FreeContact: fast and free software for protein contact prediction from residue co-evolution

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FreeContact: fast and free software for protein contact prediction from residue co-evolution

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Title: FreeContact: fast and free software for protein contact prediction from residue co-evolution
Author: Kaján, László; Hopf, Thomas A; Kalaš, Matúš; Marks, Debora S; Rost, Burkhard

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Citation: Kaján, László, Thomas A Hopf, Matúš Kalaš, Debora S Marks, and Burkhard Rost. 2014. “FreeContact: fast and free software for protein contact prediction from residue co-evolution.” BMC Bioinformatics 15 (1): 85. doi:10.1186/1471-2105-15-85. http://dx.doi.org/10.1186/1471-2105-15-85.
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Abstract: Background: 20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. Results: Here, we present FreeContact, a fast, open source implementation of EVfold-mfDCA and PSICOV. On a test set of 140 proteins, FreeContact was almost eight times faster than PSICOV without decreasing prediction performance. The EVfold-mfDCA implementation of FreeContact was over 220 times faster than PSICOV with negligible performance decrease. EVfold-mfDCA was unavailable for testing due to its dependency on proprietary software. FreeContact is implemented as the free C++ library “libfreecontact”, complete with command line tool “freecontact”, as well as Perl and Python modules. All components are available as Debian packages. FreeContact supports the BioXSD format for interoperability. Conclusions: FreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).
Published Version: doi:10.1186/1471-2105-15-85
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3987048/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12152953
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