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

2004 ◽  
Vol 44 (6) ◽  
pp. 2190-2198 ◽  
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

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

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 Spatiotemporal identification of druggable binding sites using deep learning

2020 ◽  
Author(s):  
Igor Kozlovskii ◽  
Petr Popov
Keyword(s):  
Protein Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Large Scale ◽  
Specific Binding ◽  
Three Dimensional ◽  
Growth Factor Receptor ◽  
Protein Binding Sites ◽  
Binding Site Identification ◽  
Scale Detection

Identification of novel protein binding sites expands «druggable genome» and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble to object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, named BiteNet, that considers protein conformations as the 3D-images, binding sites as the objects on these images to detect, and conformational ensembles of proteins as the 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding sites in G protein-coupled receptors. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minute to analyze 1000 conformations of a protein with 2000 atoms. BiteNet is available at https://github.com/i-Molecule/bitenet.

scholarly journals Spatiotemporal identification of druggable binding sites using deep learning

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Igor Kozlovskii ◽  
Petr Popov
Keyword(s):  
Protein Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Large Scale ◽  
Specific Binding ◽  
Three Dimensional ◽  
Growth Factor Receptor ◽  
Protein Binding Sites ◽  
Binding Site Identification ◽  
Scale Detection

Abstract Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.

scholarly journals DeeplyTough: Learning Structural Comparison of Protein Binding Sites

2019 ◽  
Author(s):  
Martin Simonovsky ◽  
Joshua Meyers
Keyword(s):  
Protein Binding ◽  
Binding Sites ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Binding Sites ◽  
Data Driven Approach ◽  
Benchmark Datasets ◽  
New Perspective

AbstractMotivationProtein binding site comparison (pocket matching) is of importance in drug discovery. Identification of similar binding sites can help guide efforts for hit finding, understanding polypharmacology and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition, and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective.ResultsWe propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein binding sites into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision: (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently.Availabilityhttps://github.com/BenevolentAI/[email protected],[email protected]

EVALUATION OF TISSUE STEROID BINDING IN VITRO

1971 ◽  
Vol 68 (1_Suppl) ◽  
pp. S223-S246 ◽  
Author(s):  
C. R. Wira ◽  
H. Rochefort ◽  
E. E. Baulieu
Keyword(s):  
Protein Binding ◽  
Binding Sites ◽  
Experimental System ◽  
Specific Protein ◽  
Coupling Mechanism ◽  
Target Tissue ◽  
Protein Binding Sites ◽  
Definition Of ◽  
Hormone Specificity

ABSTRACT The definition of a RECEPTOR* in terms of a receptive site, an executive site and a coupling mechanism, is followed by a general consideration of four binding criteria, which include hormone specificity, tissue specificity, high affinity and saturation, essential for distinguishing between specific and nonspecific binding. Experimental approaches are proposed for choosing an experimental system (either organized or soluble) and detecting the presence of protein binding sites. Techniques are then presented for evaluating the specific protein binding sites (receptors) in terms of the four criteria. This is followed by a brief consideration of how receptors may be located in cells and characterized when extracted. Finally various examples of oestrogen, androgen, progestagen, glucocorticoid and mineralocorticoid binding to their respective target tissues are presented, to illustrate how researchers have identified specific corticoid and mineralocorticoid binding in their respective target tissue receptors.

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