scholarly journals YAPP-CD: Yet another protein-peptide complex database

2021 ◽  
Author(s):  
Joon-Sang Park
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Pharmaceutical Products ◽  
Protein Protein Interactions ◽  
Peptide Complex ◽  
Prediction Quality ◽  
Peptide Docking ◽  
Peptide Interactions ◽  
Peptide Complexes ◽  
Increasing Demand

Protein-peptide interactions are of great interest to the research community not only because they serve as mediators in many protein-protein interactions but also because of the increasing demand for peptide-based pharmaceutical products. Protein-peptide docking is a major tool for studying protein-peptide interactions, and several docking methods are currently available. Among various protein-peptide docking algorithms, template-based approaches, which utilize known protein-peptide complexes or templates to predict a new one, have been shown to yield more reliable results than template-free methods in recent comparative research. To obtain reliable results with a template-based docking method, the template database must be comprehensive enough; that is, there must be similar templates of protein-peptide complexes to the protein and peptide being investigated. Thus, the template database must be updated to leverage recent advances in structural biology. However, the template database distributed with GalaxyPepDock, one of the most widely used peptide docking programs, is outdated, limiting the prediction quality of the method. Here, we present an up-to-date protein-peptide complex database called YAPP-CD, which can be directly plugged into the GalaxyPepDock binary package to improve GalaxyPepDock's prediction quality by drawing on recent discoveries in structural biology. Experimental results show that YAPP-CD significantly improves GalaxyPepDock's prediction quality, e.g., the average Ligand/Interface RMSD of a benchmark set is reduced from 7.60 A/3.62 A to 3.47 A/1.71 A.

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.

scholarly journals Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

2021 ◽  
Author(s):  
Erinna F. Lee ◽  
W. Douglas Fairlie
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Basic Science ◽  
Great Success ◽  
Protein Protein Interactions ◽  
New Class ◽  
Success Stories ◽  
History Of ◽  
Approved Drugs ◽  
Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

Structural biology of plant sulfur metabolism: from sulfate to glutathione

2019 ◽  
Vol 70 (16) ◽  
pp. 4089-4103 ◽  
Author(s):  
Joseph M Jez
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Sulfur Metabolism ◽  
Essential Element ◽  
Glutathione Synthetase ◽  
Protein Protein Interactions ◽  
Glutathione Biosynthesis ◽  
Wide Range ◽  
Aps Reductase ◽  
Level Information

Abstract Sulfur is an essential element for all organisms. Plants must assimilate this nutrient from the environment and convert it into metabolically useful forms for the biosynthesis of a wide range of compounds, including cysteine and glutathione. This review summarizes structural biology studies on the enzymes involved in plant sulfur assimilation [ATP sulfurylase, adenosine-5'-phosphate (APS) reductase, and sulfite reductase], cysteine biosynthesis (serine acetyltransferase and O-acetylserine sulfhydrylase), and glutathione biosynthesis (glutamate-cysteine ligase and glutathione synthetase) pathways. Overall, X-ray crystal structures of enzymes in these core pathways provide molecular-level information on the chemical events that allow plants to incorporate sulfur into essential metabolites and revealed new biochemical regulatory mechanisms, such as structural rearrangements, protein–protein interactions, and thiol-based redox switches, for controlling different steps in these pathways.

Structural biology and drug discovery for protein–protein interactions

2012 ◽  
Vol 33 (5) ◽  
pp. 241-248 ◽  
Author(s):  
Harry Jubb ◽  
Alicia P. Higueruelo ◽  
Anja Winter ◽  
Tom L. Blundell
Keyword(s):  
Drug Discovery ◽  
Structural Biology ◽  
Protein Interactions ◽  
Protein Protein Interactions

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 Linkers in the structural biology of protein-protein interactions

2013 ◽  
Vol 22 (2) ◽  
pp. 153-167 ◽  
Author(s):  
Vishnu Priyanka Reddy Chichili ◽  
Veerendra Kumar ◽  
J. Sivaraman
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Paramagnetic NMR Spectroscopy Is a Tool to Address Reactivity, Structure, and Protein–Protein Interactions of Metalloproteins: The Case of Iron–Sulfur Proteins

2020 ◽  
Vol 6 (4) ◽  
pp. 46
Author(s):  
Mario Piccioli
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Magnetic Coupling ◽  
Paramagnetic Nmr ◽  
Relaxation Rates ◽  
Protein Protein Interactions ◽  
Iron Sulfur Cluster ◽  
Protein Protein Interaction ◽  
Iron Sulfur ◽  
Protein Protein Interaction Networks

The study of cellular machineries responsible for the iron–sulfur (Fe–S) cluster biogenesis has led to the identification of a large number of proteins, whose importance for life is documented by an increasing number of diseases linked to them. The labile nature of Fe–S clusters and the transient protein–protein interactions, occurring during the various steps of the maturation process, make their structural characterization in solution particularly difficult. Paramagnetic nuclear magnetic resonance (NMR) has been used for decades to characterize chemical composition, magnetic coupling, and the electronic structure of Fe–S clusters in proteins; it represents, therefore, a powerful tool to study the protein–protein interaction networks of proteins involving into iron–sulfur cluster biogenesis. The optimization of the various NMR experiments with respect to the hyperfine interaction will be summarized here in the form of a protocol; recently developed experiments for measuring longitudinal and transverse nuclear relaxation rates in highly paramagnetic systems will be also reviewed. Finally, we will address the use of extrinsic paramagnetic centers covalently bound to diamagnetic proteins, which contributed over the last twenty years to promote the applications of paramagnetic NMR well beyond the structural biology of metalloproteins.

Mass Spectrometry for Structural Biology: Determining the Composition and Architecture of Protein Complexes

2011 ◽  
Vol 64 (6) ◽  
pp. 681 ◽  
Author(s):  
Tara L. Pukala
Keyword(s):  
Mass Spectrometry ◽  
Structural Biology ◽  
Protein Interactions ◽  
Structural Investigation ◽  
Protein Complexes ◽  
Individual Species ◽  
Protein Protein Interactions ◽  
Identify Protein Complex ◽  
Technological Advances ◽  
Recent Developments

Knowledge of protein structure and protein–protein interactions is vital for appreciating the elaborate biochemical pathways that underlie cellular function. While many techniques exist to probe the structure and complex interplay between functional proteins, none currently offer a complete picture. Mass spectrometry and associated methods provide complementary information to established structural biology tools, and with rapidly evolving technological advances, can in some cases even exceed other techniques by its diversity in application and information content. This is primarily because of the ability of mass spectrometry to precisely identify protein complex stoichiometry, detect individual species present in a mixture, and concomitantly offer conformational information. This review describes the attributes of mass spectrometry for the structural investigation of multiprotein assemblies in the context of recent developments and highlights in the field.

scholarly journals Improved Modeling of Peptide-Protein Binding through Global Docking and Accelerated Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Jinan Wang ◽  
Andrey Alekseenko ◽  
Dima Kozakov ◽  
Yinglong Miao
Keyword(s):  
Molecular Dynamics ◽  
Protein Binding ◽  
Protein Interactions ◽  
Protein Trafficking ◽  
Protein Protein Interactions ◽  
Peptide Backbone ◽  
Accelerated Molecular Dynamics ◽  
Peptide Docking ◽  
Medium Quality ◽  
Dynamics Simulations

AbstractPeptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3 Å – 4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6 Å – 2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

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