ReviewClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2008
On the relevance of defining protein structures in cancer research.
Review in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Are three-dimensional structures of proteins relevant in the study of cancer? The knowledge of the three-dimensional structure of a protein is crucial to gain a full understanding of its function, and structural determination has already shown its potential for guided drug design. The knowledge of the structures of proteins and their complexes with other biological macromolecules helps to elucidate functional networks and provide a better understanding of the functionally relevant behaviour of the molecular machinery of the cell. To study the cell, we must be able to work with proteins, to elucidate how they diffuse and move, to know their interacting partners, and to understand the changes induced by those interactions. Three-dimensional structures give us a picture of the protein and thereby the opportunity to introduce mutations that alter its affinity and specificity for other interactions helping us to understand the physico-chemical mechanisms that control their function. In turn these can lead to the development of novel therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.