Simple Ligand-Receptor Interaction Descriptor (SILIRID) for alignment-free binding site comparison

被引:23
作者
Chupakhin, Vladimir [1 ]
Marcou, Gilles [1 ]
Gaspar, Helena [1 ]
Varnek, Alexandre [1 ]
机构
[1] Univ Strasbourg, Lab Chemoinformat, UMR 7140, Strasbourg, France
来源
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | 2014年 / 10卷 / 16期
关键词
Protein-ligand interactions; Interaction fingerprints; Protein similarity; Protein classification; Chemogenomics; Generative Topographic Mapping;
D O I
10.1016/j.csbj.2014.05.004
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Wedescribe SILIRID (Simple Ligand-Receptor Interaction Descriptor), a novel fixed size descriptor characterizing protein-ligand interactions. SILIRID can be obtained from the binary interaction fingerprints (IFPs) by summing up the bits corresponding to identical amino acids. This results in a vector of 168 integer numbers corresponding to the product of the number of entries (20 amino acids and one cofactor) and 8 interaction types per amino acid (hydrophobic, aromatic face to face, aromatic edge to face, H-bond donated by the protein, H-bond donated by the ligand, ionic bond with protein cation and protein anion, and interaction with metal ion). Efficiency of SILIRID to distinguish different protein binding sites has been examined in similarity search in sc-PDB database, a druggable portion of the Protein Data Bank, using various protein-ligand complexes as queries. The performance of retrieval of structurally and evolutionary related classes of proteins was comparable to that of state-of-the-art approaches (ROC AUC approximate to 0.91). SILIRID can efficiently be used to visualize chemogenomic space covered by sc-PDB using Generative Topographic Mapping (GTM): sc-PDB SILIRID data form clusters corresponding to different protein types. (C) 2014 Chupakhin et. al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology.
引用
收藏
页码:33 / 37
页数:5
相关论文
共 27 条
[1]  
Aung Z, 2008, GENOME INFORM SER, V21, P65
[2]   Predicting Ligand Binding Modes from Neural Networks Trained on Protein-Ligand Interaction Fingerprints [J].
Chupakhin, Vladimir ;
Marcou, Gilles ;
Baskin, Igor ;
Varnek, Alexandre ;
Rognan, Didier .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2013, 53 (04) :763-772
[3]   PESDserv: a server for high-throughput comparison of protein binding site surfaces [J].
Das, Sourav ;
Krein, Michael P. ;
Breneman, Curt M. .
BIOINFORMATICS, 2010, 26 (15) :1913-1914
[4]   Rapid Comparison of Protein Binding Site Surfaces with Property Encoded Shape Distributions [J].
Das, Sourav ;
Kokardekar, Arshad ;
Breneman, Curt M. .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2009, 49 (12) :2863-2872
[5]   Binding of Protein Kinase Inhibitors to Synapsin I Inferred from Pair-Wise Binding Site Similarity Measurements [J].
De Franchi, Enrico ;
Schalon, Claire ;
Messa, Mirko ;
Onofri, Franco ;
Benfenati, Fabio ;
Rognan, Didier .
PLOS ONE, 2010, 5 (08)
[6]   Structural interaction fingerprint (SIFt): A novel method for analyzing three-dimensional protein-ligand binding interactions [J].
Deng, Z ;
Chuaqui, C ;
Singh, J .
JOURNAL OF MEDICINAL CHEMISTRY, 2004, 47 (02) :337-344
[7]   Generative Topographic Mapping-Based Classification Models and Their Applicability Domain: Application to the Biopharmaceutics Drug Disposition Classification System (BDDCS) [J].
Gaspar, Helena A. ;
Marcou, Gilles ;
Horvath, Dragos ;
Arault, Alban ;
Lozano, Sylvain ;
Vayer, Philippe ;
Varnek, Alexandre .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2013, 53 (12) :3318-3325
[8]   A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction [J].
Hoffmann, Brice ;
Zaslavskiy, Mikhail ;
Vert, Jean-Philippe ;
Stoven, Veronique .
BMC BIOINFORMATICS, 2010, 11
[9]   MOLECULAR RECOGNITION OF RECEPTOR-SITES USING A GENETIC ALGORITHM WITH A DESCRIPTION OF DESOLVATION [J].
JONES, G ;
WILLETT, P ;
GLEN, RC .
JOURNAL OF MOLECULAR BIOLOGY, 1995, 245 (01) :43-53
[10]   Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison [J].
Kireeva, N. ;
Baskin, I. I. ;
Gaspar, H. A. ;
Horvath, D. ;
Marcou, G. ;
Varnek, A. .
MOLECULAR INFORMATICS, 2012, 31 (3-4) :301-312