Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

2020 ◽  
Vol 124 (45) ◽  
pp. 9977-9986
Author(s):  
Shinji Iida ◽  
Hironori K. Nakamura ◽  
Tadaaki Mashimo ◽  
Yoshifumi Fukunishi
Keyword(s):  
Drug Design ◽  
Binding Sites ◽  
Aromatic Residues ◽  
Cryptic Binding Sites ◽  
Structural Fluctuations

scholarly journals Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites

2017 ◽  
Vol 4 (3) ◽  
pp. 032104 ◽  
Author(s):  
Nicholas M. Pearce ◽  
Anthony R. Bradley ◽  
Tobias Krojer ◽  
Brian D. Marsden ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Binding Sites ◽  
Analysis Method ◽  
Density Analysis ◽  
Cryptic Binding Sites

3D U-Net: A voxel-based method in binding site prediction of protein structure

2021 ◽  
Vol 19 (02) ◽  
pp. 2150006
Author(s):  
Fatemeh Nazem ◽  
Fahimeh Ghasemi ◽  
Afshin Fassihi ◽  
Alireza Mehri Dehnavi
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Binding Sites ◽  
Semantic Segmentation ◽  
Structure Based Drug Design ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Novel Proteins ◽  
Proposed Model ◽  
Deep Learning Model

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

The interaction of human glycophorin with 8-anilino-1-naphthalene sulfonate

1977 ◽  
Vol 55 (9) ◽  
pp. 942-948 ◽  
Author(s):  
Jacob A. Verpoorte
Keyword(s):  
Sialic Acid ◽  
Binding Sites ◽  
Equilibrium Dialysis ◽  
Erythrocyte Membranes ◽  
Ans Binding ◽  
Aromatic Residues ◽  
Apparent Binding Constant ◽  
Human Erythrocyte Membranes ◽  
Low Ionic Strength ◽  
Human Glycophorin

Both the sialoglycoprotein of human erythrocyte membranes, glycophorin, and the sialic acid free protein, obtained by treatment of glycophorin with neuraminidase (EC 3.2.1.18), increase the fluorescence of 8-anilino-1-naphthalene sulfonate (ANS). Binding of ANS to glycophorin is weak compared with the binding to bovine serum albumin (BSA). Equilibrium dialysis gives an apparent binding constant of about 4 × 103 M−1 at neutral pH, but Ka increases 1.75 times when NaCl or CaCl2 are added and 10-fold when the pH is lowered to 3.0. Sialic acid groups do not significantly affect ANS binding, although they have some effect at low ionic strength and neutral pH.Fluorescence studies indicate only one to two binding sites for ANS, with apparent pK = 3.8 ± 0.2. and located close to aromatic residues in glycophorin.Polarization and quantum efficiency of the fluorescence of ANS associated with glycophorin fail to indicate changes in the vicinity of the binding site when the pH is lowered.

scholarly journals Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

2020 ◽  
Vol 53 (3) ◽  
pp. 654-661 ◽  
Author(s):  
Antonija Kuzmanic ◽  
Gregory R. Bowman ◽  
Jordi Juarez-Jimenez ◽  
Julien Michel ◽  
Francesco L. Gervasio
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Sites ◽  
Cryptic Binding Sites ◽  
Dynamics Simulations

scholarly journals Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

2016 ◽  
Vol 113 (50) ◽  
pp. E8051-E8058 ◽  
Author(s):  
Fang Bai ◽  
Faruck Morcos ◽  
Ryan R. Cheng ◽  
Hualiang Jiang ◽  
José N. Onuchic
Keyword(s):  
Drug Design ◽  
Protein Interactions ◽  
Binding Sites ◽  
Hot Spots ◽  
Therapeutic Agents ◽  
Molecular Probes ◽  
Therapeutic Drug ◽  
Protein Protein Interactions ◽  
Protein Surface ◽  
Fragment Docking

Protein−protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein−protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

Structural basis of carbohydrate binding in domain C of a type I pullulanase from Paenibacillus barengoltzii

2020 ◽  
Vol 76 (5) ◽  
pp. 447-457
Author(s):  
Ping Huang ◽  
Shiwang Wu ◽  
Shaoqing Yang ◽  
Qiaojuan Yan ◽  
Zhengqiang Jiang
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Structural Basis ◽  
Carbohydrate Binding ◽  
Type I ◽  
Debranching Enzyme ◽  
Crystal Forms ◽  
Aromatic Residues ◽  
Starch Debranching Enzyme ◽  
Paenibacillus Barengoltzii

Pullulanase (EC 3.2.1.41) is a well known starch-debranching enzyme that catalyzes the cleavage of α-1,6-glycosidic linkages in α-glucans such as starch and pullulan. Crystal structures of a type I pullulanase from Paenibacillus barengoltzii (PbPulA) and of PbPulA in complex with maltopentaose (G5), maltohexaose (G6)/α-cyclodextrin (α-CD) and β-cyclodextrin (β-CD) were determined in order to better understand substrate binding to this enzyme. PbPulA belongs to glycoside hydrolase (GH) family 13 subfamily 14 and is composed of three domains (CBM48, A and C). Three carbohydrate-binding sites identified in PbPulA were located in CBM48, near the active site and in domain C, respectively. The binding site in CBM48 was specific for β-CD, while that in domain C has not been reported for other pullulanases. The domain C binding site had higher affinity for α-CD than for G6; a small motif (FGGEH) seemed to be one of the major determinants for carbohydrate binding in this domain. Structure-based mutations of several surface-exposed aromatic residues in CBM48 and domain C had a debilitating effect on the activity of the enzyme. These results suggest that both CBM48 and domain C play a role in binding substrates. The crystal forms described contribute to the understanding of pullulanase domain–carbohydrate interactions.

A novel approach to local similarity of protein binding sites substantially improves computational drug design results

2007 ◽  
Vol 69 (2) ◽  
pp. 349-357 ◽  
Author(s):  
Vasily Ramensky ◽  
Alexandr Sobol ◽  
Natalia Zaitseva ◽  
Anatoly Rubinov ◽  
Victor Zosimov
Keyword(s):  
Drug Design ◽  
Protein Binding ◽  
Binding Sites ◽  
Local Similarity ◽  
Computational Drug Design ◽  
Protein Binding Sites ◽  
Novel Approach

scholarly journals Cryptic binding sites on proteins: definition, detection, and druggability

2018 ◽  
Vol 44 ◽  
pp. 1-8 ◽  
Author(s):  
Sandor Vajda ◽  
Dmitri Beglov ◽  
Amanda E Wakefield ◽  
Megan Egbert ◽  
Adrian Whitty
Keyword(s):  
Binding Sites ◽  
Cryptic Binding Sites

SitePrint: Three-Dimensional Pharmacophore Descriptors Derived from Protein Binding Sites for Family Based Active Site Analysis, Classification, and Drug Design.

ChemInform ◽  
2005 ◽  
Vol 36 (9) ◽  
Author(s):  
James R. Arnold ◽  
Keith W. Burdick ◽  
Scott C.-H. Pegg ◽  
Samuel Toba ◽  
Michelle L. Lamb ◽  
...  
Keyword(s):  
Drug Design ◽  
Protein Binding ◽  
Active Site ◽  
Binding Sites ◽  
Three Dimensional ◽  
Protein Binding Sites ◽  
Site Analysis ◽  
Family Based
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