Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis

2010 ◽  
pp. 195-210 ◽  
Author(s):  
Kristine A. Pattin ◽  
Joshua L. Payne ◽  
Douglas P. Hill ◽  
Thomas Caldwell ◽  
Jonathan M. Fisher ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Expert Knowledge ◽  
Evolution System ◽  
Protein Protein Interactions

scholarly journals Mutation effect estimation on protein–protein interactions using deep contextualized representation learning

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Guangyu Zhou ◽  
Muhao Chen ◽  
Chelsea J T Ju ◽  
Zheng Wang ◽  
Jyun-Yu Jiang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Pair ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Point Mutations ◽  
Representation Learning ◽  
Structural Features ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Subtle Change

Abstract The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein–protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein–protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.

scholarly journals Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

2019 ◽  
Author(s):  
Guangyu Zhou ◽  
Muhao Chen ◽  
Chelsea J.-T. Ju ◽  
Zheng Wang ◽  
Jyun-Yu Jiang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Pair ◽  
Expert Knowledge ◽  
Point Mutations ◽  
Representation Learning ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Subtle Change ◽  
Learning Framework ◽  
Effect Estimation

AbstractThe functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations are commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations to PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR, to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wildtype protein pair and its mutation pair. Multiple-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that MuPIPR outperforms various state-of-the-art systems on the change of binding affinity prediction and the buried surface area prediction. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR

Protein-protein interactions of the p53 family with the Bcl-2 family in hepatocellular carcinoma

2011 ◽  
Vol 49 (08) ◽  
Author(s):  
LC König ◽  
M Meinhard ◽  
C Sandig ◽  
MH Bender ◽  
A Lovas ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
P53 Family

Partial Purification of a Specific Plasminogen Activator from Porcine Parotid Glands

1974 ◽  
Vol 31 (03) ◽  
pp. 403-414 ◽  
Author(s):  
Terence Cartwright
Keyword(s):  
Nucleic Acid ◽  
Salivary Glands ◽  
Plasminogen Activator ◽  
Protein Interactions ◽  
Partial Purification ◽  
Protein Protein Interactions ◽  
Parotid Glands ◽  
Two Stage ◽  
Preliminary Characterization ◽  
Specific Inhibitors

SummaryA method is described for the extraction with buffers of near physiological pH of a plasminogen activator from porcine salivary glands. Substantial purification of the activator was achieved although this was to some extent complicated by concomitant extraction of nucleic acid from the glands. Preliminary characterization experiments using specific inhibitors suggested that the activator functioned by a similar mechanism to that proposed for urokinase, but with some important kinetic differences in two-stage assay systems. The lack of reactivity of the pig gland enzyme in these systems might be related to the tendency to protein-protein interactions observed with this material.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Probing the 14-3-3 Isoform-Specificity Profile of Interactions Stabilized by Fusicoccin A

2020 ◽  
Author(s):  
James Frederich ◽  
Ananya Sengupta ◽  
Josue Liriano ◽  
Ewa A. Bienkiewicz ◽  
Brian G. Miller
Keyword(s):  
Protein Interactions ◽  
Neurological Diseases ◽  
Adapter Proteins ◽  
Protein Protein Interactions ◽  
Specific Expression ◽  
Individual Client ◽  
Isoform Specificity ◽  
Fusicoccin A

Fusicoccin A (FC) is a fungal phytotoxin that stabilizes protein–protein interactions (PPIs) between 14-3-3 adapter proteins and their phosphoprotein interaction partners. In recent years, FC has emerged as an important chemical probe of human 14-3-3 PPIs implicated in cancer and neurological diseases. These previous studies have established the structural requirements for FC-induced stabilization of 14-3-3·client phosphoprotein complexes; however, the effect of different 14-3-3 isoforms on FC activity has not been systematically explored. This is a relevant question for the continued development of FC variants because there are seven distinct isoforms of 14-3-3 in humans. Despite their remarkable sequence and structural similarities, a growing body of experimental evidence supports both tissue-specific expression of 14-3-3 isoforms and isoform-specific functions <i>in vivo</i>. Herein, we report the isoform-specificity profile of FC <i>in vitro</i>using recombinant human 14-3-3 isoforms and a focused library of fluorescein-labeled hexaphosphopeptides mimicking the C-terminal 14-3-3 recognition domains of client phosphoproteins targeted by FC in cell culture. Our results reveal modest isoform preferences for individual client phospholigands and demonstrate that FC differentially stabilizes PPIs involving 14-3-3s. Together, these data provide strong motivation for the development of non-natural FC variants with enhanced selectivity for individual 14-3-3 isoforms.

Identification of protein-protein interactions of human HtrA1

10.2741/e350 ◽  
2011 ◽  
Vol E3 (4) ◽  
pp. 1493-1499
Author(s):  
Alfonso Baldi
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions
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