scholarly journals Evolution of (p)ppGpp-HPRT regulation through diversification of an allosteric oligomeric interaction

2019 ◽  
Author(s):  
Brent W. Anderson ◽  
Kuanqing Liu ◽  
Christine Wolak ◽  
Katarzyna Dubiel ◽  
Kenneth A. Satyshur ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Protein Interactions ◽  
Binding Motif ◽  
Protein Ligand Interactions ◽  
Protein Interfaces ◽  
Ligand Interactions ◽  
Binding Residues ◽  
The Common ◽  
Ligand Regulation

ABSTRACTThe signaling ligand (p)ppGpp binds diverse targets across bacteria, yet the mechanistic and evolutionary basis underlying these ligand-protein interactions remains poorly understood. Here we identify a novel (p)ppGpp binding motif in the enzyme HPRT, where (p)ppGpp shares identical binding residues for PRPP and nucleobase substrates to regulate purine homeostasis. Intriguingly, HPRTs across species share the conserved binding site yet strongly differ in ligand binding, from strong inhibition by basal (p)ppGpp levels to weak regulation at induced concentrations. Surprisingly, strong ligand binding requires an HPRT dimer-dimer interaction that allosterically opens the (p)ppGpp pocket. This dimer-dimer interaction is absent in the common ancestor but evolved to favor (p)ppGpp binding in the vast majority of bacteria. We propose that the evolutionary plasticity of oligomeric interfaces enables allosteric adjustment of ligand regulation, bypassing constraints of the ligand binding site. Since most ligands bind near protein-protein interfaces, this principle likely extends to other protein-ligand interactions.

scholarly journals Virtual Docking of VBP1 with HBX and NFκB Protein to Study the Activation of NFκB in the Regulatory Mechanism of Liver Cancer

2021 ◽  
pp. 21-28
Author(s):  
Mamta Sagar ◽  
Padma Saxena ◽  
Suruchi Singh ◽  
Ravindra Nath ◽  
Pramod W. Ramteke
Keyword(s):  
Amino Acids ◽  
Liver Cancer ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Von Hippel Lindau ◽  
Hbv Replication ◽  
Protein Ligand Interactions ◽  
B Virus ◽  
Ligand Interactions ◽  
The Common

Molecular docking is an efficient way to study protein-protein and protein-ligand interactions in virtual mode, this provides structural annotations of molecular interactions, required in the drug discovery process. The Cartesian FFT approach in ‘Hex’ spherical polar Fourier (SPF) uses rotational correlations, this method is used here to study protein-protein interactions. Hepatitis B virus (HBV) X protein (HBx) is essential for virus infection and has been used in the development of therapeutics for liver cancer. It can interact with many cellular proteins. It interferes with cell viability and stimulates HBV replication. The von Hippel-Lindau binding protein 1(VBP1) has an important role in HBx-mediated nuclear factor kappa B (NFkB) stimulation. VBP1 and HBx function as coactivators in the activation of NFκB binding. Docking results revealed that HBx and NFkB bind with VBP1 at the common site on amino acids positions Arg 161, Glu 92, and Arg 82, which may have a role in HBx-mediated NFκB activation. Lowest energy complex VBP1- NFkB1 was obtained at -883.70 Kcal/mol. The amino acids involved in interaction among HBx, VBP1, and NFκB proteins, may be involved in transcriptional regulation and has significance in normal and abnormal regulation. These amino acid interactions may be associated with the manifestation of Liver cancer.

scholarly journals Bridging the gap between structure and kinetics of human SGLT1

2012 ◽  
Vol 302 (9) ◽  
pp. C1293-C1305 ◽  
Author(s):  
Monica Sala-Rabanal ◽  
Bruce A. Hirayama ◽  
Donald D. F. Loo ◽  
Vincent Chaptal ◽  
Jeff Abramson ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Sugar Transport ◽  
Gene Families ◽  
Structural Studies ◽  
Sugar Binding ◽  
Substituted Cysteine Accessibility ◽  
Biochemical Assays ◽  
Functional Consequences ◽  
Binding Residues

The Na+-glucose cotransporter hSGLT1 is a member of a class of membrane proteins that harness Na+ electrochemical gradients to drive uphill solute transport. Although hSGLT1 belongs to one gene family (SLC5), recent structural studies of bacterial Na+ cotransporters have shown that Na+ transporters in different gene families have the same structural fold. We have constructed homology models of hSGLT1 in two conformations, the inward-facing occluded (based on vSGLT) and the outward open conformations (based on Mhp1), mutated in turn each of the conserved gates and ligand binding residues, expressed the SGLT1 mutants in Xenopus oocytes, and determined the functional consequences using biophysical and biochemical assays. The results establish that mutating the ligand binding residues produces profound changes in the ligand affinity (the half-saturation concentration, K0.5); e.g., mutating sugar binding residues increases the glucose K0.5 by up to three orders of magnitude. Mutation of the external gate residues increases the Na+ to sugar transport stoichiometry, demonstrating that these residues are critical for efficient cotransport. The changes in phlorizin inhibition constant ( Ki) are proportional to the changes in sugar K0.5, except in the case of F101C, where phlorizin Ki increases by orders of magnitude without a change in glucose K0.5. We conclude that glucose and phlorizin occupy the same binding site and that F101 is involved in binding to the phloretin group of the inhibitor. Substituted-cysteine accessibility methods show that the cysteine residues at the position of the gates and sugar binding site are largely accessible only to external hydrophilic methanethiosulfonate reagents in the presence of external Na+, demonstrating that the external sugar (and phlorizin) binding vestibule is opened by the presence of external Na+ and closes after the binding of sugar and phlorizin. Overall, the present results provide a bridge between kinetics and structural studies of cotransporters.

scholarly journals Gentamicin Binds to the Megalin Receptor as a Competitive Inhibitor Using the Common Ligand Binding Motif of Complement Type Repeats

2012 ◽  
Vol 288 (6) ◽  
pp. 4424-4435 ◽  
Author(s):  
Robert Dagil ◽  
Charlotte O'Shea ◽  
Anders Nykjær ◽  
Alexandre M. J. J. Bonvin ◽  
Birthe B. Kragelund
Keyword(s):  
Ligand Binding ◽  
Competitive Inhibitor ◽  
Binding Motif ◽  
The Common

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

2021 ◽  
Vol 28 ◽  
Author(s):  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Vital Role ◽  
Atp Binding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Ligand Interactions ◽  
Protein Functions ◽  
Ligand Interactions ◽  
Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

scholarly journals Predicting target-ligand interactions using protein ligand-binding site and ligand substructures

2015 ◽  
Vol 9 (Suppl 1) ◽  
pp. S2 ◽  
Author(s):  
Caihua Wang ◽  
Juan Liu ◽  
Fei Luo ◽  
Zixing Deng ◽  
Qian-Nan Hu
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Ligand Binding Site ◽  
Ligand Interactions

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu
Keyword(s):  
Ligand Binding ◽  
Considerable Improvement ◽  
Ligand Docking ◽  
Water Molecules ◽  
Binding Modes ◽  
Binding Prediction ◽  
Docking Simulations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

scholarly journals Estimation of Binding Rates and Affinities from Multiensemble Markov Models and Ligand Decoupling

2021 ◽  
Author(s):  
Yunhui Ge ◽  
Vincent Voelz
Keyword(s):  
Ligand Binding ◽  
Markov Models ◽  
Time Correlation ◽  
Test System ◽  
Binding Affinities ◽  
Limited Data ◽  
Markov State Model ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Kinetics Of

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.

scholarly journals Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions

2018 ◽  
Vol 47 (2) ◽  
pp. 582-593 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
Mona Singh
Keyword(s):  
Ligand Binding ◽  
Small Molecules ◽  
Binding Sites ◽  
Protein Structures ◽  
Complex Structures ◽  
Disease Etiology ◽  
Systematic Assessment ◽  
Protein Ligand Interactions ◽  
Ligand Binding Sites ◽  
Ligand Interactions

Abstract Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-domain-position interaction ‘frequencies’ by aggregating protein co-complex structures by domain and ascertaining how often residues mapping to each domain position interact with ligands. We perform this domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 domains are highly consistent and can be used to identify residues facilitating interactions in ∼63–69% of human genes. Our resource of domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.

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