Combining Bayes Classification and Point Group Symmetry under Boolean Framework for Enhanced Protein Quaternary Structure Inference

Mitra, Pralay; Pal, Debnath

doi:10.1016/j.str.2011.01.009

Cited by 31 publications

(32 citation statements)

References 50 publications

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“…Diverse methods exist which try to predict PPIs based on the computation of free energies or classification models based on physico-chemical and geometrical descriptors, e.g. PQS (48), NOXclass (49), EPIC (50), PISA (51), DiMoVo (52), CRK (53), OringPV (54), IPAC (55) or IChemPIC (56). Most of those methods achieve high accuracies of 85–97%.…”

Section: The Proteinsplus Servermentioning

confidence: 99%

ProteinsPlus: a web portal for structure analysis of macromolecules

Fährrolfes

Bietz

Flachsenberg

et al. 2017

Nucleic Acids Research

182

116

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With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein–protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

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Section: The Proteinsplus Servermentioning

confidence: 99%

ProteinsPlus: a web portal for structure analysis of macromolecules

Fährrolfes

Bietz

Flachsenberg

et al. 2017

Nucleic Acids Research

182

116

View full text Add to dashboard Cite

show abstract

“…The server is very useful, and PISA has become the essential reference method, as it is currently used to predict quaternary structures of every entry in the Protein Data Bank (PDB) [6]. A number of similar methods have been developed based on various linear and nonlinear combinations of geometric and energetic descriptors of the protein-protein interface, in some cases involving machine learning and other statistical tools [7–11]. However, due to its importance we still consider PISA as providing the “golden” standard for quaternary structure prediction.…”

Section: Introductionmentioning

confidence: 99%

ClusPro-DC: Dimer Classification by the Cluspro Server for Protein–Protein Docking

Yueh

Hall

Xia

et al. 2017

Journal of Molecular Biology

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ClusPro-DC (https://cluspro.bu.edu/) implements a straightforward approach to the discrimination between crystallographic and biological dimers by docking the two subunits to exhaustively sample the interaction energy landscape. If a substantial number of low energy docked poses cluster in a narrow vicinity of the native structure of the dimer, then one can assume that there is a well-defined free energy well around the native state, which makes the interaction stable. In contrast, if the interaction sites in the docked poses do not form a large enough cluster around the native structure, then it is unlikely that the subunits form a stable biological dimer. The number of near-native structures is used to estimate the probability of a dimer being biological. Currently the server examines only the stability of a given interface rather than generating all putative quaternary structures as accomplished by PISA or EPPIC, but complements the information provided by these methods.

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“…The first of them relied on interface area analysis [5] and was followed by approaches based on sequence conservation [6-8], combination of geometrical and other properties such as conservation via machine learning [9-11] and thermodynamic estimation of interface stability [12]. This last method, implemented in the PISA server, proved to be the most successful and is the current de facto standard in the field.…”

Section: Introductionmentioning

confidence: 99%

Protein interface classification by evolutionary analysis

et al. 2012

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BackgroundDistinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.ResultsWe present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts.ConclusionsAn evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org.

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Combining Bayes Classification and Point Group Symmetry under Boolean Framework for Enhanced Protein Quaternary Structure Inference

Cited by 31 publications

References 50 publications

ProteinsPlus: a web portal for structure analysis of macromolecules

ProteinsPlus: a web portal for structure analysis of macromolecules

ClusPro-DC: Dimer Classification by the Cluspro Server for Protein–Protein Docking

Protein interface classification by evolutionary analysis

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