scholarly journals Molecular tweezers target a protein–protein interface and thereby modulate complex formation

2016 ◽  
Vol 52 (98) ◽  
pp. 14141-14144 ◽  
Author(s):  
F. Trusch ◽  
K. Kowski ◽  
K. Bravo-Rodriguez ◽  
C. Beuck ◽  
A. Sowislok ◽  
...  
Keyword(s):  
Complex Formation ◽  
Protein Interaction ◽  
Protein Interface ◽  
Protein Surface ◽  
Molecular Tweezers ◽  
Protein Protein Interaction

Supramolecular binders select few residues on a protein surface and by their unique complexation mode disrupt a critical protein–protein interaction.

scholarly journals PIPENN: Protein Interface Prediction with an Ensemble of Neural Nets

2021 ◽  
Author(s):  
Bas Stringer ◽  
Hans De Ferrante ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
K. Anton A. Feenstra ◽  
...  
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Protein Interaction ◽  
Receptor Activation ◽  
Neural Nets ◽  
Data Sets ◽  
Protein Interface ◽  
Protein Protein Interaction ◽  
Independent Test ◽  
Interface Residues

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/

Protein Interface Pharmacophore Mapping Tools for Small Molecule Protein: Protein Interaction Inhibitor Discovery

2013 ◽  
Vol 13 (9) ◽  
pp. 989-1001 ◽  
Author(s):  
Arnout Voet ◽  
Eleanor F. Banwell ◽  
Kamlesh K. Sahu ◽  
Jonathan G. Heddle ◽  
Kam Y. J. Zhang
Keyword(s):  
Small Molecule ◽  
Protein Interaction ◽  
Protein Interface ◽  
Pharmacophore Mapping ◽  
Protein Protein Interaction ◽  
Inhibitor Discovery

scholarly journals Advantage of a baculovirus expression system for protein-protein interaction studies. Involvement of posttranslational phosphorylation in the interaction between wt p53 protein and poly(ADP-ribose) polymerase-1.

2005 ◽  
Vol 52 (3) ◽  
pp. 713-719 ◽  
Author(s):  
Gerald Schmid ◽  
Jacek Wojciechowski ◽  
Józefa Wesierska-Gadek
Keyword(s):  
Complex Formation ◽  
Protein Interaction ◽  
P53 Protein ◽  
Full Length ◽  
Functional Domain ◽  
Terminal Part ◽  
Protein Protein Interaction ◽  
Amino Terminal ◽  
Carboxy Terminal ◽  
Parp 1

We recently observed an interaction between poly(ADP-ribose) polymerase-1 (PARP-1) and the tumor suppressor p53 protein. However, more extensive studies on both proteins, especially those on characterization of their domains involved in the interaction were difficult due to very low expression levels of p53 in mammalian cells. Therefore, we generated recombinant proteins for such studies. To clarify which domains of human PARP-1 and of human wild-type (wt) p53 were involved in this protein-protein interaction, we generated baculoviral constructs encoding full length or distinct functional domains of both proteins. Full length PARP-1 was simultaneously coexpressed in insect cells with full length wt p53 protein or its distinct truncated fragments and vice versa. Reciprocal immunoprecipitation of Sf9 cell lysates revealed that the central and carboxy-terminal fragments of p53 each were sufficient to confer binding to PARP-1, whereas the amino-terminal part harbouring the transactivation functional domain was dispensable. On the other hand, the amino-terminal and central fragments of PARP-1 were both necessary for complex formation with p53 protein. Since the most important features of p53 protein are regulated by phosphorylation, we addressed the question whether its phosphorylation is essential for the binding between the two proteins. Baculovirally expressed wt p53 was post-translationally modified. At least six distinct p53 isomers were resolved by immunoblotting following two-dimensional separation of baculovirally expressed wt p53 protein. Using specific phospho-serine antibodies, we identified phosphorylation of baculovirally expressed p53 protein at five distinct sites. To define the role of p53 phosphorylation, pull-down assays using untreated and dephosphorylated p53 protein were performed. Dephosphorylated p53 failed to bind PARP-1, indicating that complex formation between the two proteins was regulated by phosphorylation of p53. The marked phosphorylation of p53 at Ser392 observed in unstressed cells suggests that the phosphorylated carboxy-terminal part of p53 undergoes complex formation with PARP-1 resulting in masking of the NES and thereby preventing its export.

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