Critical assessment of methods of protein structure prediction (CASP) - progress and new directions in Round XI.

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TitleCritical assessment of methods of protein structure prediction (CASP) - progress and new directions in Round XI.
Publication TypeJournal Article
Year of Publication2016
AuthorsMoult, J, Fidelis, K, Kryshtafovych, A, Schwede, T, Tramontano, A
Date Published2016 May 12

Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas. This article is protected by copyright. All rights reserved.

Alternate JournalProteins
PubMed ID27171127
Grant ListR01 GM100482 / GM / NIGMS NIH HHS / United States
R13 GM109649 / GM / NIGMS NIH HHS / United States