A Screening Algorithm for Gastric Cancer-Binding Peptides

Janairo, Jose Isagani B.; Sy-Janairo, Marianne Linley L.

doi:10.1007/s10989-019-09874-8

Cited by 3 publications

(4 citation statements)

References 35 publications

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“…Phenolic compounds in liquid smoke play major contribution to antibacterial activity and giving smoke aroma in smoked product [10,12]. Total phenolic content in the liquid smoke obtained from different pyrolisis temperatures.…”

Section: Total Phenolic Contentmentioning

confidence: 99%

“…Phenols are obtained from lignin pyrolysis. The quantity and quality of phenol compound that can be found on liquid smoke is related with lignin-content and pyrolysis temperature [12,13]. The degradation of lignin usually starts at temperature of 300 o C [17].…”

Section: Total Phenolic Contentmentioning

confidence: 99%

“…Liquid smoke can be used as natural preservative for food or food additive that imparts a smoked flavor and aroma to meats, poultry and seafood. It also can be used as latex coagulant and as biosepticide to reduce termites impact on wood [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Effect of pyrolisis temperature and time on liquid smoke characteristics

2020

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Oil palm frond is a lignocellulose material, by pyrolysis, it will produce liquid smoke which have many advantages because of the compounds, such as acetic acid, carbonyls and phenolics contained in it. The pyrolysis of oil palm fronds in this study carried out at temperatures 400 °C, 500 °C, and 600 °C for 40 minutes, 90 minutes, and 120 minutes. There were 3 parameters observed in this study, they were acetic acid, phenolics and carbonyl contents which were analyzed by GC-MS instrument. The results of the study showed that the different temperatures and times of pyrolysis affected the content of chemical components in liquid smoke and acetic acid was the most dominant component obtained in the liquid smoke. The highest acetic acid content in the liquid smoke obtained was 92,30% at pyrolysis temperature of 500 °C for 120 minutes. Meanwhile, the highest total phenolic content obtained was 7,32% at pyrolysis temperature of 600 °C for 120 minutes and the highest carbonyl content obtained was 32,56% at the pyrolysis temperature of 400 °C for 60 minutes

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Section: Total Phenolic Contentmentioning

confidence: 99%

Section: Total Phenolic Contentmentioning

confidence: 99%

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Effect of pyrolisis temperature and time on liquid smoke characteristics

2020

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“…In addition, ML models can identify trends and patterns within the data that can lead to the design of improved peptide ligands. ML models have previously been created for other substrate-binding peptides, such as metal-binding peptides, − cancer-binding peptides, among others. To date, the application of ML to this specific problem domain is yet to be carried out.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Biomimetic Chromatography Peptide Ligands

Janairo

2022

ACS Appl. Bio Mater.

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Purification is an essential part of antibody production, which are important therapeutic biomolecules. Common methods of antibody purification rely on affinity chromatography (AC), wherein whole proteins are oftentimes used as ligands to catch the antibodies to be purified. While AC has been successful in purifying antibodies, it is associated with multiple challenges such as high cost and low stability, among others. A promising alternative is using short peptide sequences in place of whole proteins as the stationary phase for the chromatographic separation of the antibodies. In an effort to accelerate the discovery and development of short peptides for biomimetic chromatography, this study reports the creation of a machine learning classification which was trained and tested on 480 tetrapeptides. The optimized logistic regression model uses Cruciani properties as the input variables and can categorize peptides into one of two classes based on their binding affinity with immunoglobulin G (IgG). The externally validated model demonstrates satisfactory predictive performance and excellent discrimination as demonstrated by performance metrics such as AUC = 0.874, Balanced Accuracy = 0.874, F1 = 0.871, Precision = 0.884, and Recall = 0.859. Apart from this, the classifier has also provided valuable insights into important variables that influence the classification, such as electrostatic and hydrophobic interactions. Overall, the classifier can be regarded as a welcome development for biomimetic chromatography and is the first study that aims to integrate machine learning in the biomimetic chromatography peptide development process.

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Predicting Peptide Oligomeric State Through Chemical Artificial Intelligence

Janairo

2020

Int J Pept Res Ther

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A Screening Algorithm for Gastric Cancer-Binding Peptides

Cited by 3 publications

References 35 publications

Effect of pyrolisis temperature and time on liquid smoke characteristics

Effect of pyrolisis temperature and time on liquid smoke characteristics

Machine Learning Model for Biomimetic Chromatography Peptide Ligands

Predicting Peptide Oligomeric State Through Chemical Artificial Intelligence

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