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000226801 005__ 20181203024617.0
000226801 0247_ $$2doi$$a10.1038/s41598-017-00266-w
000226801 02470 $$2ISI$$a000396982200012
000226801 037__ $$aARTICLE
000226801 245__ $$aDisentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies
000226801 269__ $$a2017
000226801 260__ $$bNature Publishing Group$$c2017$$aLondon
000226801 300__ $$a9
000226801 336__ $$aJournal Articles
000226801 520__ $$aPredicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.
000226801 6531_ $$aComputational biophysics
000226801 6531_ $$aMolecular modelling
000226801 6531_ $$aProtein structure predictions
000226801 6531_ $$aArtificial Evolution
000226801 700__ $$0247462$$g231878$$aTamo, Giorgio Elikem
000226801 700__ $$0244468$$g195419$$aMaesani, Andrea
000226801 700__ $$0250111$$g194287$$aTräger, Sylvain
000226801 700__ $$0243888$$g147791$$aDegiacomi, Matteo
000226801 700__ $$aFloreano, Dario$$g111729$$0240742
000226801 700__ $$aDal Peraro, Matteo$$g182443$$0243887
000226801 773__ $$j7$$tScientific Reports$$k235
000226801 909C0 $$xU10370$$0252161$$pLIS
000226801 909C0 $$pUPDALPE$$xU11830$$0252070
000226801 909CO $$pSV$$pSTI$$particle$$ooai:infoscience.tind.io:226801
000226801 917Z8 $$x111729
000226801 917Z8 $$x111729
000226801 937__ $$aEPFL-ARTICLE-226801
000226801 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000226801 980__ $$aARTICLE