Quantitative structure-activity relationships by distance geometry: thyroxine binding site.

G M Crippen

doi:10.1021/jm00134a014

Home/Archive/PubMed

PubMed · 7205888

Quantitative structure-activity relationships by distance geometry: thyroxine binding site.

G M Crippen

View original source ↗Export metadata ↓

Abstract

The binding data for 27 analogues of thyroxine interacting with human prealbumin have been fitted to a simple distance geometry model of the binding site. The novel aspect of the calculation is that a general definition of chirality has been implemented, so that the model site exhibits the observed stereospecificity. The calculated free energies of binding of the ligands fit the experimental data with a root mean square deviation of 0.5 kcal/mol. Three additional analogues not included in the original data set fit equally well. The geometry of the proposed binding site matches the X-ray crystal structure of the tetraiodothyronine-prealbumin complex with a root mean square deviation of 1.0 to 1.6 A.

Explore related subjects

Binding Sites Humans Mathematics Prealbumin Protein Binding Structure-Activity Relationship Thyroxine X-Ray Diffraction Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

G M Crippen. 1981. Quantitative structure-activity relationships by distance geometry: thyroxine binding site.. https://doi.org/10.1021/jm00134a014

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗

PubMed2024

Unplugging lateral fenestrations of NALCN reveals a hidden drug binding site within the pore region.

The sodium (Na+) leak channel (NALCN) is a member of the four-domain voltage-gated cation channel family that includes the prototypical voltage-gated sodium and calcium channels (NaVs and CaVs, respectively). Unlike NaVs and CaVs, which have four lateral fenestrations that serve as routes for lipophilic compounds to enter the central cavity to modulate channel function, NALCN has bulky residues (W311, L588, M1145, and Y1436) that block these openings. Structural data suggest that occluded fenestrations underlie the pharmacological resistance of NALCN, but functional evidence is lacking. To test this hypothesis, we unplugged the fenestrations of NALCN by substituting the four aforementioned residues with alanine (AAAA) and compared the effects of NaV, CaV, and NALCN blockers on both wild-type (WT) and AAAA channels. Most compounds behaved in a similar manner on both channels, but phenytoin and 2-aminoethoxydiphenyl borate (2-APB) elicited additional, distinct responses on AAAA channels. Further experiments using single alanine mutants revealed that phenytoin and 2-APB enter the inner cavity through distinct fenestrations, implying structural specificity to their modes of access. Using a combination of computational and functional approaches, we identified amino acid residues critical for 2-APB activity, supporting the existence of drug binding site(s) within the pore region. Intrigued by the activity of 2-APB and its analogues, we tested compounds containing the diphenylmethane/amine moiety on WT channels. We identified clinically used drugs that exhibited diverse activity, thus expanding the pharmacological toolbox for NALCN. While the low potencies of active compounds reiterate the pharmacological resistance of NALCN, our findings lay the foundation for rational drug design to develop NALCN modulators with refined properties.

Binding Sites↗

PubMed2008

TAp73beta and DNp73beta activate the expression of the pro-survival caspase-2S.

p73, the p53 homologue, exists as a transactivation-domain-proficient TAp73 or deficient deltaN(DN)p73 form. Expectedly, the oncogenic DNp73 that is capable of inactivating both TAp73 and p53 function, is over-expressed in cancers. However, the role of TAp73, which exhibits tumour-suppressive properties in gain or loss of function models, in human cancers where it is hyper-expressed is unclear. We demonstrate here that both TAp73 and DNp73 are able to specifically transactivate the expression of the anti-apoptotic member of the caspase family, caspase-2(S). Neither p53 nor TAp63 has this property, and only the p73beta form, but not the p73alpha form, has this competency. Caspase-2 promoter analysis revealed that a non-canonical, 18 bp GC-rich Sp-1-binding site-containing region is essential for p73beta-mediated activation. However, mutating the Sp-1-binding site or silencing Sp-1 expression did not affect p73beta's transactivation ability. In vitro DNA binding and in vivo chromatin immunoprecipitation assays indicated that p73beta is capable of directly binding to this region, and consistently, DNA binding p73 mutant was unable to transactivate caspase-2(S). Finally, DNp73beta over-expression in neuroblastoma cells led to resistance to cell death, and concomitantly to elevated levels of caspase-2(S.) Silencing p73 expression in these cells led to reduction of caspase-2(S) expression and increased cell death. Together, the data identifies caspase-2(S) as a novel transcriptional target common to both TAp73 and DNp73, and raises the possibility that TAp73 may be over-expressed in cancers to promote survival.

Binding Sites↗