding lectins, with respect to the specific recognition of high-mannose and complex N-glycans. Because of the high specificity towards mannose these lectins are valuable tools for deciphering and characterizing the complex mannose-containing glycans that decorate both normal and transformed cells, e.g., the altered high-mannose N-glycans that often occur at the surface of various cancer cells.

Machine Learning to Predict Binding Affinity

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.

ConA-Like Lectins: High Similarity Proteins as Models to Study Structure/Biological Activities Relationships

Lectins are a widely studied group of proteins capable of specific and reversible binding to carbohydrates. Undoubtedly, the best characterized are those extracted from plants of the Leguminosae family. Inside this group of proteins, those from the Diocleinae subtribe have attracted attention, in particular Concanavalin A (ConA), the best-studied lectin of the group. Diocleinae lectins, also called ConA-like lectins, present a high similarity of sequence and three-dimensional structure and are known to present inflammatory, vasoactive, antibiotic, immunomodulatory and antitumor activities, among others. This high similarity of lectins inside the ConA-like group makes it possible to use them to study structure/biological activity relationships by the variability of both carbohydrate specificity and biological activities results. It is in this context the following review aims to summarize the most recent data on the biochemical and structural properties, as well as biological activities, of ConA-like lectins and the use of these lectins as models to study structure/biological activity relationships.

Structural studies and nociceptive activity of a native lectin from Platypodium elegans seeds (nPELa)

A native lectin (nPELa), purified from seeds of the species Platypodium elegans, Dalbergieae tribe, was crystallized and structurally characterized by X-ray diffraction crystallography and bioinformatics tools. The obtained crystals diffracted to 1.6Å resolution, and nPELa structure were solved through molecular substitution. In addition, nPELa has a metal binding site and a conserved carbohydrate recognition domain (CRD) similar to other Dalbergieae tribe lectins, such as PAL (Pterocarpus angolensis) and CTL (Centrolobium tomentosum). Molecular docking analysis indicated high affinity of this lectin for different mannosides, mainly trimannosides, formed by α-1,3 or α-1,6 glycosidic bond, as evidenced by the obtained scores. In addition, molecular dynamics simulations were performed to demonstrate the structural behavior of nPELa in aqueous solution. In solution, nPELa was highly stable, and structural modifications in its carbohydrate recognition site allowed interaction between the lectin and the different ligands. Different modifications were observed during simulations for each one of the glycans, which included different hydrogen bonds and hydrophobic interactions through changes in the relevant residues. In addition, nPELa was evaluated for its nociceptive activity in mice and was reported to be the first lectin of the Dalbergieae tribe to show CRD-dependent hypernociceptive activity.