Open Structural Data in Precision Medicine.

AI KRas cancer chromatin accessibility drug resistance free-energy landscape machine learning network medicine signaling targeted therapy

Journal

Annual review of biomedical data science
ISSN: 2574-3414
Titre abrégé: Annu Rev Biomed Data Sci
Pays: United States
ID NLM: 101714020

Informations de publication

Date de publication:
10 08 2022
Historique:
pubmed: 29 4 2022
medline: 13 8 2022
entrez: 28 4 2022
Statut: ppublish

Résumé

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalizedpharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine.

Identifiants

pubmed: 35483346
doi: 10.1146/annurev-biodatasci-122220-012951
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article Review Research Support, N.I.H., Intramural Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

95-117

Subventions

Organisme : NCI NIH HHS
ID : HHSN261201500003I
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG073323
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066707
Pays : United States
Organisme : NIA NIH HHS
ID : R56 AG074001
Pays : United States

Auteurs

Ruth Nussinov (R)

Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland, USA; email: NussinoR@mail.nih.gov.
Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.

Hyunbum Jang (H)

Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland, USA; email: NussinoR@mail.nih.gov.

Guy Nir (G)

Department of Biochemistry and Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, and Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, Texas, USA.

Chung-Jung Tsai (CJ)

Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland, USA; email: NussinoR@mail.nih.gov.

Feixiong Cheng (F)

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.

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