Journal Article

The ruggedness of protein–protein energy landscape and the cutoff for 1/<i>r</i><sup><i>n</i></sup> potentials

Anatoly M. Ruvinsky and Ilya A. Vakser

in Bioinformatics

Volume 25, issue 9, pages 1132-1136
Published in print May 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI:
The ruggedness of protein–protein energy landscape and the cutoff for 1/rn potentials

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Motivation: Computational studies of the energetics of protein association are important for revealing the underlying fundamental principles and for designing better tools to model protein complexes. The interaction cutoff contribution to the ruggedness of protein–protein energy landscape is studied in terms of relative energy fluctuations for 1/rn potentials based on a simplistic model of a protein complex. This artificial ruggedness exists for short cutoffs and gradually disappears with the cutoff increase.

Results: The critical values of the cutoff were calculated for each of 11 popular power-type potentials with n=0÷9, 12 and for two thresholds of 5% and 10%. The artificial ruggedness decreases to tolerable thresholds for cutoffs larger than the critical ones. The results showed that for both thresholds the critical cutoff is a non-monotonic function of the potential power n. The functions reach the maximum at n=3÷4 and then decrease with the increase of the potential power. The difference between two cutoffs for 5% and 10% artificial ruggedness becomes negligible for potentials decreasing faster than 1/r12. The analytical results obtained for the simple model of protein complexes agree with the analysis of artificial ruggedness in a dataset of 62 protein–protein complexes, with different parameterizations of soft Lennard–Jones potential and two types of protein representations: all-atom and coarse-grained. The results suggest that cutoffs larger than the critical ones can be recommended for protein–protein potentials.


Journal Article.  3310 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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