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

Cavada, Benildo S.; Araripe, David Alencar; Silva, Ivanice Bezerra; Pinto-Junior, Vanir Reis; Osterne, Vinicius Jose Da Silva; Neco, Antonio Hadson Bastos; Laranjeira, Eva Pollyanna Peixe; Lossio, Claudia Figueiredo; Correia, Jorge Luis Almeida; Pires, Alana F.; Assreuy, Ana Maria Sampaio; Nascimento, Kyria S.

doi:10.1016/j.ijbiomac.2017.08.174

Cited by 11 publications

(6 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A-K correspond to lectins used as structural scaffolds (( A ): Con A, ( B ): LcA, ( C ): BanLec, ( D ): Morniga-M, ( E ): PPL, ( F ): Heltuba, ( G ): Gastrodianin, ( H ): ASA, ( I ): GNA, ( J ): Nictaba, ( K ): IraB. References [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ,…”

Section: Figureunclassified

Man-Specific Lectins from Plants, Fungi, Algae and Cyanobacteria, as Potential Blockers for SARS-CoV, MERS-CoV and SARS-CoV-2 (COVID-19) Coronaviruses: Biomedical Perspectives

Barre

Damme

Simplicien

et al. 2021

Cells

View full text Add to dashboard Cite

Betacoronaviruses, responsible for the “Severe Acute Respiratory Syndrome” (SARS) and the “Middle East Respiratory Syndrome” (MERS), use the spikes protruding from the virion envelope to attach and subsequently infect the host cells. The coronavirus spike (S) proteins contain receptor binding domains (RBD), allowing the specific recognition of either the dipeptidyl peptidase CD23 (MERS-CoV) or the angiotensin-converting enzyme ACE2 (SARS-Cov, SARS-CoV-2) host cell receptors. The heavily glycosylated S protein includes both complex and high-mannose type N-glycans that are well exposed at the surface of the spikes. A detailed analysis of the carbohydrate-binding specificity of mannose-binding lectins from plants, algae, fungi, and bacteria, revealed that, depending on their origin, they preferentially recognize either complex type N-glycans, or high-mannose type N-glycans. Since both complex and high-mannose glycans substantially decorate the S proteins, mannose-specific lectins are potentially useful glycan probes for targeting the SARS-CoV, MERS-CoV, and SARS-CoV-2 virions. Mannose-binding legume lectins, like pea lectin, and monocot mannose-binding lectins, like snowdrop lectin or the algal lectin griffithsin, which specifically recognize complex N-glycans and high-mannose glycans, respectively, are particularly adapted for targeting coronaviruses. The biomedical prospects of targeting coronaviruses with mannose-specific lectins are wide-ranging including detection, immobilization, prevention, and control of coronavirus infection.

show abstract

Section: Figureunclassified

Man-Specific Lectins from Plants, Fungi, Algae and Cyanobacteria, as Potential Blockers for SARS-CoV, MERS-CoV and SARS-CoV-2 (COVID-19) Coronaviruses: Biomedical Perspectives

Barre

Damme

Simplicien

et al. 2021

Cells

View full text Add to dashboard Cite

show abstract

“…These data allows us to open perspectives for even more to be discovered about the interactions of lectins with cellular structures involved in in ammation. It has been observed for lectins isolated from the Dalbergieae tribe (mannoside a nity) the ability to bind glycan structures involved in cell-cell recognition process by molecular docking [44][45][46]. Studies with a lectin-like domain of TNF-α have shown improve in lung function in transplanted rats, including the of reduction in reactive oxygen species [47].…”

Section: Discussionmentioning

confidence: 99%

The anti-inflamatory effect of Andira anthelmia lectin in rats involves inhibition of the prostanoid pathway, TNF-α and lectin domain

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective: To investigate the effect and mechanisms of Andira anthelmia lectin in rat models of acute in ammation.Material: AAL anti-in ammatory activity was evaluated in Wistar rat models of paw edema, peritonitis and hyperalgesia.Methods: AAL (0.01-1 mg/kg i.v.) was injected 30 min before stimulation with carrageenan and with initial and late phase in ammatory mediators into the animals paw and peritoneum for evaluation of: cell migration (optical and intravital microscopy), paw edema (plethysmometry and histopathology); hyperalgesia (analgesimetry).Results: AAL inhibited leukocyte migration induced by carrageenan, mainly neutrophils to the peritoneal uid, decreasing leukocyte adhesion. In the peritoneal uid, AAL reduced the gene expression of TNF-α and cyclooxygenase, as well the levels of PGE 2 . ALL also inhibited the paw edema induced by carrageenan, mainly the late phase, but also the edema induced by serotonin, histamine, TNF-α, PLA 2 and PGE 2 , but not by L-arginine and bradykinin. In this model, AAL inhibited mechanical hypernociception induced by TNF-α, PGE 2 , db-cAMP and capsaicin, and the activity of myeloperoxidase in the paw tissues.Conclusion: AAL presents anti-in ammatory effect in acute models of rat in ammation involving the participation of prostaglandins, TNF-α and lectin domain.

show abstract

“…SAnDReS aims to integrate all necessary steps to create machine learning models in one suite of programs [44]. SAnDReS has been applied to a wide range of different protein systems [125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143] and has successfully generated machine learning models that outperform classical scoring functions in the prediction of binding affinity [45,[144][145][146][147][148][149][150][151].…”

Section: Binding Affinity With Sandresmentioning

confidence: 99%

“…Application of the machine learning methods of SAnDReS to the structures in the training set and the energy terms calculated using the program AutoDock4 generated polynomials equations with three independent variables (features in the machine learning terminology), which gives more than six observations for each independent variable. These polynomial scoring equations are described elsewhere [125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143]. The ratio of five observations (structures) per independent variable (or descriptor) is the minimum requested by the rule of thumb recommended for regressions models [152,153].…”

Section: Binding Affinity With Sandresmentioning

confidence: 99%

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

Veit-Acosta

Azevedo

2022

CMC

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 11 publications

References 59 publications

Man-Specific Lectins from Plants, Fungi, Algae and Cyanobacteria, as Potential Blockers for SARS-CoV, MERS-CoV and SARS-CoV-2 (COVID-19) Coronaviruses: Biomedical Perspectives

Man-Specific Lectins from Plants, Fungi, Algae and Cyanobacteria, as Potential Blockers for SARS-CoV, MERS-CoV and SARS-CoV-2 (COVID-19) Coronaviruses: Biomedical Perspectives

The anti-inflamatory effect of Andira anthelmia lectin in rats involves inhibition of the prostanoid pathway, TNF-α and lectin domain

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

Contact Info

Product

Resources

About