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Fast algorithm for population-based protein structural model analysis.

ABSTRACT: De novo protein structure prediction often generates a large population of candidates (models), and then selects near-native models through clustering. Existing structural model clustering methods are time consuming due to pairwise distance calculation between models. In this paper, we present a novel method for fast model clustering without losing the clustering accuracy. Instead of the commonly used pairwise root mean square deviation and TM-score values, we propose two new distance measures, Dscore1 and Dscore2, based on the comparison of the protein distance matrices for describing the difference and the similarity among models, respectively. The analysis indicates that both the correlation between Dscore1 and root mean square deviation and the correlation between Dscore2 and TM-score are high. Compared to the existing methods with calculation time quadratic to the number of models, our Dscore1-based clustering achieves a linearly time complexity while obtaining almost the same accuracy for near-native model selection. By using Dscore2 to select representatives of clusters, we can further improve the quality of the representatives with little increase in computing time. In addition, for large size (~500 k) models, we can give a fast data visualization based on the Dscore distribution in seconds to minutes. Our method has been implemented in a package named MUFOLD-CL, available at http://mufold.org/clustering.php.

SUBMITTER: Zhang J

PROVIDER: S-EPMC3641909 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Fast algorithm for population-based protein structural model analysis.

Zhang Jingfen J Xu Dong D

Proteomics 20130103 2

De novo protein structure prediction often generates a large population of candidates (models), and then selects near-native models through clustering. Existing structural model clustering methods are time consuming due to pairwise distance calculation between models. In this paper, we present a novel method for fast model clustering without losing the clustering accuracy. Instead of the commonly used pairwise root mean square deviation and TM-score values, we propose two new distance measures, ...[more]

PMID: 23184517

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Project description:A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity). Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments and for incorporation thereof into high-performance PSSM-based alignment profiles.

Dataset Information

Fast algorithm for population-based protein structural model analysis.

Publications

Fast algorithm for population-based protein structural model analysis.

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