CISI: A Tool for Predicting Cross-interaction or Self-interaction of Monoclonal Antibodies Using Sequences

Dzisoo, Anthony Mackitz; He, Bifang; Karikari, Rita; Agoalikum, Elijah; Huang, Jian

doi:10.1007/s12539-019-00330-1

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…The convexity of the ROC curve offers insights into the model’s performance, with a curve skewing towards the top-left corner being indicative of superior predictive capability. The AUC, on the other hand, quantifies the overall performance, with values tending towards 1.0 symbolizing exemplary predictions, while a score around 0.5 is indicative of a model that predicts no better than random chance ( Dzisoo et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/.Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

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Section: Methodsmentioning

confidence: 99%

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Liu,

Wu,

et al. 2024

Front. Genet.

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“…As the information mentioned above is essential for antibody analysis and evaluation, a more comprehensive database for therapeutic antibodies against COVID-19 is needed. Facing hundreds and even thousands of antibody candidates, better and more bioinformatics tools for evaluating the antibody developability are also needed to accelerate the speed of antibody development [123,124].…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Development and application of therapeutic antibodies against COVID-19

Lin¹,

Abagna²,

Jiang³

et al. 2021

Int. J. Biol. Sci.

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The pandemic of Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2) continues to be a global health crisis. Fundamental studies at genome, transcriptome, proteome, and interactome levels have revealed many viral and host targets for therapeutic interventions. Hundreds of antibodies for treating COVID-19 have been developed at preclinical and clinical stages in the format of polyclonal antibodies, monoclonal antibodies, and cocktail antibodies. Four products, i.e., convalescent plasma, bamlanivimab, REGN-Cov2, and the cocktail of bamlanivimab and etesevimab have been authorized by the U.S. Food and Drug Administration (FDA) for emergency use. Hundreds of relevant clinical trials are ongoing worldwide. Therapeutic antibody therapies have been a very active and crucial part of COVID-19 treatment. In this review, we focus on the progress of therapeutic COVID-19 antibody development and application, discuss corresponding problems and challenges, suggesting new strategies and solutions.

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“…The tripeptide composition (TPC) is widely used to convert the sequences to vectors as TPC helps to reflect the sequence order and total amino acid composition. TPC has better predictive results than a single amino acid and a dipeptide composition [19,31]. The method for extracting TPC is shown as…”

Section: 2mentioning

confidence: 99%

“…The field of antibody drug development is no exception. There are attempts to predict viscosity, developability, crossinteraction, or selfinteraction of antibodies [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

Dzisoo

Kang

Yao

et al. 2020

BioMed Research International

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Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody’s sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).

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CISI: A Tool for Predicting Cross-interaction or Self-interaction of Monoclonal Antibodies Using Sequences

Cited by 8 publications

References 27 publications

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization

Development and application of therapeutic antibodies against COVID-19

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

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