Sampling properties of the alternating projection distance geometry algorithm applied to unconstrained polypeptide chains

Edwards, Jonathan; Chatham, Greg; Glunt, W.; McDonald, Daniel; Wells, Chris; Hayden, T. L.

doi:10.1016/s0097-8485(96)00018-6

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…It has been shown by Edwards et al [24] and by Steipe and Kaindl [25,26] that RMSD opt is a metric on the space of the classes of equivalent structures (i. e. structures that can be superimposed exactly by a rototranslation). As a consequence both the triangle inequality

(R M S D_{i j}^{o p t} MathClass-rel< MathClass-rel= R M S D_{i k}^{o p t} MathClass-bin+ R M S D_{k j}^{o p t})

and the inverse triangle inequality

(R M S D_{i j}^{o p t} MathClass-rel> MathClass-rel= MathClass-rel| R M S D_{i k}^{o p t} MathClass-bin- R M S D_{k j}^{o p t} MathClass-rel|)

hold.…”

Section: Methodsmentioning

confidence: 99%

“…Let us define

and RMSD ij as the RMSD between structures i and j , after optimal superposition and with no optimal superposition, respectively. It has been shown by Edwards et al [ 24 ] and by Steipe and Kaindl [ 25 , 26 ] that RMSD opt is a metric on the space of the classes of equivalent structures (i. e. structures that can be superimposed exactly by a rototranslation). As a consequence both the triangle inequality

and the inverse triangle inequality

hold.…”

Section: Methodsmentioning

confidence: 99%

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Fast structure similarity searches among protein models: efficient clustering of protein fragments

Fogolari

Corazza

Viglino

et al. 2012

Algorithms Mol Biol

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BackgroundFor many predictive applications a large number of models is generated and later clustered in subsets based on structure similarity. In most clustering algorithms an all-vs-all root mean square deviation (RMSD) comparison is performed. Most of the time is typically spent on comparison of non-similar structures. For sets with more than, say, 10,000 models this procedure is very time-consuming and alternative faster algorithms, restricting comparisons only to most similar structures would be useful.ResultsWe exploit the inverse triangle inequality on the RMSD between two structures given the RMSDs with a third structure. The lower bound on RMSD may be used, when restricting the search of similarity to a reasonably low RMSD threshold value, to speed up similarity searches significantly. Tests are performed on large sets of decoys which are widely used as test cases for predictive methods, with a speed-up of up to 100 times with respect to all-vs-all comparison depending on the set and parameters used. Sample applications are shown.ConclusionsThe algorithm presented here allows fast comparison of large data sets of structures with limited memory requirements. As an example of application we present clustering of more than 100000 fragments of length 5 from the top500H dataset into few hundred representative fragments. A more realistic scenario is provided by the search of similarity within the very large decoy sets used for the tests. Other applications regard filtering nearly-indentical conformation in selected CASP9 datasets and clustering molecular dynamics snapshots.AvailabilityA linux executable and a Perl script with examples are given in the supplementary material (Additional file 1). The source code is available upon request from the authors.

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(R M S D_{i j}^{o p t} MathClass-rel< MathClass-rel= R M S D_{i k}^{o p t} MathClass-bin+ R M S D_{k j}^{o p t})

and the inverse triangle inequality

(R M S D_{i j}^{o p t} MathClass-rel> MathClass-rel= MathClass-rel| R M S D_{i k}^{o p t} MathClass-bin- R M S D_{k j}^{o p t} MathClass-rel|)

hold.…”

Section: Methodsmentioning

confidence: 99%

“…Let us define

and the inverse triangle inequality

hold.…”

Section: Methodsmentioning

confidence: 99%

Fast structure similarity searches among protein models: efficient clustering of protein fragments

Fogolari

Corazza

Viglino

et al. 2012

Algorithms Mol Biol

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show abstract

“…refs −19, have been devised for structure calculation based on experimental distance constraints as obtained by NMR. Again good sampling properties are vital for the performance of these strategies. ,,− Despite the existing discussion in the literature about conformational sampling, pure MD protocols are frequently used in published NMR studies of peptides. The applicability of the specific protocol and the possibility of incomplete sampling of the conformational space are usually not addressed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Protocols for Calculation of Peptide Structures from Experimental NMR Data

Fuhrmans

Milbradt

Renner

2005

J. Chem. Theory Comput.

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In a comparison of structure calculation protocols we clearly demonstrate the need for generating independent starting structures, which is for peptides most efficiently achieved by distance geometry (DG) methods. Our test set consisted of 20 peptides with 7-9 amino acid residues additionally constrained by backbone cyclization and/or the presence of a disulfide bridge. Small peptides usually adopt defined conformational properties only upon introduction of additional constraints, such as cyclization. Therefore, we believe the results of our comparison to be applicable to a large and important class of molecules. The problems associated with the use of restrained molecular dynamics (MD) for conformational searching in the context of structure calculation consist in energy barriers that derive mainly but not exclusively from the experimental NOE constraints. A valid alternative to the DG approach, although for peptides computationally less efficient, is MD simulated annealing starting from random structures as commonly performed in the protein structure calculation from NMR data. As a consequence of our study it must be expected that a considerable fraction of published peptide structures are artificially well-defined or even wrong. Given the relevance of peptide studies for both drug development and protein folding we regard it highly important that structure calculations of peptides are performed with more consideration.

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Improved convergence and speed for the distance geometry program APA to determine protein structure

Glunt

Hayden

2001

Computers & Chemistry

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Sampling properties of the alternating projection distance geometry algorithm applied to unconstrained polypeptide chains

Cited by 4 publications

References 18 publications

Fast structure similarity searches among protein models: efficient clustering of protein fragments

Fast structure similarity searches among protein models: efficient clustering of protein fragments

Comparison of Protocols for Calculation of Peptide Structures from Experimental NMR Data

Improved convergence and speed for the distance geometry program APA to determine protein structure

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