scholarly journals Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

2022 ◽  
Vol 18 ◽  
pp. 1-12
Author(s):  
Hendrik Gruß ◽  
Rebecca C Feiner ◽  
Ridhiwan Mseya ◽  
David C Schröder ◽  
Michał Jewgiński ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Late Stage ◽  
Cross Coupling ◽  
Proteinase K ◽  
Protein Protein Interactions ◽  
Binding Partner ◽  
Novel Approach ◽  
Long Time ◽  
Proteolytic Stability ◽  
Peptide Stapling

The development of peptide stapling techniques to stabilise α-helical secondary structure motifs of peptides led to the design of modulators of protein–protein interactions, which had been considered undruggable for a long time. We disclose a novel approach towards peptide stapling utilising macrocyclisation by late-stage Suzuki–Miyaura cross-coupling of bromotryptophan-containing peptides of the catenin-binding domain of axin. Optimisation of the linker length in order to find a compromise between both sufficient linker rigidity and flexibility resulted in a peptide with an increased α-helicity and enhanced binding affinity to its native binding partner β-catenin. An increased proteolytic stability against proteinase K has been demonstrated.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

Biomolecules ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 106
Author(s):  
Pavel V. Ershov ◽  
Yuri V. Mezentsev ◽  
Alexis S. Ivanov
Keyword(s):  
Protein Interactions ◽  
Targeted Delivery ◽  
Protein Complexes ◽  
Protein Protein Interactions ◽  
Good Efficiency ◽  
Cellular Targets ◽  
Proteolytic Stability ◽  
Clinically Significant

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, “hot spots”, as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ–TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

scholarly journals The mechanism of sperm–oocyte fusion in mammals

Reproduction ◽  
2004 ◽  
Vol 127 (4) ◽  
pp. 423-429 ◽  
Author(s):  
Keisuke Kaji ◽  
Akira Kudo
Keyword(s):  
Membrane Protein ◽  
Sexual Reproduction ◽  
Gene Targeting ◽  
Molecular Mechanism ◽  
Protein Interactions ◽  
Recent Progress ◽  
Research Field ◽  
Protein Protein Interactions ◽  
Long Time ◽  
Adams Family

Sperm–oocyte fusion is one of the most impressive events in sexual reproduction, and the elucidation of its molecular mechanism has fascinated researchers for a long time. Because of the limitation of materials and difficulties in analyzing membrane protein–protein interactions, many attempts have failed to reach this goal. Recent studies involving gene targeting have clearly demonstrated the various molecules that are involved in sperm–oocyte binding and fusion. Sperm ADAMs (family of proteins with a disintegrin and metalloprotease domain), including fertilin α, fertilin β and cyritestin, have been investigated and found to be important for binding rather than for fusion and painstaking studies have raised suspicions that their putative receptors, oocyte integrins, are necessary for the sperm–oocyte interaction. Recently, several studies have focused the spotlight on CD9 and glycosylphosphatidylinositol (GPI)-anchored proteins on oocytes, and epididymal protein DE on sperm, as candidate molecules involved in sperm–oocyte fusion. Lack of, or interference with the function of, these proteins can disrupt the sperm–oocyte fusion without changing the binding. In this review we highlight the candidate molecules involved in the sperm–oocyte interaction suggested from the recent progress in this research field.

scholarly journals Virus–Host Protein–Protein Interactions between Human Papillomavirus 16 E6 A1 and D2/D3 Sub-Lineages: Variances and Similarities

2020 ◽  
Vol 21 (21) ◽  
pp. 7980
Author(s):  
Guillem Dayer ◽  
Mehran L. Masoom ◽  
Melissa Togtema ◽  
Ingeborg Zehbe
Keyword(s):  
Human Papillomavirus ◽  
Asian American ◽  
Protein Interactions ◽  
Host Protein ◽  
Cellular Proteins ◽  
Loss Of Function ◽  
Protein Protein Interactions ◽  
Human Papillomavirus 16 ◽  
Novel Approach ◽  
Causative Agents

High-risk strains of human papillomavirus are causative agents for cervical and other mucosal cancers, with type 16 being the most frequent. Compared to the European Prototype (EP; A1), the Asian-American (AA; D2/D3) sub-lineage seems to have increased abilities to promote carcinogenesis. Here, we studied protein–protein interactions (PPIs) between host proteins and sub-lineages of the key transforming E6 protein. We transduced human keratinocyte with EP or AA E6 genes and co-immunoprecipitated E6 proteins along with interacting cellular proteins to detect virus–host binding partners. AAE6 and EPE6 may have unique PPIs with host cellular proteins, conferring gain or loss of function and resulting in varied abilities to promote carcinogenesis. Using liquid chromatography-mass spectrometry and stringent interactor selection criteria based on the number of peptides, we identified 25 candidates: 6 unique to AAE6 and EPE6, along with 13 E6 targets common to both. A novel approach based on pathway selection discovered 171 target proteins: 90 unique AAE6 and 61 unique EPE6 along with 20 common E6 targets. Interpretations were made using databases, such as UniProt, BioGRID, and Reactome. Detected E6 targets were differentially implicated in important hallmarks of cancer: deregulating Notch signaling, energetics and hypoxia, DNA replication and repair, and immune response.

Multidimensional Integration Analysis of Autophagy-related Modules in Colorectal Cancer

2019 ◽  
Vol 16 (4) ◽  
pp. 340-346
Author(s):  
Yang Zhang ◽  
Zheng Zhang ◽  
Dong Wang ◽  
Jianzhen Xu ◽  
Yanhui Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Protein Interactions ◽  
Colon Adenocarcinoma ◽  
Undifferentiated Carcinoma ◽  
Protein Protein Interactions ◽  
Integration Strategy ◽  
Novel Approach ◽  
Multidimensional Integration ◽  
Regulation Network ◽  
Integration Analysis

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract occurring in the colon, which mainly divided into adenocarcinoma, mucinous adenocarcinoma, and undifferentiated carcinoma. However, autophagy is related to the occurrence and development of various kinds of human diseases such as cancer. There is little research on the relationship between CRC and autophagy. Hence, we performed multidimensional integration analysis to systematically explore potential relationship between autophagy and CRC. Based on gene expression datasets of colon adenocarcinoma (COAD) and protein-protein interactions (PPIs), we first identified 12 autophagy-related modules in COAD using WGCNA. Then, 9 module pairs which with significantly crosstalk were deciphered, a total of 6 functional modules. Autophagy-related genes in these modules were closely related with CRC, emphasizing that the important role of autophagy-related genes in CRC, including PPP2CA and EIF4E, etc. In addition to, by integrating transcription factor (TF)-target and RNA-associated interactions, a regulation network was constructed, in which 42 TFs (including SMAD3 and TP53, etc.) and 20 miRNAs (including miR-20 and miR-30a, etc.) were identified as pivot regulators. Pivot TFs were mainly involved in cell cycle, cell proliferation and pathways in cancer. And pivot miRNAs were demonstrated associated with CRC. It suggests that these pivot regulators might be have an effect on the development of CRC by regulating autophagy. In a word, our results suggested that multidimensional integration strategy provides a novel approach to discover potential relationships between autophagy and CRC, and further improves our understanding of autophagy and tumor in human.

Plasma thermogram profiling: A novel biomarker for lung cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22074-e22074 ◽  
Author(s):  
D. Xiang ◽  
N. C. Garbett ◽  
J. Chaires ◽  
D. L. Laber ◽  
G. H. Kloecker
Keyword(s):  
Lung Cancer ◽  
Healthy Volunteers ◽  
Protein Interactions ◽  
Plasma Proteins ◽  
Molecular Mechanisms ◽  
Plasma Proteome ◽  
Protein Protein Interactions ◽  
Scanning Calorimetry ◽  
Novel Approach ◽  
The Individual

e22074 Background: Currently, there are no clinically useful biomarkers for lung cancer (LC). We developed a novel approach by using differential scanning calorimetry (DSC) to analyze the plasma proteome of LC patients. Our assay produces a signature snapshot of the plasma proteome based on the thermal properties of the constituent proteins within the mixture. Each individual protein has a unique and characteristic melting temperature and melting enthalpy. These biophysical properties are as intrinsic and unique for each protein as are its sequence, mass, and charge. The denaturation of plasma results in a composite signature thermogram that represents the sum of all of the individual proteins within the proteome, weighted according to their concentration within the plasma or serum. Our goal is to identify unique thermogram profiles as biomarkers for lung cancer evaluation. Methods: Plasma samples were collected from LC patients and healthy volunteers. Sample analysis was performed using an automated capillary DSC. Comparison of plasma thermograms from controls and diseased individuals was done using quantile-quantile plots and the Kolmogorov-Smirnov test. Results: One hundred samples were obtained from healthy volunteers. DSC thermograms from control plasma were highly reproducible and yielded a characteristic signature with a well-defined shape and temperature maxima. Twenty samples from LC patients were obtained at diagnosis. Plasma from LC individuals yielded a unique thermogram that differs from that of healthy controls. Preliminary results suggested that DSC was sensitive to properties of the plasma proteins other than their charge and mass. We hypothesize that changes in thermogram shapes and positions in LC can arise from changes in the concentrations of plasma proteins, from disease-induced protein-protein interactions, or from the secretion into plasma of disease-related peptides that can bind to the most abundant plasma proteins. Conclusions: Lung cancer may have a characteristic signature thermogram that may serve as a biomarker for LC; further studies are needed to elucidate the biophysical and molecular mechanisms of different thermograms between normal individuals versus those with disease or cancer. No significant financial relationships to disclose.

scholarly journals AutoDock CrankPep: combining folding and docking to predict protein–peptide complexes

2019 ◽  
Vol 35 (24) ◽  
pp. 5121-5127 ◽  
Author(s):  
Yuqi Zhang ◽  
Michel F Sanner
Keyword(s):  
Amino Acids ◽  
Protein Interactions ◽  
Disordered Proteins ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Peptide Docking ◽  
Novel Approach ◽  
Peptide Interactions ◽  
Therapeutic Molecules ◽  
Peptide Complexes

Abstract Motivation Protein–peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. Results Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein–peptide complex. We show that it outperforms leading peptide docking methods on two protein–peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein–peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein–protein interactions and interactions with disordered proteins. Availability and implementation ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. Supplementary information Supplementary data are available at Bioinformatics online.

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