Contribution to the Understanding of Protein–Protein Interface and Ligand Binding Site Based on Hydrophobicity Distribution—Application to Ferredoxin I and II Cases

Ferredoxin I and II are proteins carrying a specific ligand—an iron-sulfur cluster—which allows transport of electrons. These two classes of ferredoxin in their monomeric and dimeric forms are the object of this work. Characteristic of hydrophobic core in both molecules is analyzed via fuzzy oil drop model (FOD) to show the specificity of their structure enabling the binding of a relatively large ligand and formation of the complex. Structures of FdI and FdII are a promising example for the discussion of influence of hydrophobicity on biological activity but also for an explanation how FOD model can be used as an initial stage adviser (or a scoring function) in the search for locations of ligand binding pockets and protein–protein interaction areas. It is shown that observation of peculiarities in the hydrophobicity distribution present in the molecule (in this case—of a ferredoxin) may provide a promising starting location for computer simulations aimed at the prediction of quaternary structure of proteins.

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Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations

Scientific Reports ◽

10.1038/s41598-021-84488-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gert-Jan Bekker ◽

Ikuo Fukuda ◽

Junichi Higo ◽

Yoshifumi Fukunishi ◽

Narutoshi Kamiya

Keyword(s):

Ligand Binding ◽

Cancer Progression ◽

Hydrophobic Interactions ◽

Binding Mechanism ◽

Docking Simulations ◽

Large Conformational Change ◽

Binding Pockets ◽

Dynamic Docking ◽

Site Binding ◽

Apo State

AbstractWe have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

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Identification of Potential Kinase Inhibitors within the PI3K/AKT Pathway of Leishmania Species

Biomolecules ◽

10.3390/biom11071037 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1037

Author(s):

Rodrigo Ochoa ◽

Amaya Ortega-Pajares ◽

Florencia A. Castello ◽

Federico Serral ◽

Darío Fernández Do Porto ◽

...

Keyword(s):

Kinase Inhibitors ◽

Screening Method ◽

Molecular Targets ◽

Leishmania Species ◽

Akt Pathway ◽

Protein Protein Interaction ◽

Starting Point ◽

Binding Pockets ◽

Protein Protein Interaction Networks

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases’ participation in protein–protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti-Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

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Architectural Repertoire of Ligand-Binding Pockets on Protein Surfaces

ChemBioChem ◽

10.1002/cbic.200900604 ◽

2010 ◽

Vol 11 (4) ◽

pp. 556-563 ◽

Cited By ~ 18

Author(s):

Martin Weisel ◽

Jan M. Kriegl ◽

Gisbert Schneider

Keyword(s):

Ligand Binding ◽

Protein Surfaces ◽

Binding Pockets

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Functional CBS modules make IMPDHs octameric

Acta Crystallographica Section A Foundations and Advances ◽

10.1107/s2053273314087166 ◽

2014 ◽

Vol 70 (a1) ◽

pp. C1283-C1283

Author(s):

Gilles Labesse ◽

Thomas Alexandre ◽

Laurène Vaupré ◽

Isabelle Salard-Arnaud ◽

Joséphine Lai Kee Him ◽

...

Keyword(s):

Analytical Ultracentrifugation ◽

Quaternary Structure ◽

Catalytic Domain ◽

Structural Data ◽

Site Directed Mutagenesis ◽

X Ray Crystallography ◽

Binding Pockets ◽

Essential Enzyme ◽

Cbs Domains

Inosine-5'-monophosphate dehydrogenase (1, 2) (IMPDH) is a major target for antiviral, antiparasitic, antileukemic and immunosuppressive therapies. It is an ubiquitous and essential enzyme for the biosynthesis of guanosine nucleotides. Up to now, IMPDHs have been reported as tetrameric enzymes harbouring a catalytic domain and a tandem of cystathionine-ß-synthase (CBS) modules. The latter had no precise function assigned despite their nearly absolute conservation among IMPDHs and consistent indication of their importance in vivo. The aim of our study was to provide evidence for the role of the CBS modules on the quaternary structure and on the regulation of IMPDHs. A multidisciplinary approach involving enzymology, site-directed mutagenesis, analytical ultracentrifugation, X-ray crystallography, SAXS, cryo-electron microscopy and molecular modelling allowed us to demonstrate that the Pseudomonas aeruginosa IMPDH is functionally active as an octamer and allosterically regulated by MgATP via each CBS module. Revisiting deposited structural data, we found this newly discovered octameric organization conserved in other IMPDH structures. Meanwhile, we demonstrated that the human IMPDH1 formed two distinct octamers that can pile up into isolated fibres in the presence of MgATP while its pathogenic mutant D226N, localised into the CBS domains, appeared to form massively aggregating fibres. The dramatic impact of this mutation could explain the severe retinopathy adRP10. Our data (3) revealed for the first time that IMPDH has an octameric architecture modulated by MgATP binding to the CBS modules, inducing large structural rearrangements. Thus, the regulatory CBS modules in IMPDHs are functional and they can either modulate catalysis or/and macromolecular assembly. Targeting the conserved effector binding pockets identified within the CBS modules might be promising to develop antibacterial compounds or drugs to counter retinopathy onset.

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Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

10.26434/chemrxiv.13637756 ◽

2021 ◽

Author(s):

Fergus Boyles ◽

Charlotte M Deane ◽

Garrett Morris

Keyword(s):

Machine Learning ◽

Ligand Binding ◽

Crystal Structures ◽

Binding Affinity ◽

Scoring Function ◽

Scoring Functions ◽

Data Set ◽

Core Sets ◽

Strong Performance

Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes.<br><br>We explore how the use of docked, rather than crystallographic, poses for both training and testing affects the performance of machine learning scoring functions. Using the PDBbind Core Sets as benchmarks, we show that the performance of a structure-based machine learning scoring function trained and tested on docked poses is lower than that of the same scoring function trained and tested on crystallographic poses. We construct a hybrid scoring function by combining both structure-based and ligand-based features, and show that its ability to predict binding affinity using docked poses is comparable to that of purely structure-based scoring functions trained and tested on crystal poses. Despite strong performance on docked poses of the PDBbind Core Sets, we find that our hybrid scoring function fails to generalise to anew data set, demonstrating the need for improved scoring functions and additional validation benchmarks. <br><br>Code and data to reproduce our results are available from https://github.com/oxpig/learning-from-docked-poses.

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Design, synthesis and bioevaluation of inhibitors targeting HSP90-CDC37 protein-protein interaction based on a hydrophobic core

European Journal of Medicinal Chemistry ◽

10.1016/j.ejmech.2020.112959 ◽

2021 ◽

Vol 210 ◽

pp. 112959

Author(s):

Qiuyue Zhang ◽

Xuexuan Wu ◽

Jianrui Zhou ◽

Lixiao Zhang ◽

Xiaoli Xu ◽

...

Keyword(s):

Protein Interaction ◽

Hydrophobic Core ◽

Design Synthesis ◽

Protein Protein Interaction

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BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

Bioinformatics ◽

10.1093/bioinformatics/btaa094 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3077-3083

Author(s):

Wentao Shi ◽

Jeffrey M Lemoine ◽

Abd-El-Monsif A Shawky ◽

Manali Singha ◽

Limeng Pu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Supplementary Information ◽

Heme Binding ◽

Unseen Data ◽

Ligand Binding Sites ◽

Binding Pockets

Abstract Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.

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Mechanism of ligand binding – PDZ domain taken as example

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2017-0022 ◽

2017 ◽

Vol 13 (4) ◽

Author(s):

Dawid Dułak ◽

Mateusz Banach ◽

Zdzisław Wiśniowski ◽

Leszek Konieczny ◽

Irena Roterman

Keyword(s):

Crystal Structure ◽

Ligand Binding ◽

Ion Channel ◽

Pdz Domain ◽

Traditional Model ◽

Beta Sheet ◽

Hydrophobic Core ◽

Beta Strand ◽

Model Based ◽

Specific Ligand

AbstractThe mechanism of specific ligand binding by proteins is discussed using the PDZ domain complexing the pentapeptide. This process is critical for clustering the membrane ion channel. The traditional model based on the Beta-sheet extension by complexed pentapeptide is interpreted as a hydrophobic core extension supported by additional Beta-strand generated by complexed pentapeptide. The explanation is based on the fuzzy oil drop model applied to the crystal structure of PDZ-pentapeptide.

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