scholarly journals Cloud Computing for Protein-Ligand Binding Site Comparison

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Che-Lun Hung ◽  
Guan-Jie Hua
Keyword(s):  
Cloud Computing ◽  
Ligand Binding ◽  
Binding Site ◽  
High Performance ◽  
Cross Reactivity ◽  
Ligand Binding Site ◽  
Structure Based Drug Design ◽  
Computing Platform ◽  
Wide Range ◽  
Cloud Computing Service

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.

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 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>

New opportunities for protease ligand-binding site comparisons using SitesBase

2007 ◽  
Vol 35 (3) ◽  
pp. 561-565 ◽  
Author(s):  
N.D. Gold ◽  
K. Deville ◽  
R.M. Jackson
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Structural Information ◽  
Cross Reactivity ◽  
Rapid Expansion ◽  
Structure Based Drug Design ◽  
Structure Database ◽  
Ligand Binding Sites ◽  
Source Of Information

The rapid expansion of structural information for protein ligand-binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross-reactivity and toxicity. We have developed SitesBase, a comprehensive database of ligand-binding sites extracted automatically from the Macromolecular Structure Database. SitesBase is an easily accessible database which is simple to use and holds pre-calculated information about structural similarities between known ligand-binding sites. These similarities are presented to the wider community enabling binding-site comparisons for therapeutically interesting protein families, such as the proteases and for new proteins to enable the discovery of interesting new structure–function relationships. The database is available from http://www.modelling.leeds.ac.uk/sb/.

Constraints of Opsin Structure on the Ligand-binding Site: Studies with Ring-fused Retinals¶

2002 ◽  
Vol 76 (6) ◽  
pp. 606 ◽  
Author(s):  
Takahiro Hirano ◽  
In Taek Lim ◽  
Don Moon Kim ◽  
Xiang-Guo Zheng ◽  
Kazuo Yoshihara ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Ligand Binding Site

Capturing of the free cysteine residue in the ligand-binding site by affinity labeling of the ORL1 nociceptin receptor

2011 ◽  
Vol 19 (24) ◽  
pp. 7597-7602 ◽  
Author(s):  
Ayami Matsushima ◽  
Hirokazu Nishimura ◽  
Shogo Inamine ◽  
Shiho Uemura ◽  
Yasuyuki Shimohigashi
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Cysteine Residue ◽  
Affinity Labeling ◽  
Ligand Binding Site ◽  
Nociceptin Receptor

A Monoclonal Antibody against the Ligand Binding Site of the Receptor for Mouse Interferon-γ

1989 ◽  
Vol 9 (5) ◽  
pp. 551-562 ◽  
Author(s):  
MITALI BASU ◽  
JUDITH L. PACE ◽  
DAVID M. PINSON ◽  
STEPHEN W. RUSSELL
Keyword(s):  
Monoclonal Antibody ◽  
Ligand Binding ◽  
Binding Site ◽  
Interferon Γ ◽  
Ligand Binding Site

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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