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Sparse estimation for structural variability.

Hosur R, Singh R, Berger B - Algorithms Mol Biol (2011)

Bottom Line: Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise.In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data.Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. bab@mit.edu.

ABSTRACT

Background: Proteins are dynamic molecules that exhibit a wide range of motions; often these conformational changes are important for protein function. Determining biologically relevant conformational changes, or true variability, efficiently is challenging due to the noise present in structure data.

Results: In this paper we present a novel approach to elucidate conformational variability in structures solved using X-ray crystallography. We first infer an ensemble to represent the experimental data and then formulate the identification of truly variable members of the ensemble (as opposed to those that vary only due to noise) as a sparse estimation problem. Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise. We validate our predictions for structures in the Protein Data Bank by comparing with NMR experiments, as well as on synthetic data. In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data. In the case of Human Ubiquitin-conjugating enzyme Ubc9, variability identified by the algorithm corresponds to functionally important residues implicated by mutagenesis experiments. Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

No MeSH data available.


Related in: MedlinePlus

Example of ensemble construction and classification. A) PDB structure is shown in green, the second conformer in the synthetic crystal is in gray. The two structures classified by Lasso as variable are shown in blue and the two as variable due to noise, in red. B) Summary of the algorithm output using synthetic data. RMSD is calculated with respect to the PDB structure (green). Suitability of the linear model and statistical significance of the regression coefficients were evaluated using standard techniques (R2 and t-test).
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Figure 2: Example of ensemble construction and classification. A) PDB structure is shown in green, the second conformer in the synthetic crystal is in gray. The two structures classified by Lasso as variable are shown in blue and the two as variable due to noise, in red. B) Summary of the algorithm output using synthetic data. RMSD is calculated with respect to the PDB structure (green). Suitability of the linear model and statistical significance of the regression coefficients were evaluated using standard techniques (R2 and t-test).

Mentions: Our algorithm successfully models variability in a simulated crystal having two conformations, one the PDB structure (conformer 1) and the other constructed computationally (conformer 2) (Figure 2A; green and gray; RMSD = 0.989 Å). The second conformer was constructed using ChainTweak [21]; we randomly selected a conformation from a set of 100. Side chains were built using RAPPER and all atoms were assigned a B-factor of 30 Å2. Synthetic diffraction data were computed by averaging the simulated structure factors of the two conformers using the experimental resolution cutoffs [12,22].


Sparse estimation for structural variability.

Hosur R, Singh R, Berger B - Algorithms Mol Biol (2011)

Example of ensemble construction and classification. A) PDB structure is shown in green, the second conformer in the synthetic crystal is in gray. The two structures classified by Lasso as variable are shown in blue and the two as variable due to noise, in red. B) Summary of the algorithm output using synthetic data. RMSD is calculated with respect to the PDB structure (green). Suitability of the linear model and statistical significance of the regression coefficients were evaluated using standard techniques (R2 and t-test).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3101643&req=5

Figure 2: Example of ensemble construction and classification. A) PDB structure is shown in green, the second conformer in the synthetic crystal is in gray. The two structures classified by Lasso as variable are shown in blue and the two as variable due to noise, in red. B) Summary of the algorithm output using synthetic data. RMSD is calculated with respect to the PDB structure (green). Suitability of the linear model and statistical significance of the regression coefficients were evaluated using standard techniques (R2 and t-test).
Mentions: Our algorithm successfully models variability in a simulated crystal having two conformations, one the PDB structure (conformer 1) and the other constructed computationally (conformer 2) (Figure 2A; green and gray; RMSD = 0.989 Å). The second conformer was constructed using ChainTweak [21]; we randomly selected a conformation from a set of 100. Side chains were built using RAPPER and all atoms were assigned a B-factor of 30 Å2. Synthetic diffraction data were computed by averaging the simulated structure factors of the two conformers using the experimental resolution cutoffs [12,22].

Bottom Line: Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise.In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data.Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. bab@mit.edu.

ABSTRACT

Background: Proteins are dynamic molecules that exhibit a wide range of motions; often these conformational changes are important for protein function. Determining biologically relevant conformational changes, or true variability, efficiently is challenging due to the noise present in structure data.

Results: In this paper we present a novel approach to elucidate conformational variability in structures solved using X-ray crystallography. We first infer an ensemble to represent the experimental data and then formulate the identification of truly variable members of the ensemble (as opposed to those that vary only due to noise) as a sparse estimation problem. Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise. We validate our predictions for structures in the Protein Data Bank by comparing with NMR experiments, as well as on synthetic data. In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data. In the case of Human Ubiquitin-conjugating enzyme Ubc9, variability identified by the algorithm corresponds to functionally important residues implicated by mutagenesis experiments. Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

No MeSH data available.


Related in: MedlinePlus