Structural Modeling and Ligand-Binding Prediction for Analysis of Structure-Unknown and Function-Unknown Proteins Using FORTE Alignment and PoSSuM Pocket Search

Author(s):  
Yuko Tsuchiya ◽  
Kentaro Tomii
Blood ◽  
2003 ◽  
Vol 101 (9) ◽  
pp. 3485-3491 ◽  
Author(s):  
Teruo Kiyoi ◽  
Yoshiaki Tomiyama ◽  
Shigenori Honda ◽  
Seiji Tadokoro ◽  
Morio Arai ◽  
...  

The molecular basis for the interaction between a prototypic non–I-domain integrin, αIIbβ3, and its ligands remains to be determined. In this study, we have characterized a novel missense mutation (Tyr143His) in αIIb associated with a variant of Glanzmann thrombasthenia. Osaka-12 platelets expressed a substantial amount of αIIbβ3(36%-41% of control) but failed to bind soluble ligands, including a high-affinity αIIbβ3-specific peptidomimetic antagonist. Sequence analysis revealed that Osaka-12 is a compound heterozygote for a single 521T>C substitution leading to a Tyr143His substitution in αIIb and for the null expression of αIIb mRNA from the maternal allele. Given that Tyr143 is located in the W3 4-1 loop of the β-propeller domain of αIIb, we examined the effects of Tyr143His or Tyr143Ala substitution on the expression and function of αIIbβ3 and compared them with KO (Arg-Thr insertion between 160 and 161 residues of αIIb) and with the Asp163Ala mutation located in the same loop by using 293 cells. Each of them abolished the binding function of αIIbβ3 for soluble ligands without disturbing αIIbβ3 expression. Because immobilized fibrinogen and fibrin are higher affinity/avidity ligands for αIIbβ3, we performed cell adhesion and clot retraction assays. In sharp contrast to KO mutation and Asp163AlaαIIbβ3, Tyr143HisαIIbβ3-expressing cells still had some ability for cell adhesion and clot retraction. Thus, the functional defect induced by Tyr143HisαIIb is likely caused by its allosteric effect rather than by a defect in the ligand-binding site itself. These detailed structure–function analyses provide better understanding of the ligand-binding sites in integrins.


2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.


2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu

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.


2001 ◽  
Vol 46 (1) ◽  
pp. 110-127 ◽  
Author(s):  
Ivano Bertini ◽  
Claudio Luchinat ◽  
Alessandro Provenzani ◽  
Antonio Rosato ◽  
Paul R. Vasos

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2374-2374
Author(s):  
Clemens Stockklausner ◽  
Nicole Echner ◽  
Anne-Christine Klotter ◽  
Isabelle Nadine Kuhlee ◽  
Andreas E Kulozik

Abstract Abstract 2374 Thrombopoiesis is tightly regulated by the interaction between thrombopoietin (TPO) and its receptor c-Mpl. Receptor binding also leads to the clearance of TPO from the plasma thus establishing a negative feedback loop. However, it is still an open question how the receptor activates its downstream pathway. Alternative models posit that ligand binding either results in receptor dimerization in the plasma membrane or leads to conformational change of preformed receptor dimers. Several mutations in the TPO and the c-Mpl receptor genes have been linked to either hereditary thrombocytopenia or thrombocytosis. We focused on mutations in the extracellular part of the c-Mpl receptor, where ligand binding and receptor dimerization occur. Mutated homozygous c-Mpl R102P and compound heterozygous R102P/F104S receptors cause severe hereditary thrombocytopenia. In contrast, the homozygous c-Mpl P106L mutation was found in patients with hereditary thrombocytosis. We now addressed the question of how the disparate phenotype of mutations in the same domain of the c-Mpl receptor can be explained. We first functionally analyzed and compared normal with mutated R102P, F104S and P106L c-Mpl receptors in transfected HeLa and BA/F3 cells and found that the normal and the F104S c-Mpl receptors are glycosylated normally by the Golgi apparatus and reach the plasma membrane. In contrast, the R102P and P106L mutated receptors are not fully glycosylated, do not reach the plasma membrane and are atypically distributed in the ER. Functional analysis of the TPO/c-Mpl signaling pathway in BA/F3 cells showed decreased phosphorylation of Stat3, Stat5 and Erk1/2 with the R102P and F104S mutants when compared to normal. By contrast, TPO/c-Mpl signaling was up-regulated in cells transfected with the P106L-mutated receptor. Moreover, the P106L mutant, but not the other mutant receptors, enhanced ligand-independent growth of transfected BA/F3 cells. Despite of their opposite function, the TPO plasma levels of patients carrying both, homozygous R102P and P106L mutations were elevated 10 to 20-fold compared to normal and heterozygous individuals. This finding, together with their impaired glycosylation and inability to reach the plasma membrane, suggests that these mutants do not bind and internalize their ligand. TPO binding and degradation thus requires the receptor to be expressed at the plasma membrane, whereas, surprisingly, c-Mpl P106L activated its signaling pathway in a ligand independent fashion. Correct receptor processing and function can thus be separated. This indicates that TPO binding is required for regulation but that the constitutive activation of c-Mpl P106L is a likely direct consequence of premature receptor dimerization in the ER, auto-phosphorylation and subsequent activation of downstream targets. Disclosures: No relevant conflicts of interest to declare.


2005 ◽  
Vol 19 (6) ◽  
pp. 1516-1528 ◽  
Author(s):  
Anobel Tamrazi ◽  
Kathryn E. Carlson ◽  
Alice L. Rodriguez ◽  
John A. Katzenellenbogen

Abstract The direct regulation of gene transcription by nuclear receptors, such as the estrogen receptor (ER), involves not just ligand and DNA binding but the recruitment of coregulators. Typically, recruitment of p160 coactivator proteins to agonist-liganded ER is considered to be unidirectional, with ligand binding stabilizing an ER ligand binding domain (LBD) conformation that favors coactivator interaction. Using fluorophore-labeled ERα-LBDs, we present evidence for a pronounced stabilization of ER conformation that results from coactivator binding, manifest by decreased ER sensitivity to proteases and reduced conformational dynamics, as well as for the formation of a novel coactivator-stabilized (costabilized) receptor conformation, that can be conveniently monitored by the generation of an excimer emission from pyrene-labeled ERα-LBDs. This costabilized conformation may embody features required to support ER transcriptional activity. Different classes of coactivator proteins combine with estrogen agonists of different structure to elicit varying degrees of this receptor stabilization, and antagonists and coactivator binding inhibitors disfavor the costabilized conformation. Remarkably, high concentrations of coactivators engender this conformation even in apo- and antagonist-bound ERs (more so with selective ER modulators than with pure antagonists), providing an in vitro model for the development of resistance to hormone therapy in breast cancer.


Biochemistry ◽  
1991 ◽  
Vol 30 (16) ◽  
pp. 3988-4001 ◽  
Author(s):  
Peter J. Steinbach ◽  
Anjum Ansari ◽  
Joel Berendzen ◽  
David Braunstein ◽  
Kelvin Chu ◽  
...  

2009 ◽  
Vol 42 (4) ◽  
pp. 341-356 ◽  
Author(s):  
Dellel Rezgui ◽  
Christopher Williams ◽  
Sharon A Savage ◽  
Stuart N Prince ◽  
Oliver J Zaccheo ◽  
...  

The mannose 6-phosphate/IGF 2 receptor (IGF2R) is comprised of 15 extra-cellular domains that bind IGF2 and mannose 6-phosphate ligands. IGF2R transports ligands from the Golgi to the pre-lysosomal compartment and thereafter to and from the cell surface. IGF2R regulates growth, placental development, tumour suppression and signalling. The ligand IGF2 is implicated in the growth phenotype, where IGF2R normally limits bioavailability, such that loss and gain of IGF2R results in increased and reduced growth respectively. The IGF2R exon 34 (5002A>G) polymorphism (rs629849) of the IGF2 specific binding domain has been correlated with impaired childhood growth (A/A homozygotes). We evaluated the function of the Gly1619Arg non-synonymous amino acid modification of domain 11. NMR and X-ray crystallography structures located 1619 remote from the ligand binding region of domain 11. Arg1619 was located close to the fibronectin type II (FnII) domain of domain 13, previously implicated as a modifier of IGF2 ligand binding through indirect interaction with the AB loop of the binding cleft. However, comparison of binding kinetics of IGF2R, Gly1619 and Arg1619 to either IGF2 or mannose 6-phosphate revealed no differences in ‘on’ and ‘off’ rates. Quantitative PCR, 35S pulse chase and flow cytometry failed to demonstrate altered gene expression, protein half-life and cell membrane distribution, suggesting the polymorphism had no direct effect on receptor function. Intronic polymorphisms were identified which may be in linkage disequilibrium with rs629849 in certain populations. Other potential IGF2R polymorphisms may account for the correlation with childhood growth, warranting further functional evaluation.


2020 ◽  
Vol 6 ◽  
pp. e253
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.


Sign in / Sign up

Export Citation Format

Share Document