Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations
Laura Orellana, Ozge Yoluk, Oliver Carrillo, Modesto Orozco & Erik Lindahl
Nature Communications 7, Article number: 12575 (2016)
Computational biophysics Molecular modelling Protein structure predictions
Received: 26 January 2016 Accepted: 13 July 2016 Published online: 31 August 2016
Figure 8: Correlations between structural variables for GLIC and the PC1-2 partitions reveal two possible temperature-dependent pathways for gating.
Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.
This work was funded by IRB (M.O., O.C., L.O.), the Swedish Research Council (2013-5901) and the Swedish e-Science Research Center (E.L, L.O., O.Y.). The authors acknowledge the use of computational resources by the Swedish National Infrastructure for Computing (2015/16-45). L.O. was supported by a postdoctoral scholarship from SeRC/Vetenskapsrådet. L.O. thanks Dr Johan Gustavsson for helpful discussions and suggestions, thorough reading of the manuscript and help with the preparation of figures and interactive plots. Thanks to Spanish Ministry of Science (MINECO) grant Bio2015 64802-R, Catalan AGAUR, European H2020 Program (Bio Excel CoE) and European Research Council (ERC Advanced Grant SimDNA). M.O. is an ICREA Academia Fellow. We thank Dr Seth Robia and Dr Marc Baaden for generously providing SERCA and GLIC MD trajectories.
Department of Theoretical Physics, KTH Royal Institute of Technology, Box 1031, 171 21 Solna, Sweden
Laura Orellana, Ozge Yoluk & Erik Lindahl
Scuola Normale Superiore de Pisa, Dipartimento di Fisica, Piazza dei Cavalieri, 7, 56126 Pisa, Italy
Joint BSC-IRB Research Program in Computational Biology, Institute for Research in Biomedicine (IRB) Barcelona, C/Baldiri Reixac 10, 08028 Barcelona, Spain
L.O. conceived the original idea, developed the eBDIMS code, performed, analysed and interpreted calculations and wrote the paper. O.Y. performed PCA and eBDIMS calculations, and MD simulations. O.C. wrote the initial eBDIMS code. M.O and E.L. critically read the manuscript and helped with useful suggestions for calculations and data interpretation.
The authors declare no competing financial interests.
Correspondence to Laura Orellana.