Rapid Generation and Molecular Docking Analysis of Single-Chain Fragment Variable (scFv) Antibody Selected by Ribosome Display Targeting Cholecystokinin B Receptor (CCK-BR) for Reduction of Chronic Neuropathic Pain

Kunamneni, Adinarayana; Montera, Marena A.; Durvasula, Ravi; Alles, Sascha R.A.; Goyal, Shubham; Westlund, Karin N.

doi:10.3390/ijms241311035

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Studies are approved by the Institutional Care and Use Committee of the University of New Mexico Health Sciences Center (IACUC #23-201364-HSC, 5-13-2024). Mouse DRG cultures were performed as previously described with some modifications (Kunamneni et al, 2023). Briefly, lumbar DRG were collected and put into an enzymatic solution containing sterile, magnesium/calcium-free HBSS, papain, dispase, and collagenase.…”

Section: Methodsmentioning

confidence: 99%

Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons

Zurek,

Thiyagarajan,

Ehsanian

et al. 2024

Preprint

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Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the molecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neurons are single-firing (exhibit only one action potential) or multi-firing (exhibit 2 or more action potentials). To determine if single- and multi-firing hDRG exhibit differences in intrinsic properties, firing phenotypes, and AP waveform properties, and if these properties could be used to predict multi-firing, we measured 22 electrophysiological properties by whole-cell patch-clamp electrophysiology of 94 hDRG neurons from 6 male and 4 female donors. We then analyzed the data using several machine learning models to determine if these properties could be used to predict multi-firing. We used 1000 iterations of Monte Carlo Cross Validation to split the data into different train and test sets and tested the Logistic Regression, k-Nearest Neighbors, Random Forest, Supported Vector Classification, and XGBoost machine learning models. All models tested had a greater than 80% accuracy on average, with Supported Vector Classification and XGBoost performing the best. We found that several properties correlated with multi-firing hDRG neurons and together could be used to predict multi-firing neurons in hDRG including a long decay time, a low rheobase, and long first spike latency. We also found that the hDRG models were able to predict multi-firing with 90% accuracy in mDRG. Targeting the neuronal properties that lead to multi-firing could elucidate better targets for treatment of chronic pain.

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Section: Methodsmentioning

confidence: 99%

Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons

Zurek,

Thiyagarajan,

Ehsanian

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Machine Learning Elucidates Electrophysiological Properties Predictive of Multi- and Single-Firing Human and Mouse Dorsal Root Ganglia Neurons

Zurek,

Thiyagarajan,

Ehsanian

et al. 2024

eNeuro

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Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the molecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neurons are single-firing (exhibit only one action potential) or multi-firing (exhibit 2 or more action potentials). To determine if single- and multi-firing hDRG neurons exhibit differences in intrinsic properties, firing phenotypes, and AP waveform properties, and if these properties could be used to predict multi-firing, we measured 22 electrophysiological properties by whole-cell patch-clamp electrophysiology of 94 hDRG neurons from 6 male and 4 female donors. We then analyzed the data using several machine learning models to determine if these properties could be used to predict multi-firing. We used 1000 iterations of Monte Carlo Cross Validation to split the data into different train and test sets and tested the Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Classifier, and XGBoost machine learning models. All models tested had a greater than 80% accuracy on average, with Support Vector Classifier and XGBoost performing the best. We found that several properties correlated with multi-firing hDRG neurons and together could be used to predict multi-firing neurons in hDRG including a long decay time, a low rheobase, and long first spike latency. We also found that the hDRG models were able to predict multi-firing with 90% accuracy in mDRG neurons. Understanding these properties could be beneficial in the elucidation of targets on peripheral sensory neurons related to pain.Significance StatementThis study will improve understanding of the electrophysiological mechanisms of DRG neurons. Our machine learning algorithms show few species differences between mouse and human DRG neuron electrophysiology under baseline conditions. These are important findings for the study of neuronal excitability in the context of pain therapeutic development.

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Eukaryotic ribosome display for antibody discovery: A review

Chance,

Kang

2024

HAB

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Monoclonal antibody biologics have significantly transformed the therapeutic landscape within the biopharmaceutical industry, partly due to the utilisation of discovery technologies such as the hybridoma method and phage display. While these established platforms have streamlined the development process to date, their reliance on cell transformation for antibody identification faces limitations related to library diversification and the constraints of host cell physiology. Cell-free systems like ribosome display offer a complementary approach, enabling antibody selection in a completely in vitro setting while harnessing enriched cellular molecular machinery. This review aims to provide an overview of the fundamental principles underlying the ribosome display method and its potential for advancing antibody discovery and development.

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Rapid Generation and Molecular Docking Analysis of Single-Chain Fragment Variable (scFv) Antibody Selected by Ribosome Display Targeting Cholecystokinin B Receptor (CCK-BR) for Reduction of Chronic Neuropathic Pain

Cited by 3 publications

References 66 publications

Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons

Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons

Machine Learning Elucidates Electrophysiological Properties Predictive of Multi- and Single-Firing Human and Mouse Dorsal Root Ganglia Neurons

Eukaryotic ribosome display for antibody discovery: A review

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