Cell phenotypes can be predicted from propensities of protein conformations

Nussinov, Ruth; Liu, Yonglan; Zhang, Wengang; Jang, Hyunbum

doi:10.1016/j.sbi.2023.102722

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…Finally, it connects protein conformational ensembles to function, mechanisms of activation and drug discovery, in line with theory. 100,101…”

Section: Discussionmentioning

confidence: 99%

The allosteric mechanism of mTOR activation can inform bitopic inhibitor optimization

Liu,

Zhang,

Jang

et al. 2024

Chem. Sci.

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“…Finally, it connects protein conformational ensembles to function, mechanisms of activation and drug discovery, in line with theory. 100,101…”

Section: Discussionmentioning

confidence: 99%

The allosteric mechanism of mTOR activation can inform bitopic inhibitor optimization

Liu,

Zhang,

Jang

et al. 2024

Chem. Sci.

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“…The challenge is in selecting the questions, the right tools (artificial intelligence (AI) based such as machine learning (ML), deep learning (DL), or other, advanced, or classical methods) to target them, and the protocols ( Zeng et al, 2022 ). Among these, molecular dynamics simulations, which are essentially a single molecule approach, can also be considered as a predictive tool of cell phenotype ( Nussinov et al, 2023b ; a ). Tool selection is also dependent on how much data (and of what quality) are available.…”

Section: Introduction: Background and Premisementioning

confidence: 99%

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Nussinov,

Yavuz,

Demirel

et al. 2024

Front. Cell Dev. Biol.

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The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

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Single cell spatial biology over developmental time can decipher pediatric brain pathologies

Nussinov,

Yavuz,

Jang

2024

Neurobiology of Disease

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Cell phenotypes can be predicted from propensities of protein conformations

Cited by 4 publications

References 64 publications

The allosteric mechanism of mTOR activation can inform bitopic inhibitor optimization

The allosteric mechanism of mTOR activation can inform bitopic inhibitor optimization

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Single cell spatial biology over developmental time can decipher pediatric brain pathologies

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