One of the general problems in biology today is that we are characterizing genomic sequences much faster than identifying the functions of the gene products, and the same problem exists with cytochromes P450 (P450). One fourth of the human P450s are not well-characterized and therefore considered "orphans." A number of approaches to deorphanization are discussed generally. Several liquid chromatography-mass spectrometry approaches have been applied to some of the human and Streptomyces coelicolor P450s. One current limitation is that too many fatty acid oxidations have been identified and we are probably missing more relevant substrates, possibly due to limits of sensitivity.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2939962 | PMC |
http://dx.doi.org/10.1016/j.bbapap.2010.05.005 | DOI Listing |
Cell Syst
November 2024
National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India. Electronic address:
Danneskiold-Samsøe and coworkers have developed an in silico screening pipeline based on AlphaFold2 for identifying single-pass transmembrane receptors for secreted peptides that play important roles in cell-cell signaling. Their approach can be used to deorphanize a diverse range of ligands. The overall strategy can be valuable in screening for weak and transient interactions.
View Article and Find Full Text PDFCell Syst
November 2024
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. Electronic address:
Secreted proteins play crucial roles in paracrine and endocrine signaling; however, identifying ligand-receptor interactions remains challenging. Here, we benchmarked AlphaFold2 (AF2) as a screening approach to identify extracellular ligands to single-pass transmembrane receptors. Key to the approach is the optimization of AF2 input and output for screening ligands against receptors to predict the most probable ligand-receptor interactions.
View Article and Find Full Text PDFBiotechnol Notes
November 2023
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.
Gen Comp Endocrinol
December 2024
Department of Biology, York University, Toronto, ON, Canada. Electronic address:
J Chem Inf Model
September 2024
Center for Computational Biology (CBIO), Mines Paris-PSL, 75006 Paris, France.
Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!