scholarly journals A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

2020 ◽  
Author(s):  
Jonas Gossen ◽  
Simone Albani ◽  
Anton Hanke ◽  
Benjamin P. Joseph ◽  
Cathrine Bergh ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Active Sites ◽  
Chemical Space ◽  
Protein Structures ◽  
Rational Drug Design ◽  
In Vitro Assays ◽  
Strong Basis ◽  
High Affinity ◽  
Compound Libraries

AbstractThe SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

scholarly journals Identification of protein pockets and cavities by Euclidean Distance Transform

2018 ◽  
Author(s):  
Sebastian Daberdaku
Keyword(s):  
Ligand Binding ◽  
Active Sites ◽  
Euclidean Distance ◽  
Protein Structures ◽  
Ligand Docking ◽  
Distance Transform ◽  
Structure Based Drug Design ◽  
Distance Map ◽  
Euclidean Distance Transform ◽  
Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

Generation of an agonistic binding site for blockers of the M3 muscarinic acetylcholine receptor

2008 ◽  
Vol 412 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Doreen Thor ◽  
Angela Schulz ◽  
Thomas Hermsdorf ◽  
Torsten Schöneberg
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
G Protein ◽  
Binding Sites ◽  
Expression System ◽  
Muscarinic Acetylcholine Receptor ◽  
Intracellular Loop ◽  
Inverse Agonists ◽  
Yeast Expression System ◽  
High Affinity

GPCRs (G-protein-coupled receptors) exist in a spontaneous equilibrium between active and inactive conformations that are stabilized by agonists and inverse agonists respectively. Because ligand binding of agonists and inverse agonists often occurs in a competitive manner, one can assume an overlap between both binding sites. Only a few studies report mutations in GPCRs that convert receptor blockers into agonists by unknown mechanisms. Taking advantage of a genetically modified yeast strain, we screened libraries of mutant M3Rs {M3 mAChRs [muscarinic ACh (acetylcholine) receptors)]} and identified 13 mutants which could be activated by atropine (EC50 0.3–10 μM), an inverse agonist on wild-type M3R. Many of the mutations sensitizing M3R to atropine activation were located at the junction of intracellular loop 3 and helix 6, a region known to be involved in G-protein coupling. In addition to atropine, the pharmacological switch was found for other M3R blockers such as scopolamine, pirenzepine and oxybutynine. However, atropine functions as an agonist on the mutant M3R only when expressed in yeast, but not in mammalian COS-7 cells, although high-affinity ligand binding was comparable in both expression systems. Interestingly, we found that atropine still blocks carbachol-induced activation of the M3R mutants in the yeast expression system by binding at the high-affinity-binding site (Ki ∼10 nM). Our results indicate that blocker-to-agonist converting mutations enable atropine to function as both agonist and antagonist by interaction with two functionally distinct binding sites.

scholarly journals Characterization of the High-Affinity Drug Ligand Binding Site of Mouse Recombinant TSPO

2019 ◽  
Vol 20 (6) ◽  
pp. 1444 ◽  
Author(s):  
Soria Iatmanen-Harbi ◽  
lucile Senicourt ◽  
Vassilios Papadopoulos ◽  
Olivier Lequin ◽  
Jean-Jacques Lacapere
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Conformational Changes ◽  
Protoporphyrin Ix ◽  
Binding Pocket ◽  
Site Directed Mutagenesis ◽  
Translocator Protein ◽  
Binding Affinities ◽  
Ligand Selectivity ◽  
High Affinity

The optimization of translocator protein (TSPO) ligands for Positron Emission Tomography as well as for the modulation of neurosteroids is a critical necessity for the development of TSPO-based diagnostics and therapeutics of neuropsychiatrics and neurodegenerative disorders. Structural hints on the interaction site and ligand binding mechanism are essential for the development of efficient TSPO ligands. Recently published atomic structures of recombinant mammalian and bacterial TSPO1, bound with either the high-affinity drug ligand PK 11195 or protoporphyrin IX, have revealed the membrane protein topology and the ligand binding pocket. The ligand is surrounded by amino acids from the five transmembrane helices as well as the cytosolic loops. However, the precise mechanism of ligand binding remains unknown. Previous biochemical studies had suggested that ligand selectivity and binding was governed by these loops. We performed site-directed mutagenesis to further test this hypothesis and measured the binding affinities. We show that aromatic residues (Y34 and F100) from the cytosolic loops contribute to PK 11195 access to its binding site. Limited proteolytic digestion, circular dichroism and solution two-dimensional (2-D) NMR using selective amino acid labelling provide information on the intramolecular flexibility and conformational changes in the TSPO structure upon PK 11195 binding. We also discuss the differences in the PK 11195 binding affinities and the primary structure between TSPO (TSPO1) and its paralogous gene product TSPO2.

scholarly journals PDBspheres - a method for finding 3D similarities in local regions in proteins

2022 ◽  
Author(s):  
Adam Zemla ◽  
Jonathan E. Allen ◽  
Dan Kirshner ◽  
Felice C. Lightstone
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Structures ◽  
Data Bank ◽  
Structure Alignment ◽  
Alignment Algorithm ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Structure Similarity

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions (spheres) adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. Currently, PDBspheres library contains more than 2 million spheres, organized to facilitate searches by sequence and/or structure similarity of protein-ligand binding sites or interfaces between interacting molecules. PDBspheres uses the LGA structure alignment algorithm as the main engine for detecting structure similarities between the protein of interest and library spheres. An all-atom structure similarity metric ensures that sidechain placement is taken into account in the PDBspheres primary assessment of confidence in structural matches. In this paper, we (1) describe the PDBspheres method, (2) demonstrate how PDBspheres can be used to detect and characterize binding sites in protein structures, (3) compare PDBspheres use for binding site prediction with seven other binding site prediction methods using a curated dataset of 2,528 ligand-bound and ligand-free crystal structures, and (4) use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of the 4,876 structures in the refined set of PDBbind 2019 dataset. The PDBspheres library is made publicly available for download at https://proteinmodel.org/AS2TS/PDBspheres

scholarly journals DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

2021 ◽  
Author(s):  
Rishal Aggarwal ◽  
Akash Gupta ◽  
Vineeth Chelur ◽  
C. V. Jawahar ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Site ◽  
Convolutional Neural Networks ◽  
Binding Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Ligand Binding Site ◽  
Structure Based Drug Design

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

scholarly journals Noncontiguous regions in the extracellular domain of EGF receptor define ligand-binding specificity.

1991 ◽  
Vol 2 (5) ◽  
pp. 337-345 ◽  
Author(s):  
I Lax ◽  
R Fischer ◽  
C Ng ◽  
J Segre ◽  
A Ullrich ◽  
...  
Keyword(s):  
Binding Energy ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Affinity ◽  
Egf Receptor ◽  
Binding Properties ◽  
Amino Terminal ◽  
High Affinity ◽  
Domain Iii ◽  
Domain I

Murine epidermal growth factor (EGF) binds with approximately 250-fold higher binding affinity to the human EGF receptor (EGFR) than to the chicken EGFR. This difference in binding affinity enabled the identification of a major ligand-binding domain for EGF by studying the binding properties of various chicken/human EGFR chimera expressed in transfected cells lacking endogenous EGFR. It was shown that domain III of EGFR is a major ligand-binding region. Here, we analyze the binding properties of novel chicken/human chimera to further delineate the contact sequences in domain III and to assess the role of other regions of EGFR for their contribution to the display of high-affinity EGF binding. The chimeric receptors include chicken EGFR containing domain I of the human EGFR, chicken receptor containing domain I and III of the human EGFR, and two chimeric chicken EGFR containing either the amino terminal or the carboxy terminal halves of domain III of human EGFR, respectively. In addition, the binding of various human-specific anti-EGFR monoclonal antibodies that interfere with EGF binding is also compared. It is concluded that noncontiguous regions of the EGFR contribute additively to the binding of EGF. Each of the two halves of domain III has a similar contribution to the binding energy, and the sum of both is close to that of the entire domain III. This suggests that the folding of domain III juxtaposes sequences that together constitute the ligand-binding site. Domain I also provides a contribution to the binding energy, and the added contributions of both domain I and III to the binding energy generate the high-affinity binding site typical of human EGFR.

scholarly journals Binding site matching in rational drug design: algorithms and applications

2018 ◽  
Vol 20 (6) ◽  
pp. 2167-2184 ◽  
Author(s):  
Misagh Naderi ◽  
Jeffrey Mitchell Lemoine ◽  
Rajiv Gandhi Govindaraj ◽  
Omar Zade Kana ◽  
Wei Pan Feinstein ◽  
...  
Keyword(s):  
Small Molecules ◽  
Binding Site ◽  
Protein Structures ◽  
Chemical Properties ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
Basic Knowledge ◽  
Rational Drug ◽  
Binding Pockets ◽  
Local Protein

Abstract Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

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