Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes

Gutteridge, Alex; Bartlett, Gail J.; Thornton, Janet M.

doi:10.1016/s0022-2836(03)00515-1

Cited by 161 publications

(197 citation statements)

References 51 publications

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“…No obvious differences in other properties were observed. The buried area was computed using NACCESS [23,24]. A dataset of interfaces was thus obtained from the set of quaternary structures.…”

Section: Selecting Structures For Dataset100 Dataset30 and Dataset30_3mentioning

confidence: 99%

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Characterization of Protein–Protein Interfaces

et al. 2007

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We analyze the characteristics of protein-protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and noninterface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and noninterface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.

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Section: Selecting Structures For Dataset100 Dataset30 and Dataset30_3mentioning

confidence: 99%

“…Non-interface surface residues are the non-interface residues whose rASA is at least 25%. The rASA of residues was calculated using the NACCESS program [23,24]. As all the other studies, interface residues are defined based on the known interaction surfaces on PDB complexes.…”

Section: Protein Cores Interfaces and Non-interface Surfacesmentioning

confidence: 99%

Characterization of Protein–Protein Interfaces

et al. 2007

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“…This is typically accomplished by developing structural descriptors of active sites for defi ned protein functional classes and then fi tting these structural templates to novel folds to identify putative active sites and annotate the hypothetical proteins. A variety of approaches are being applied that include aligning structures to match a few consensus or enzymatic catalytic residues, [14][15][16][17][18][19][20][21][22][23] identifi cation of cavities consistent with shapes of known ligands, 24 a sequence independent force fi eld to extract common active site features, 25 theoretical prediction of titration curves, 26 using chemical properties and electrostatic potentials of amino acid residues consistent with active site characteristics, 27,28 neural network analysis of spatial clustering of residues, 29 and conserved residues from multiple sequence alignments (phylogenetic motifs). 20,30 Nevertheless, direct experimental observation of protein-ligand interactions are a more reliable mechanism for the proper and accurate identifi cation of protein active sites.…”

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confidence: 99%

Comparison of protein active site structures for functional annotation of proteins and drug design

et al. 2006

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Rapid and accurate functional assignment of novel proteins is increasing in importance, given the completion of numerous genome sequencing projects and the vastly expanding list of unannotated proteins. Traditionally, global primary‐sequence and structure comparisons have been used to determine putative function. These approaches, however, do not emphasize similarities in active site configurations that are fundamental to a protein's activity and highly conserved relative to the global and more variable structural features. The Comparison of Protein Active Site Structures (CPASS) database and software enable the comparison of experimentally identified ligand‐binding sites to infer biological function and aid in drug discovery. The CPASS database comprises the ligand‐defined active sites identified in the protein data bank, where the CPASS program compares these ligand‐defined active sites to determine sequence and structural similarity without maintaining sequence connectivity. CPASS will compare any set of ligand‐defined protein active sites, irrespective of the identity of the bound ligand. Proteins 2006. © 2006 Wiley‐Liss, Inc.

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“…Approaches based on preexisting structure may be grouped broadly into those using energetics, [12][13][14][15] and others using structural and geometric analysis. [16][17][18][19] Here, we focus on comparative, or evolutionary approaches, [20][21][22][23][24][25][26] and specifically on the evolutionary trace (ET). 27,28 ET maps functional hotspots on protein structures: areas of the protein where amino acids that impact function concentrate.…”

Section: Introductionmentioning

confidence: 99%

Sequence and structure continuity of evolutionary importance improves protein functional site discovery and annotation

et al. 2010

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Protein functional sites control most biological processes and are important targets for drug design and protein engineering. To characterize them, the evolutionary trace (ET) ranks the relative importance of residues according to their evolutionary variations. Generally, top-ranked residues cluster spatially to define evolutionary hotspots that predict functional sites in structures. Here, various functions that measure the physical continuity of ET ranks among neighboring residues in the structure, or in the sequence, are shown to inform sequence selection and to improve functional site resolution. This is shown first, in 110 proteins, for which the overlap between top-ranked residues and actual functional sites rose by 8% in significance. Then, on a structural proteomic scale, optimized ET led to better 3D structure-function motifs (3D templates) and, in turn, to enzyme function prediction by the Evolutionary Trace Annotation (ETA) method with better sensitivity of (40% to 53%) and positive predictive value (93% to 94%). This suggests that the similarity of evolutionary importance among neighboring residues in the sequence and in the structure is a universal feature of protein evolution. In practice, this yields a tool for optimizing sequence selections for comparative analysis and, via ET, for better predictions of functional site and function. This should prove useful for the efficient mutational redesign of protein function and for pharmaceutical targeting.

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Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes

Cited by 161 publications

References 51 publications

Characterization of Protein–Protein Interfaces

Characterization of Protein–Protein Interfaces

Comparison of protein active site structures for functional annotation of proteins and drug design

Sequence and structure continuity of evolutionary importance improves protein functional site discovery and annotation

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