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Keras/TensorFlow in Drug Design for Immunity Disorders

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed ti...

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Autores principales: Dragan, Paulina, Joshi, Kavita, Atzei, Alessandro, Latek, Dorota
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573944/
https://www.ncbi.nlm.nih.gov/pubmed/37834457
http://dx.doi.org/10.3390/ijms241915009
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author Dragan, Paulina
Joshi, Kavita
Atzei, Alessandro
Latek, Dorota
author_facet Dragan, Paulina
Joshi, Kavita
Atzei, Alessandro
Latek, Dorota
author_sort Dragan, Paulina
collection PubMed
description Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.
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spelling pubmed-105739442023-10-14 Keras/TensorFlow in Drug Design for Immunity Disorders Dragan, Paulina Joshi, Kavita Atzei, Alessandro Latek, Dorota Int J Mol Sci Article Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset. MDPI 2023-10-09 /pmc/articles/PMC10573944/ /pubmed/37834457 http://dx.doi.org/10.3390/ijms241915009 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dragan, Paulina
Joshi, Kavita
Atzei, Alessandro
Latek, Dorota
Keras/TensorFlow in Drug Design for Immunity Disorders
title Keras/TensorFlow in Drug Design for Immunity Disorders
title_full Keras/TensorFlow in Drug Design for Immunity Disorders
title_fullStr Keras/TensorFlow in Drug Design for Immunity Disorders
title_full_unstemmed Keras/TensorFlow in Drug Design for Immunity Disorders
title_short Keras/TensorFlow in Drug Design for Immunity Disorders
title_sort keras/tensorflow in drug design for immunity disorders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573944/
https://www.ncbi.nlm.nih.gov/pubmed/37834457
http://dx.doi.org/10.3390/ijms241915009
work_keys_str_mv AT draganpaulina kerastensorflowindrugdesignforimmunitydisorders
AT joshikavita kerastensorflowindrugdesignforimmunitydisorders
AT atzeialessandro kerastensorflowindrugdesignforimmunitydisorders
AT latekdorota kerastensorflowindrugdesignforimmunitydisorders