Cognate Ligand Domain Mapping for Enzymes

Bashton, Matthew; Nobeli, Irene; Thornton, Janet M.

doi:10.1016/j.jmb.2006.09.041

Cited by 27 publications

(28 citation statements)

References 42 publications

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“…About 64% of ligandbound, two-domain proteins interact with at least one ligand at their domain interface. Although the biological relevance of most of these ligands has not been verified, we found 340 cognate ligands from 173 two-domain enzymes in the PROCOGNATE database (34). And 115 (66%) of these enzymes have at least one cognate ligand located in the vicinity of domain interfaces.…”

Section: Discussionmentioning

confidence: 90%

“…For example, this can occur when certain compounds are added to assist crystallization by stabilizing the complex structure or to trap them in a desired state, such as 2-methyl-2,4-pentanediol (MPD), dithiothreitol (DTT), and 2-amino-2-hydroxymethyl-propane-1,3-diol (Tris). While most ligands found in interfacial pockets are endogenous, it is often not clear whether their presence is related to an intended in vivo functional role of the protein complex (34).…”

Section: Discussionmentioning

confidence: 99%

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The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation

Gao

Skolnick

2012

Proc. Natl. Acad. Sci. U.S.A.

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Protein-protein and protein-ligand interactions are ubiquitous in a biological cell. Here, we report a comprehensive study of the distribution of protein-ligand interaction sites, namely ligand-binding pockets, around protein-protein interfaces where protein-protein interactions occur. We inspected a representative set of 1,611 representative protein-protein complexes and identified pockets with a potential for binding small molecule ligands. The majority of these pockets are within a 6 Å distance from protein interfaces. Accordingly, in about half of ligand-bound protein-protein complexes, amino acids from both sides of a protein interface are involved in direct contacts with at least one ligand. Statistically, ligands are closer to a protein-protein interface than a random surface patch of the same solvent accessible surface area. Similar results are obtained in an analysis of the ligand distribution around domain-domain interfaces of 1,416 nonredundant, two-domain protein structures. Furthermore, comparable sized pockets as observed in experimental structures are present in artificially generated protein complexes, suggesting that the prominent appearance of pockets around protein interfaces is mainly a structural consequence of protein packing and thus, is an intrinsic geometric feature of protein structure. Nature may take advantage of such a structural feature by selecting and further optimizing for biological function. We propose that packing nearby protein-protein or domain-domain interfaces is a major route to the formation of ligand-binding pockets.packing | promiscuous interaction | protein structural evolution A t one point or another, virtually all biological processes are dependent on protein-protein and/or protein-ligand interactions (1). Since these interactions are ubiquitous for biological activity and are important to drug design, intense research efforts have been dedicated to elucidating their structural basis. This has resulted in the deposition of thousands of protein-protein and protein-ligand complexes in the PDB (2). From a structural prospective, protein surface regions directly contacting other proteins are known as protein-protein interfaces, whereas bindingsites for small molecule ligands are referred to as ligand-binding pockets.Using insights gleaned from high resolution structures, numerous studies have characterized protein-protein interfaces (3-6) and ligand-binding pockets (7,8). Protein interfaces are usually large, with a buried solvent accessible area of over 1;000 Å 2 (3). With the exception of intertwined interface structures, most protein interfaces have planar shapes (4, 9, 10). Although there are in principle millions of ways of forming protein-protein interfaces, a recent study has revealed that the structural space of protein interfaces is surprisingly small, primarily attributed to limited ways of protein secondary structural packing and the flatness of interfaces (10). This affords the possibility of convergent evolution for common biological functions. In contrast...

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation

Gao

Skolnick

2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Ligands for this database have been identified using data from the ENZYME and KEGG databases and are further compared to the PDB ligand using the graph matching approach for the finding the chemical similarity. Further ligands are assigned to the enzymes structures which have EC numbers and also have known reactions in ENZYME and as well in KEGG (Bashton et al 2006(Bashton et al , 2008.…”

Section: Pdb -Protein Data Bankmentioning

confidence: 99%

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

Hariharaputran

2010

Nat Prec

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The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

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“…The identification of cognate ligands is critical for several studies, including defining the range and types of ligands bound by a protein superfamily for the purpose of functional prediction. Table 1 shows the top 40 non-cognate and cognate ligands retrieved from the PDB and the PROCOGNATE ligand-domain mapping (Bashton et al, 2006(Bashton et al, , 2008 ordered according to the number of different domains with which they interact. In Table 1a it can be seen that the most popular non-cognate ligands are sulphate ion and glycerol, both of which are frequently found in the crystallisation buffer and usually bind non-specifically.…”

Section: The Non-cognate Ligand Problemmentioning

confidence: 99%

“…Full details of the procedure for the generation of the PROCOGNATE database are outlined in detail in our previous publication (Bashton et al, 2006). There are two important main steps to the generation of PROCOGNATE:…”

Section: Generation Of the Procognate Databasementioning

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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Cognate Ligand Domain Mapping for Enzymes

Cited by 27 publications

References 42 publications

The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation

The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

Domain–ligand mapping for enzymes

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