PDBLIG:  Classification of Small Molecular Protein Binding in the Protein Data Bank

Chalk, Andrew J.; Worth, Catherine L.; Overington, John P.; Chan, A. W. Edith

doi:10.1021/jm040804f

Cited by 40 publications

(33 citation statements)

References 45 publications

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“…Other studies have examined nucleotide cofactor binding in a range of domains and ligands. [15][16][17][18] A more recent and comprehensive survey covering the whole PDB is the PDBLIG database, 19 where the binding of all ligands within the PDB is associated with domains of the CATH structural classification. Although we also associate the binding of PDB ligands to domains, our work differs from the PDBLIG database, in that we have mapped the cognate (i.e.…”

Section: Introductionmentioning

confidence: 99%

Cognate Ligand Domain Mapping for Enzymes

Bashton¹,

Nobeli²,

Thornton³

2006

Journal of Molecular Biology

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Section: Introductionmentioning

confidence: 99%

Cognate Ligand Domain Mapping for Enzymes

Bashton¹,

Nobeli²,

Thornton³

2006

Journal of Molecular Biology

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“…The rapid growth of the Protein Data Bank provides opportunities to enhance current protocols for molecular docking and scoring, which are at the core of structure-based drug design and hit identification [80]. Recent publications by Estrada et al [81] described another interesting index for QSBR models.…”

Section: Estrada's Folding Indices: I 3 and Tight-bindingmentioning

confidence: 99%

QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues

González-Dı́az¹,

Pérez-Montoto²,

Duardo-Sánchez³

et al. 2009

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The Quantitative Structure-Property Relationship (QSPR) models based on Graph or Network theory are important to represent and predict interesting properties of low-molecular-weight compounds. The graph parameters called Topological Indices (TIs) are useful to link the molecular structure with physicochemical and biological properties. However, there have been recent efforts to extend these methods to the study of proteins and whole proteomes as well. In this case, we are in the presence of Quantitative Protein/Proteome-Property Relationship (QPPR) models, by analogy to QSPR. In the present work we review, discuss, and outline some perspectives on the use of these QPPR techniques applied to single proteins of Parasitic Organisms, Plants and Human Guests. We make emphasis on the different types of graphs and network representations of proteins, the structural information codified by different protein TIs, the statistical or machine learning techniques used and the biological properties predicted. This article also provides a reference to the various legal avenues that are available for the protection of software used in proteins QSAR; as well as the acceptance and legal treatment of scientific results and techniques derived from such software. We also make reference to the recent implementation by Munteanu and González-Díaz of the internet portal called BioAims freely available for the use of the international research community. This portal includes the web-server packages TargetPred with two new Protein-QSAR servers: ATCUNPred (http://miaja.tic.udc.es/Bio-AIMS/ATCUNPred.php) for prediction of ATCUN-mediated DNAclevage anticancer proteins and EnzClassPred for prediction of enzyme classes (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php). Last we included an overview of relevant topics related to legal protection, regulation, and international tax issues involved in practical use of this type of models and software in proteomics.

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“…PDBLIG (Chalk et al, 2004) is a database which associates the binding of ligands from the PDB to domains in the CATH database (Cuff et al, 2009 …”

Section: Domain-ligand Interaction Databasesmentioning

confidence: 99%

Domain–ligand mapping for enzymes

Bashton

Thornton

2009

J of Molecular Recognition

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In this paper we provide an overview of our current knowledge of the mapping between small molecule ligands and protein domains. We give an overview of the present data resources available on the Web, which provide information about protein-ligand interactions, as well as discussing our own PROCOGNATE database. We present an update of ligand binding in large protein superfamilies and identify those ligands most frequently utilized by nature. Finally we discuss potential uses for this type of data.

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PDBLIG: Classification of Small Molecular Protein Binding in the Protein Data Bank

Cited by 40 publications

References 45 publications

Cognate Ligand Domain Mapping for Enzymes

Cognate Ligand Domain Mapping for Enzymes

QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues

Domain–ligand mapping for enzymes

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