scholarly journals Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4586 ◽  
Author(s):  
David J. Diller ◽  
Jon Swanson ◽  
Alexander S. Bayden ◽  
Chris J. Brown ◽  
Dawn Thean ◽  
...  
Keyword(s):  
Drug Design ◽  
Protein Interactions ◽  
Experimental Investigations ◽  
Peptide Drug ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
Binding Data ◽  
Helical Peptide ◽  
Macrocyclic Peptides ◽  
Computational Platform

There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

Divide-and-Link Peptide Docking: A Fragment-Based Peptide Docking Protocol

2021 ◽  
Author(s):  
Lu Sun ◽  
Tingting Fu ◽  
Dan Zhao ◽  
Hongjun Fan ◽  
Shijun Zhong
Keyword(s):  
Protein Folding ◽  
Drug Design ◽  
Protein Interactions ◽  
Structural Data ◽  
Peptide Drug ◽  
Protein Protein Interactions ◽  
Peptide Docking ◽  
Docking Protocol ◽  
Peptide Interaction

Protein-peptide interaction is crucial for various important cellular regulations, and also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained...

scholarly journals Optimal Anchoring of a Urea-based Foldamer Inhibitor of ASF1 Histone Chaperone Through Backbone Plasticity

2020 ◽  
Author(s):  
Johanne Mbianda ◽  
May Bakail ◽  
Christophe André ◽  
Gwenaëlle Moal ◽  
Marie E. Perrin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Histone Chaperone ◽  
Protein Protein Interactions ◽  
Protein Surface ◽  
Chromatin Dynamics ◽  
Correct Orientation ◽  
Surface Recognition ◽  
Binding Interface ◽  
Helical Peptide ◽  
Α Helix

<p><b>Sequence-specific oligomers with predictable folding patterns, i.e. foldamers provide new opportunities to mimic α-helical peptides and design inhibitors of protein-protein interactions. One major hurdle of this strategy is to retain the correct orientation of key side chains involved in protein surface recognition. Here, we show that the structural plasticity of a foldamer backbone may significantly contribute to the required spatial adjustment for optimal interaction with the protein surface. By using oligoureas as α-helix mimics, we designed a foldamer/peptide hybrid inhibitor of histone chaperone ASF1, a key regulator of chromatin dynamics. The crystal structure of its complex with ASF1 reveals a striking plasticity of the urea backbone, which adapts to the ASF1 surface to maintain the same binding interface. One additional benefit of generating ASF1 ligands with non-peptide oligourea segments is the resistance to proteolysis in human plasma which was highly improved compared to the cognate α-helical peptide. </b></p>

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

2019 ◽  
Vol 21 (5) ◽  
pp. 1798-1805 ◽  
Author(s):  
Kai Yu ◽  
Qingfeng Zhang ◽  
Zekun Liu ◽  
Yimeng Du ◽  
Xinjiao Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Web Server ◽  
Data Sets ◽  
Lysine Acetylation ◽  
Protein Acetylation ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Lysine Acetylation ◽  
Specific Regulation

Abstract Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein–protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

scholarly journals Structure-Based Stabilization of Non-native Protein–Protein Interactions of Coronavirus Nucleocapsid Proteins in Antiviral Drug Design

2020 ◽  
Vol 63 (6) ◽  
pp. 3131-3141 ◽  
Author(s):  
Shan-Meng Lin ◽  
Shih-Chao Lin ◽  
Jia-Ning Hsu ◽  
Chung-ke Chang ◽  
Ching-Ming Chien ◽  
...  
Keyword(s):  
Drug Design ◽  
Protein Interactions ◽  
Antiviral Drug ◽  
Native Protein ◽  
Protein Protein Interactions ◽  
Nucleocapsid Proteins

scholarly journals Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

2016 ◽  
Vol 113 (50) ◽  
pp. E8051-E8058 ◽  
Author(s):  
Fang Bai ◽  
Faruck Morcos ◽  
Ryan R. Cheng ◽  
Hualiang Jiang ◽  
José N. Onuchic
Keyword(s):  
Drug Design ◽  
Protein Interactions ◽  
Binding Sites ◽  
Hot Spots ◽  
Therapeutic Agents ◽  
Molecular Probes ◽  
Therapeutic Drug ◽  
Protein Protein Interactions ◽  
Protein Surface ◽  
Fragment Docking

Protein−protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein−protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

scholarly journals Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein–Protein Interactions

2017 ◽  
Vol 60 (21) ◽  
pp. 8982-8988 ◽  
Author(s):  
Dennis M. Krüger ◽  
Adrian Glas ◽  
David Bier ◽  
Nicole Pospiech ◽  
Kerstin Wallraven ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Macrocyclic Peptides
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