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 Molecular recognition of ternary complexes: a new dimension in the structure-guided design of chemical degraders

2017 ◽  
Vol 61 (5) ◽  
pp. 505-516 ◽  
Author(s):  
Scott J. Hughes ◽  
Alessio Ciulli
Keyword(s):  
Molecular Recognition ◽  
Drug Design ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Data ◽  
Ternary Complexes ◽  
Protein Protein Interactions ◽  
New Paradigm ◽  
Design And Optimization ◽  
Three Body

Molecular glues and bivalent inducers of protein degradation (also known as PROTACs) represent a fascinating new modality in pharmacotherapeutics: the potential to knockdown previously thought ‘undruggable’ targets at sub-stoichiometric concentrations in ways not possible using conventional inhibitors. Mounting evidence suggests these chemical agents, in concert with their target proteins, can be modelled as three-body binding equilibria that can exhibit significant cooperativity as a result of specific ligand-induced molecular recognition. Despite this, many existing drug design and optimization regimens still fixate on binary target engagement, in part due to limited structural data on ternary complexes. Recent crystal structures of protein complexes mediated by degrader molecules, including the first PROTAC ternary complex, underscore the importance of protein–protein interactions and intramolecular contacts to the mode of action of this class of compounds. These discoveries have opened the door to a new paradigm for structure-guided drug design: borrowing surface area and molecular recognition from nature to elicit cellular signalling.

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.

Neuronal SNARE complex: A protein folding system with intricate protein-protein interactions, and its common neuropathological hallmark, SNAP25

2019 ◽  
Vol 122 ◽  
pp. 196-207 ◽  
Author(s):  
Srijeeb Karmakar ◽  
Laipubam Gayatri Sharma ◽  
Abhishek Roy ◽  
Anjali Patel ◽  
Lalit Mohan Pandey
Keyword(s):  
Protein Folding ◽  
Protein Interactions ◽  
Snare Complex ◽  
Protein Protein Interactions

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein–protein Interactions

2019 ◽  
Vol 21 (35) ◽  
pp. 18958-18969 ◽  
Author(s):  
Ercheng Wang ◽  
Gaoqi Weng ◽  
Huiyong Sun ◽  
Hongyan Du ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Protein Folding ◽  
Protein Interactions ◽  
Conformational Transitions ◽  
Protein Recognition ◽  
Enhanced Sampling ◽  
Protein Protein Interactions ◽  
Dielectric Model

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein–protein recognition.

scholarly journals LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Eliza C. Martin ◽  
Octavina C. A. Sukarta ◽  
Laurentiu Spiridon ◽  
Laurentiu G. Grigore ◽  
Vlad Constantinescu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Data ◽  
Innate Immune ◽  
Protein Protein Interactions ◽  
Ensemble Of Classifiers ◽  
Immune Receptor ◽  
Motif Detection ◽  
Protein Architecture ◽  
Recognition Specificity ◽  
Leucine Rich Repeats

Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein–protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning.

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.

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