scholarly journals Small molecules targeting the NEDD8·NAE protein–protein interaction

2021 ◽  
Author(s):  
Chen-Ming Lin ◽  
Zhengyang Jiang ◽  
Zhe Gao ◽  
Maritess Arancillo ◽  
Kevin Burgess

Discovery of the first NEDDylation inhibitor, that targets the NEDD8·NAE protein–protein interaction, was acheived using the Exploring Key Orientations (EKO) approach.

2013 ◽  
Vol 5 (12) ◽  
pp. 1423-1435 ◽  
Author(s):  
Lucy D Smith ◽  
Robin J Leatherbarrow ◽  
Alan C Spivey

2017 ◽  
Author(s):  
Carolina dos S. Passos ◽  
Nathalie Deschamps ◽  
Yun Choi ◽  
Robert E. Cohen ◽  
Remo Perozzo ◽  
...  

AbstractHistone deacetylase 6 (HDAC6) is a cytoplasmic HDAC isoform able to remove acetyl groups from cellular substrates such as α-tubulin. In addition to the two deacetylase domains, HDAC6 has a C-terminal zinc-finger ubiquitin (Ub)-binding domain (ZnF-UBP) able to recognize free Ub. HDAC6-Ub interaction is thought to function in regulating the elimination of misfolded proteins during stress response through the aggresome pathway. Small molecules inhibiting deacetylation by HDAC6 were shown to reduce aggresomes, but the interplay between HDAC6 catalytic activity and Ub-binding function is not fully understood. Here we describe two methods to measure the HDAC6-Ub interaction in vitro using full-length HDAC6. Both methods were effective for screening inhibitors of the HDAC6-Ub protein-protein interaction independently of the catalytic activity. Our results suggest a potential role for the HDAC6 deacetylase domains in modulating HDAC6-Ub interaction. This new aspect of HDAC6 regulation can be targeted to address the roles of HDAC6-Ub interaction in normal and disease conditions.


Biochemistry ◽  
2017 ◽  
Vol 56 (12) ◽  
pp. 1768-1784 ◽  
Author(s):  
Degang Liu ◽  
David Xu ◽  
Min Liu ◽  
William Eric Knabe ◽  
Cai Yuan ◽  
...  

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 6001
Author(s):  
Ajaybabu V. Pobbati ◽  
Brian P. Rubin

The identification of protein-protein interaction disruptors (PPIDs) that disrupt the YAP/TAZ-TEAD interaction has gained considerable momentum. Several studies have shown that YAP/TAZ are no longer oncogenic when their interaction with the TEAD family of transcription factors is disrupted. The transcriptional co-regulator YAP (its homolog TAZ) interact with the surface pockets of TEADs. Peptidomimetic modalities like cystine-dense peptides and YAP cyclic and linear peptides exploit surface pockets (interface 2 and interface 3) on TEADs and function as PPIDs. The TEAD surface might pose a challenge for generating an effective small molecule PPID. Interestingly, TEADs also have a central pocket that is distinct from the surface pockets, and which small molecules leverage exclusively to disrupt the YAP/TAZ-TEAD interaction (allosteric PPIDs). Although small molecules that occupy the central pocket belong to diverse classes, they display certain common features. They are flexible, which allows them to adopt a palmitate-like conformation, and they have a predominant hydrophobic portion that contacts several hydrophobic residues and a small hydrophilic portion that faces the central pocket opening. Despite such progress, more selective PPIDs that also display favorable pharmacokinetic properties and show tolerable toxicity profiles are required to evaluate the feasibility of using these PPIDs for cancer therapy.


2016 ◽  
Vol 15 (8) ◽  
pp. 533-550 ◽  
Author(s):  
Duncan E. Scott ◽  
Andrew R. Bayly ◽  
Chris Abell ◽  
John Skidmore

2021 ◽  
Author(s):  
Bas Stringer ◽  
Hans De Ferrante ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
K. Anton A. Feenstra ◽  
...  

Motivation: Protein interactions play an essential role in many biological and cellular processes, such as protein—protein interaction (PPI) in signaling pathways, binding to DNA in transcription, and binding to small molecules in receptor activation or enzymatic activity. Experimental identification of protein binding interface residues is a time-consuming, costly, and challenging task. Several machine learning and other computational approaches exist which predict such interface residues. Here we explore if Deep Learning (DL) can be used effectively for this prediction task, and which learning strategies and architectures may be most efficient. We introduce seven DL architectures that are applied to eleven independent test sets, focused on the residues involved in PPI interfaces and in binding RNA/DNA and small molecule ligands. Results: We constructed a large data set dubbed BioDL, comprising protein-protein interaction data from the PDB and protein-ligand interactions (DNA, RNA and small molecules) from the BioLip database. Additionally, we reused our existing curated homo- and heteromeric PPI data sets. We performed several experiments to assess the impact of different data features, spatial forms, encoding schemes, network initializations, loss functions, regularization mechanisms, and activation functions on the performance of the predictors. Benchmarking the resulting DL models with an independent test set (ZK448) shows no single DL architecture performs best on all instances, but that an ensemble of DL architectures consistently achieves peak prediction performance. Our PIPENN's ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on all interaction types, achieving AUCs of 0.718 (protein—protein), 0.823 (protein—nucleotide) and 0.842 (protein—small molecule) respectively. Availability: Source code and data sets at https://github.com/ibivu/


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