diSBPred: A machine learning based approach for disulfide bond prediction

Mishra, Avdesh; Kabir, Wasi Ul; Hoque, Tamjidul

doi:10.1016/j.compbiolchem.2021.107436

Cited by 8 publications

(3 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PDIs are multifunctional thiol oxidoreductases that catalyze the oxidation, reduction, and isomerization of protein disulfide bonds, the most studied Cys ox-PTM [ 9 ]. Apart from peptide bonds, disulfides are the most prevalent covalent link between two amino acids and are one of the most frequent PTMs [ [10] , [11] , [12] ]. Disulfide bonds play important roles in protein structure and function, but also in the folding of nascent peptides in the ER [ 13 , 14 ].…”

Section: Protein Disulfide Isomerasesmentioning

confidence: 99%

Redox proteomics and structural analyses provide insightful implications for additional non-catalytic thiol-disulfide motifs in PDIs

Cuervo¹,

Grandvaux²

2023

Redox Biology

View full text Add to dashboard Cite

Section: Protein Disulfide Isomerasesmentioning

confidence: 99%

Redox proteomics and structural analyses provide insightful implications for additional non-catalytic thiol-disulfide motifs in PDIs

Cuervo¹,

Grandvaux²

2023

Redox Biology

View full text Add to dashboard Cite

“…40 In addition, the stability of the pterin deaminase 3D structure was confirmed with the predicted disulfide bonds (Figure 5). 41 Our study explored the detailed structural characterization of the pterin deaminase enzyme from A. tumefaciens LBA4404 and its application as therapeutic drugs for cancer therapy.…”

Section: Enzyme Stabilitymentioning

confidence: 99%

Identification and structural prediction of the unrevealed amidohydrolase enzyme: Pterin deaminase from Agrobacterium tumefaciens LBA4404

Dhanapal

Thandeeswaran

Palaniswamy

et al. 2022

Biotech and App Biochem

View full text Add to dashboard Cite

Microbes make a remarkable contribution to the health and well-being of living beings all over the world. Interestingly, pterin deaminase is an amidohydrolase enzyme that exhibits antitumor, anticancer activities and antioxidant properties. With the existing evidence of the presence of pterin deaminase from microbial sources, an attempt was made to reveal the existence of this enzyme in the unexplored bacterium Agrobacterium tumefaciens LBA4404. After, the cells were harvested and characterized as intracellular enzymes and then partially purified through acetone precipitation. Subsequently, further purification step was carried out with an ion-exchange chromatogram (HiTrap Q FF) using the Fast-Protein Liquid Chromatography technique (FPLC). Henceforward, the approximate molecular weight of the purified pterin deaminase was determined through SDS-PAGE. Furthermore, the purified protein was identified accurately by MALDI-TOF, and the sequence was explored through a Mascot search engine. Additionally, the three-dimensional structure was predicted and then validated, as well as ligand-binding sites, and the stability of this enzyme was confirmed for the first time. Thus, the present study revealed the selected parameters showing a considerable impact on the identification and purification of pterin deaminase from A. tumefaciens LBA4404 for the first time. The enzyme specificity makes it a favorable choice as a potent anticancer agent.

show abstract

“…18,20−22 With the development of artificial intelligence technology, machine learning (ML) models have been applied in many biochemistry research fields, including protein and protein-like molecule analysis. 23,24,4,25,26 For instance, Bao et al developed several tools covering a wide range of applications, such as Golgi_DF, which classifies Golgi proteins using deep forest algorithms, 27 and Phage_UniR_LGBM, which classifies phage virion proteins using UniRep features and the LightGBM algorithm. 24 Information on BBB permeability is also often difficult to interpret because of the multitude of research methods used.…”

Section: ■ Introductionmentioning

confidence: 99%

BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides

de Oliveira,

Hirmz,

Wynendaele

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).

show abstract

diSBPred: A machine learning based approach for disulfide bond prediction

Cited by 8 publications

References 58 publications

Redox proteomics and structural analyses provide insightful implications for additional non-catalytic thiol-disulfide motifs in PDIs

Redox proteomics and structural analyses provide insightful implications for additional non-catalytic thiol-disulfide motifs in PDIs

Identification and structural prediction of the unrevealed amidohydrolase enzyme: Pterin deaminase from Agrobacterium tumefaciens LBA4404

BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides

Contact Info

Product

Resources

About