CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites

Leinweber, Matthias; Fober, Thomas; Strickert, Marc; Baumgärtner, Lars; Klebe, G.; Freisleben, Bernd; Hüllermeier, Eyke

doi:10.1109/tkde.2016.2520484

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Such methods rely on molecular interaction fields (MIFs), eg, BioGPS and IsoMIF, on shape and physicochemical properties of the surface, eg, protein functional surfaces, on graphs representing the 3D atomic similarities, eg, IsoCleft or on fingerprints describing the binding sites, eg, PocketMatch . In several data bases, properties of binding sites are stored for comparison, such as pseudocenters with projected physicochemical properties in CavBase, in CavSimBase and in SiteEngine, sequence and structural similarity in CPASS, position of functional groups in SuMo, or surface geometrics and electrostatics in eF‐site …”

Section: Introductionmentioning

confidence: 99%

Electrostatic recognition in substrate binding to serine proteases

Waldner

Kraml

Kahler

et al. 2018

J of Molecular Recognition

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Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

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Section: Introductionmentioning

confidence: 99%

Electrostatic recognition in substrate binding to serine proteases

Waldner

Kraml

Kahler

et al. 2018

J of Molecular Recognition

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“…A similar idea is used in CASSERT [33] and GPU-CASSERT [29], but structural residue descriptors consist of the information on relative position of the C α atom, the secondary structure type, and the residue type. Another feature-based approach is used in methods proposed by Fober and colleagues in [7,8] and Leinweber et al in CavSimBase [17]. These approaches make use of graphs to model molecular structures and represent various features of protein structures.…”

Section: Methods For Protein Structure Alignmentmentioning

confidence: 99%

“…The first group of GPU-based approaches contains solutions proposed by Leinweber et al [15][16][17] for structural comparisons of protein binding sites. These approaches rely on the feature-based representation of protein structure and graph comparisons, mentioned in the previous section.…”

Section: Scalable Solutions For 3d Protein Structure Similarity Searcmentioning

confidence: 99%

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

Mrozek

Suwała

2018

Knowl Inf Syst

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Efficient computational solutions for identification of protein functions or finding structural homologs of proteins gain importance in the era of structural genomics and in the face of growing volumes of biological data. Structural alignments, which underlie these two processes, take a lot of time to complete, especially when performed for large collections of 3D protein structures. Fortunately, structural alignments can be carried out on well-separable and independent subsets of the whole macromolecular data repository, which perfectly fits the MapReduce processing paradigm of bringing computations to data. In this paper, we show how the protein function identification and finding structural homologs can be efficiently accelerated with the use of the MapReduce procedure executed on Hadoop cluster established in a virtualized compute environment or a private cloud. For this purpose, we propose Map-only processing pattern of the MapReduce procedure, which is formally defined in this paper. The solution that we show joins advantages of performing computations in small virtualized compute environments with large-scale computations in public clouds, thus allowing to perform structural alignments for a number of usage scenarios, including comparison of pairs of 3D protein structures during evaluation of predicted protein models, one-to-many comparisons while identifying possible functions of the given structure, or all-to-all alignments while investigating the divergence between known protein structures and classifying proteins by their fold. In this paper, we also present results of performance tests when scaling up nodes of the Hadoop cluster and increasing the degree of parallelism with the intention of improving efficiency of the computations. Keywords Bioinformatics • Big data • Proteins • Scalable computations and MapReduce • 3D protein structures • Cloud computing 148 D. Mrozek et al.

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“…However, the number of available binding site comparison methods is still growing [6] and it is becoming increasingly difficult to choose the most suitable method for a specific area of research. Although binding site similarities can now be retrieved from elaborate similarity databases [7][8][9][10], it is often advisable to perform additional comparisons, or it may even be necessary if proprietary structures are used. The importance of selecting an appropriate program and underlying binding site similarity metric is comprehensively summarized in an article by Kellenberger et al [11].…”

Section: Introductionmentioning

confidence: 99%

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

2018

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The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

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CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites

Cited by 8 publications

References 30 publications

Electrostatic recognition in substrate binding to serine proteases

Electrostatic recognition in substrate binding to serine proteases

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

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