Optimal Pairwise Alignment of Fixed Protein Structures in Subquadratic Time

Poleksić, Aleksandar

doi:10.1142/s0219720011005562

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Possible combinations of corresponding residues are countable while possibilities of special superposition are innumerable. Therefore, the computational complexity of the proposed algorithm is inherently less than most commonly used structure alignment methods [66]. The LAD algorithm was implemented by C# .NET running on an Intel Core i5-2500 3.3GHz computer with 16GB ram.…”

Section: Discussionmentioning

confidence: 99%

A local average distance descriptor for flexible protein structure comparison

et al. 2014

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BackgroundProtein structures are flexible and often show conformational changes upon binding to other molecules to exert biological functions. As protein structures correlate with characteristic functions, structure comparison allows classification and prediction of proteins of undefined functions. However, most comparison methods treat proteins as rigid bodies and cannot retrieve similarities of proteins with large conformational changes effectively.ResultsIn this paper, we propose a novel descriptor, local average distance (LAD), based on either the geodesic distances (GDs) or Euclidean distances (EDs) for pairwise flexible protein structure comparison. The proposed method was compared with 7 structural alignment methods and 7 shape descriptors on two datasets comprising hinge bending motions from the MolMovDB, and the results have shown that our method outperformed all other methods regarding retrieving similar structures in terms of precision-recall curve, retrieval success rate, R-precision, mean average precision and F1-measure.ConclusionsBoth ED- and GD-based LAD descriptors are effective to search deformed structures and overcome the problems of self-connection caused by a large bending motion. We have also demonstrated that the ED-based LAD is more robust than the GD-based descriptor. The proposed algorithm provides an alternative approach for blasting structure database, discovering previously unknown conformational relationships, and reorganizing protein structure classification.

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Section: Discussionmentioning

confidence: 99%

A local average distance descriptor for flexible protein structure comparison

et al. 2014

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“…These data are inherently compositional and a representation as explicit (vectorial) features leads to information loss. As an alternative, tailored dissimilarity measures such as pairwise alignment functions, kernels for structures, or other domain-specific similarity and dissimilarity functions can be used as an interface to the data [41,42]. Also for vectorial data, non-metric proximity measures are quite common in some disciplines.…”

Section: Indefinite Proximity Functionsmentioning

confidence: 99%

Data-Driven Supervised Learning for Life Science Data

Münch

Raab

Biehl

et al. 2020

Front. Appl. Math. Stat.

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Life science data are often encoded in a non-standard way by means of alphanumeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.

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“…Over the last couple of years, there have been several publications that have promoted fast algorithms for database-wide comparisons [1,8,9,10] (see the related work in [11,12,13,14,15,16,17,18,19]). These algorithms are designed to efficiently discern if two proteins share a similar molecular structure.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust

Holder

Simon

Strauser

et al. 2013

Biology

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Several efficient algorithms to conduct pairwise comparisons among large databases of protein structures have emerged in the recent literature. The central theme is the design of a measure between the Cα atoms of two protein chains, from which dynamic programming is used to compute an alignment. The efficiency and efficacy of these algorithms allows large-scale computational studies that would have been previously impractical. The computational study herein shows that the structural alignment algorithm eigen-decomposition alignment with the spectrum (EIGAs) is robust against both parametric and structural variation.

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Optimal Pairwise Alignment of Fixed Protein Structures in Subquadratic Time

Cited by 7 publications

References 43 publications

A local average distance descriptor for flexible protein structure comparison

A local average distance descriptor for flexible protein structure comparison

Data-Driven Supervised Learning for Life Science Data

Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust

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