Evaluation of structural similarity based on reduced dimensionality representations of protein structure

Albrecht, Birgit; Grant, Guy H.; Richards, William G.

doi:10.1093/protein/gzh049

Cited by 5 publications

(10 citation statements)

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“…In a previous paper [8] we established a new method for calculating protein similarity using reduced dimensionality maps. The two-dimensionality map retains from the original three-dimensional structure, the amino acid distance matrix information (within certain errors as described in the previous section) and quantifiable physico-chemical properties of the amino acids (polarity, hydrophobicity, side chain surface area, etc.)…”

Section: Similaritymentioning

confidence: 99%

“…The two-dimensionality map retains from the original three-dimensional structure, the amino acid distance matrix information (within certain errors as described in the previous section) and quantifiable physico-chemical properties of the amino acids (polarity, hydrophobicity, side chain surface area, etc.) in form of the distance dependent similarity field [8]. However, it has the advantage of a lower number of degrees of freedom.…”

Section: Similaritymentioning

confidence: 99%

“…The concept of distance dependent amino acid similarity field (amino acid sim ) between two amino acids p and q was previously defined [8] as the product of a similarity index dependent on the nature of the amino acids and a Gaussian representation of the distance between them:…”

Section: Similaritymentioning

confidence: 99%

“…We have previously tested a range of different properties for this [8]. In the following we have tried a different approach by using actual three dimensional similarities of the amino acids based on the Hodgkin index calculated by the ASP module in the Tsar computational package [19] as well as other quantifiable properties like structural similarity [20] and hydrophobicity pattern [21].…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…The premise underlying this tool is that proteins with similar or even identical functions also have a high structural similarity in three-dimensions. Our group had previously reported [8] that a critical evaluation of structural similarity can be achieved using reduced dimensionality representations of protein structure. Hence, by calculating a reduced dimensionality map, namely a two-dimensional map, of the protein of unknown function and comparing it to maps of known proteins from different classes, it should be possible to predict function based on the similarity scores.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classification of proteins based on similarity of two-dimensional protein maps

Albrecht

Grant

Sisu

et al. 2008

Biophysical Chemistry

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Data reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.

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Section: Similaritymentioning

confidence: 99%

Section: Similaritymentioning

confidence: 99%

Section: Similaritymentioning

confidence: 99%

Section: Accepted M Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of proteins based on similarity of two-dimensional protein maps

Albrecht

Grant

Sisu

et al. 2008

Biophysical Chemistry

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Evolution of the international workshops on quantitative structure-activity relationships (QSARs) in environmental toxicology

Kaiser¹

2007

SAR and QSAR in Environmental Research

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This presentation will review the evolution of the workshops from a scientific and personal perspective. From their modest beginning in 1983, the workshops have developed into larger international meetings, regularly held every two years. Their initial focus on the aquatic sphere soon expanded to include properties and effects on atmospheric and terrestrial species, including man. Concurrent with this broadening of their scientific scope, the workshops have become an important forum for the early dissemination of all aspects of qualitative and quantitative structure-activity research in ecotoxicology and human health effects. Over the last few decades, the field of quantitative structure/activity relationships (QSARs) has quickly emerged as a major scientific method in understanding the properties and effects of chemicals on the environment and human health. From substances that only affect cell membranes to those that bind strongly to a specific enzyme, QSARs provides insight into the biological effects and chemical and physical properties of substances. QSARs are useful for delineating the quantitative changes in biological effects resulting from minor but systematic variations of the structure of a compound with a specific mode of action. In addition, more holistic approaches are being devised that result in our ability to predict the effects of structurally unrelated compounds with (potentially) different modes of action. Research in QSAR environmental toxicology has led to many improvements in the manufacturing, use, and disposal of chemicals. Furthermore, it has led to national policies and international agreements, from use restrictions or outright bans of compounds, such as polychlorinated biphenyls (PCBs), mirex, and highly chlorinated pesticides (e.g. DDT, dieldrin) for the protection of avian predators, to alternatives for ozone-depleting compounds, to better waste treatment systems, to more powerful and specific acting drugs. Most of the recent advances in drug development could not have been achieved without the use of QSARs in one form or another. The pace of such developments is rapid and QSARs are the keystone to that progress. These workshops have contributed to this progress and will continue to do so in the future.

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Ultrafast shape recognition for similarity search in molecular databases

Ballester

Richards

2007

Proc. R. Soc. A.

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Molecular databases are routinely screened for compounds that most closely resemble a molecule of known biological activity to provide novel drug leads. It is widely believed that three-dimensional molecular shape is the most discriminating pattern for biological activity as it is directly related to the steep repulsive part of the interaction potential between the drug-like molecule and its macromolecular target. However, efficient comparison of molecular shape is currently a challenge. Here, we show that a new approach based on moments of distance distributions is able to recognize molecular shape at least three orders of magnitude faster than current methodologies. Such an ultrafast method permits the identification of similarly shaped compounds within the largest molecular databases. In addition, the problematic requirement of aligning molecules for comparison is circumvented, as the proposed distributions are independent of molecular orientation. Our methodology could be also adapted to tackle similar hard problems in other fields, such as designing content-based Internet search engines for three-dimensional geometrical objects or performing fast similarity comparisons between proteins. From a broader perspective, we anticipate that ultrafast pattern recognition will soon become not only useful, but also essential to address the data explosion currently experienced in most scientific disciplines.

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Evaluation of structural similarity based on reduced dimensionality representations of protein structure

Cited by 5 publications

References 0 publications

Classification of proteins based on similarity of two-dimensional protein maps

Classification of proteins based on similarity of two-dimensional protein maps

Evolution of the international workshops on quantitative structure-activity relationships (QSARs) in environmental toxicology

Ultrafast shape recognition for similarity search in molecular databases

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