PocketMatch: A new algorithm to compare binding sites in protein structures

Yeturu, Kalidas; Chandra, Nagasuma

doi:10.1186/1471-2105-9-543

Cited by 160 publications

(213 citation statements)

References 28 publications

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“…PocketMatch uses a frame-invariant representation of binding sites as 90 lists of sorted distances capturing both the shape and the physicochemical properties of the cavity. 20 As to be expected for this peculiar data set, PocketMatch performs equally to FuzCav in terms of ROC score for classifying adenine-binding pockets from decoys (area under the ROC curve of 0.85, Table 4). PocketMatch is however still inferior to our method when enrichment in true positives among the top-scoring entries is considered ( Figure 5A).…”

Section: Resultsmentioning

confidence: 93%

Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein−Ligand Binding Sites

Weill

Rognan

2010

J. Chem. Inf. Model.

122

197

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Inferring the biological function of a protein from its three-dimensional structure as well as explaining why a drug may bind to various targets is of crucial importance to modern drug discovery. Here we present a generic 4833-integer vector describing druggable protein-ligand binding sites that can be applied to any protein and any binding cavity. The fingerprint registers counts of pharmacophoric triplets from the Calpha atomic coordinates of binding-site-lining residues. Starting from a customized data set of diverse protein-ligand binding site pairs, the most appropriate metric and a similarity threshold could be defined for similar binding sites. The method (FuzCav) has been used in various scenarios: (i) screening a collection of 6000 binding sites for similarity to different queries; (ii) classifying protein families (serine endopeptidases, protein kinases) by binding site diversity; (iii) discriminating adenine-binding cavities from decoys. The fingerprint generation and comparison supports ultra-high throughput (ca. 1000 measures/s), does not require prior alignment of protein binding sites, and is able to detect local similarity among subpockets. It is thus particularly well suited to the functional annotation of novel genomic structures with low sequence identity to known X-ray templates.

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Section: Resultsmentioning

confidence: 93%

Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein−Ligand Binding Sites

Weill

Rognan

2010

J. Chem. Inf. Model.

122

197

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“…In cases where only local and not global similarities can be detected between two unrelated protein cavities, this approach is likely to fail. Interestingly, alignment-free binding site comparison methods [88,89] have been recently reported to be as accurate as alignment-dependant site matching tools and suitable to detect local subpocket similarities. Although not necessary, the binding site reference to which all active sites are compared should be cocrystallized with a drug-like ligand to avoid ligand-induced fit phenomena.…”

Section: Pros and Consmentioning

confidence: 99%

Structure‐Based Approaches to Target Fishing and Ligand Profiling

Rognan¹

2010

Molecular Informatics

143

111

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Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time-consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure-based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

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“…Identification of all relevant binding sites in protein molecules, therefore becomes a key step in the process of gaining functional insights from protein structures. A number of methods have emerged in the last decade for the task of locating binding sites in proteins (Goodford, 1985 Yeturu and Chandra, 2008). They can be broadly classified into (a) geometry-based and (b) energy-based methods.…”

Section: Function Annotation At Binding Site Levelmentioning

confidence: 99%

Structural bioinformatics: Deriving biological insights from protein structures

Chandra

Yeturu

2010

Interdiscip Sci Comput Life Sci

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Structural bioinformatics can be described as an approach that will help decipher biological insights from protein structures. As an important component of structural biology, this area promises to provide a high resolution understanding of biology by assisting comprehension and interpretation of a large amount of structural data. Biological function of protein molecules can be inferred from their three-dimensional structures by comparing structures, classifying them and transferring function from a related protein or family. It is well known now that the structure space of protein molecules is more conserved than the sequence space, making it important to seek functional associations at the structural level. An added advantage of structural bioinformatics over simpler sequence-based methods is that the former also provides ultimate insights into the mechanisms by which various biological events take place. A bird's eye-view of the different aspects of structural bioinformatics is given here along with various recent advances in the area including how knowledge obtained from structural bioinformatics can be applied in drug discovery.

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PocketMatch: A new algorithm to compare binding sites in protein structures

Cited by 160 publications

References 28 publications

Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein−Ligand Binding Sites

Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein−Ligand Binding Sites

Structure‐Based Approaches to Target Fishing and Ligand Profiling

Structural bioinformatics: Deriving biological insights from protein structures

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